COVID-19_Reinfection-CN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "76fd0ed7",
   "metadata": {},
   "source": [
    "# 1. Base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd042438",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:55:56.947715Z",
     "start_time": "2024-06-17T12:55:56.340143Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "from matplotlib.pyplot import MultipleLocator\n",
    "from matplotlib.ticker import FormatStrFormatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4500c86e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:55:56.963240Z",
     "start_time": "2024-06-17T12:55:56.949715Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['Times New Roman']\n",
    "plt.rcParams['font.size'] = 35\n",
    "plt.rcParams['font.weight'] = 'bold'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "732f7641",
   "metadata": {},
   "source": [
    "<b> Remember to change the file path ！！！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "242f4027",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:42:12.472935Z",
     "start_time": "2024-06-17T13:42:12.402638Z"
    }
   },
   "outputs": [],
   "source": [
    "#Remember to change the file path ！！！\n",
    "data = pd.read_excel(\"D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\data\\\\DSPs_data_for_RI.xlsx\")\n",
    "data.head(3)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc22a9e9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:42:12.607907Z",
     "start_time": "2024-06-17T13:42:12.586175Z"
    }
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "def convert_to_datetime(date_str):\n",
    "    if isinstance(date_str, datetime):\n",
    "        return date_str  # If it's already a datetime object, return it as is\n",
    "    try:\n",
    "        return datetime.strptime(str(date_str), '%Y-%m-%d')\n",
    "    except ValueError:\n",
    "        return None\n",
    "\n",
    "data['Trecover'] = data['Trecover'].apply(lambda x: convert_to_datetime(x))\n",
    "data['Trecover_register'] = data['Trecover_register'].apply(lambda x: convert_to_datetime(x))\n",
    "data['Tpi'] = data['Tpi'].apply(lambda x: convert_to_datetime(x))\n",
    "data['Tri'] = data['Tri'].apply(lambda x: convert_to_datetime(x))\n",
    "data['endpoint'] = data['endpoint'].apply(lambda x: convert_to_datetime(x))\n",
    "data['Start_time'] = data['Start_time'].apply(lambda x: convert_to_datetime(x))\n",
    "data['Tv1'] = pd.to_datetime(data['Tv1'])\n",
    "data['Tv2'] = pd.to_datetime(data['Tv2'])\n",
    "data['Tv3'] = pd.to_datetime(data['Tv3'])\n",
    "data['Tv4'] = pd.to_datetime(data['Tv4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a3bbcc1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:42:12.758761Z",
     "start_time": "2024-06-17T13:42:12.751199Z"
    }
   },
   "outputs": [],
   "source": [
    "data['TPR'] = (data['Tri'] - data['Tpi']).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9bdfdb2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:42:13.004058Z",
     "start_time": "2024-06-17T13:42:12.993877Z"
    }
   },
   "outputs": [],
   "source": [
    "data['TPR'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed8dbc2a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:42:13.721402Z",
     "start_time": "2024-06-17T13:42:13.714400Z"
    }
   },
   "outputs": [],
   "source": [
    "data['RI_status'] = (data['TPR'] >= 90).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc60f551",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:42:14.501436Z",
     "start_time": "2024-06-17T13:42:14.481294Z"
    }
   },
   "outputs": [],
   "source": [
    "def calculate_tvr(row):\n",
    "    if row['RI_status'] == 1:\n",
    "        if row['Vaccine_status'] == 1:\n",
    "            return row['Tri'] - row['Tv1']\n",
    "        elif row['Vaccine_status'] == 2:\n",
    "            return row['Tri'] - row['Tv2']\n",
    "        elif row['Vaccine_status'] == 3:\n",
    "            return row['Tri'] - row['Tv3']\n",
    "    return np.nan\n",
    "\n",
    "data['Tvr'] = data.apply(calculate_tvr, axis=1)  \n",
    "data['Tvr'] = data['Tvr'].dt.total_seconds() / 86400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dee1c867",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:42:15.145691Z",
     "start_time": "2024-06-17T13:42:15.134831Z"
    }
   },
   "outputs": [],
   "source": [
    "data.loc[data['RI_status'] == 1, 'T_ending'] = data['TPR']\n",
    "data.loc[data['RI_status'] == 0, 'T_ending'] = (data['endpoint'] - data['Tpi']).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9fe322d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:42:17.602568Z",
     "start_time": "2024-06-17T13:42:17.592957Z"
    }
   },
   "outputs": [],
   "source": [
    "age_bins = [0, 14, 64, float('inf')]\n",
    "age_labels = ['<14', '15-64', '65+']\n",
    "\n",
    "data['age_group'] = pd.cut(data['age'], bins=age_bins, labels=age_labels, right=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10959718",
   "metadata": {},
   "source": [
    "## 2.1 Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b19a70aa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:56:08.242953Z",
     "start_time": "2024-06-17T12:56:07.964430Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "symp =  [[1, 135], [72,64],[63, 73], [0,136]]\n",
    "chi2_contingency(symp, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9e68514",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:56:08.489500Z",
     "start_time": "2024-06-17T12:56:08.472310Z"
    }
   },
   "outputs": [],
   "source": [
    "symp =  [[91, 100], [100, 91]]\n",
    "chi2_contingency(symp, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fb785bb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:56:08.949986Z",
     "start_time": "2024-06-17T12:56:08.934237Z"
    }
   },
   "outputs": [],
   "source": [
    "sex =  [[149, 178], [178, 149]]\n",
    "chi2_contingency(sex, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aacbaaa2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:56:09.135012Z",
     "start_time": "2024-06-17T12:56:09.123492Z"
    }
   },
   "outputs": [],
   "source": [
    "sex =  [[91, 100], [100, 91]]\n",
    "chi2_contingency(sex, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34d3661a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:56:09.381830Z",
     "start_time": "2024-06-17T12:56:09.376820Z"
    }
   },
   "outputs": [],
   "source": [
    "sex =  [[58, 78], [78, 58]]\n",
    "chi2_contingency(sex, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47e6c438",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:56:10.619903Z",
     "start_time": "2024-06-17T12:56:10.612290Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "sex =  [[149, 178], [91, 100], [58, 78]]\n",
    "chi2_contingency(sex, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f98ff9b6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:56:11.434137Z",
     "start_time": "2024-06-17T12:56:11.419604Z"
    }
   },
   "outputs": [],
   "source": [
    "data['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0720053",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:56:12.025073Z",
     "start_time": "2024-06-17T12:56:12.012562Z"
    }
   },
   "outputs": [],
   "source": [
    "data[data['Years'] == 2021]['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4a9230c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:56:12.319293Z",
     "start_time": "2024-06-17T12:56:12.310748Z"
    }
   },
   "outputs": [],
   "source": [
    "data[data['Years'] == 2022]['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab84b9bb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:56:12.428146Z",
     "start_time": "2024-06-17T12:56:12.421635Z"
    }
   },
   "outputs": [],
   "source": [
    "x1 = data['age']\n",
    "x2 = data[data['Years'] == 2021]['age']\n",
    "x3 = data[data['Years'] == 2022]['age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27cec7d0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:56:14.697284Z",
     "start_time": "2024-06-17T12:56:14.679243Z"
    }
   },
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "stats.shapiro(x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55cc79be",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:56:14.990182Z",
     "start_time": "2024-06-17T12:56:14.976599Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.shapiro(x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ca3e3a4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:56:15.097963Z",
     "start_time": "2024-06-17T12:56:15.085877Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.shapiro(x3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2016196b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:56:17.460522Z",
     "start_time": "2024-06-17T12:56:17.452011Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.levene(x1, x2, x3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ddbcf1b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:56:18.878356Z",
     "start_time": "2024-06-17T12:56:18.867448Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.kruskal(x1, x2, x3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc4adbe0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:56:59.433161Z",
     "start_time": "2024-06-17T12:56:59.418647Z"
    }
   },
   "outputs": [],
   "source": [
    "age_p =  [[22, 305], [285, 42], [20, 307]]\n",
    "chi2_contingency(age_p, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "415451b8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:00.259606Z",
     "start_time": "2024-06-17T12:57:00.244556Z"
    }
   },
   "outputs": [],
   "source": [
    "age_p =  [[17, 174], [166, 25], [8, 183]]\n",
    "chi2_contingency(age_p, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78550c24",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:01.959715Z",
     "start_time": "2024-06-17T12:57:01.940575Z"
    }
   },
   "outputs": [],
   "source": [
    "age_p =  [[5, 131], [119, 17], [12, 124]]\n",
    "chi2_contingency(age_p, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd947a5a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:02.790609Z",
     "start_time": "2024-06-17T12:57:02.779525Z"
    }
   },
   "outputs": [],
   "source": [
    "age_p =  [[22, 285,20], [17, 166,8], [5, 119,12]]\n",
    "chi2_contingency(age_p, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ac82df6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:03.464491Z",
     "start_time": "2024-06-17T12:57:03.444489Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p =  [[81,246], [31, 296], [12, 315],[19, 308],[175,152],[9, 318]]\n",
    "chi2_contingency(reg_p, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27431d0b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:04.076720Z",
     "start_time": "2024-06-17T12:57:04.068214Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p =  [[9,182], [6, 185], [2, 189],[6, 185],[168,23],[0, 191]]\n",
    "chi2_contingency(reg_p, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99ce1b2d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:05.737829Z",
     "start_time": "2024-06-17T12:57:05.724114Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p =  [[72,64], [25, 111], [10, 126],[13, 123],[7,129],[9, 127]]\n",
    "chi2_contingency(reg_p, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f37383ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:06.289571Z",
     "start_time": "2024-06-17T12:57:06.277012Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p =  [[81,31,12,19,175,9], [9,6,2,6,168,0], [72,25,10,13,7,9]]\n",
    "chi2_contingency(reg_p, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e35bf9e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:09.553584Z",
     "start_time": "2024-06-17T12:57:09.547580Z"
    }
   },
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "\n",
    "reg_p = [[81,31,12,19,175,9], [9,6,2,6,168,0], [72,25,10,13,7,9]]\n",
    "\n",
    "comparisons = [(i, j) for i in range(len(reg_p)) for j in range(i+1, len(reg_p))]\n",
    "\n",
    "adjusted_p_values = []\n",
    "for comparison in comparisons:\n",
    "    chi2, p, _, _ = stats.chi2_contingency([reg_p[comparison[0]], reg_p[comparison[1]]])\n",
    "    adjusted_p = p * len(comparisons)  # Bonferroni校正\n",
    "    adjusted_p_values.append((f'Group{comparison[0]+1} vs Group{comparison[1]+1}', adjusted_p))\n",
    "\n",
    "for comparison, p in adjusted_p_values:\n",
    "    print(f'{comparison}: {p}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bbbfec6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:11.863502Z",
     "start_time": "2024-06-17T12:57:11.844283Z"
    }
   },
   "outputs": [],
   "source": [
    "data.groupby(data['workpalce'])['workpalce'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d8f3e70",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:15.413074Z",
     "start_time": "2024-06-17T12:57:15.397358Z"
    }
   },
   "outputs": [],
   "source": [
    "data.groupby(['Years','workpalce'])['Years'].count().unstack('workpalce') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a40638e1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:16.972833Z",
     "start_time": "2024-06-17T12:57:16.963974Z"
    }
   },
   "outputs": [],
   "source": [
    "wp =  [[11,316], [55, 272], [160, 167],[2, 325],[10,317],[57, 270],[32, 295]]\n",
    "chi2_contingency(wp, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f806235",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:17.552694Z",
     "start_time": "2024-06-17T12:57:17.537621Z"
    }
   },
   "outputs": [],
   "source": [
    "wp =  [[5,186], [13, 178], [125, 66],[0, 191],[6,185],[20, 171],[22, 169]]\n",
    "chi2_contingency(wp, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c599f41",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:18.084242Z",
     "start_time": "2024-06-17T12:57:18.075732Z"
    }
   },
   "outputs": [],
   "source": [
    "wp =  [[6,130], [42, 94], [35, 101],[2, 134],[4,132],[37, 99],[10, 126]]\n",
    "chi2_contingency(wp, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31d59d95",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:20.112908Z",
     "start_time": "2024-06-17T12:57:20.099815Z"
    }
   },
   "outputs": [],
   "source": [
    "wp =  [[11,55,160,2,10,57,32], [5,13,125,0,6,20,22], [6,42,35,2,4,37,10]]\n",
    "chi2_contingency(wp, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d92d1f0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:20.965094Z",
     "start_time": "2024-06-17T12:57:20.947582Z"
    }
   },
   "outputs": [],
   "source": [
    "symp =  [[1,220,98,8], [0,148,35,8], [1,72,63,0]]\n",
    "chi2_contingency(symp, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4484782a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:21.549945Z",
     "start_time": "2024-06-17T12:57:21.537338Z"
    }
   },
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "symp =  [[1,220,98,8], [0,148,35,8], [1,72,63,0]]\n",
    "\n",
    "comparisons = [(i, j) for i in range(len(symp)) for j in range(i+1, len(symp))]\n",
    "\n",
    "adjusted_p_values = []\n",
    "for comparison in comparisons:\n",
    "    chi2, p, _, _ = stats.chi2_contingency([symp[comparison[0]], symp[comparison[1]]])\n",
    "    adjusted_p = p * len(comparisons)  # Bonferroni校正\n",
    "    adjusted_p_values.append((f'Group{comparison[0]+1} vs Group{comparison[1]+1}', adjusted_p))\n",
    "\n",
    "for comparison, p in adjusted_p_values:\n",
    "    print(f'{comparison}: {p}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15d480ed",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:24.559934Z",
     "start_time": "2024-06-17T12:57:24.547415Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p = [[11,55,160,2,10,57,32], [5,13,125,0,6,20,22], [6,42,35,2,4,37,10]]\n",
    "\n",
    "comparisons = [(i, j) for i in range(len(reg_p)) for j in range(i+1, len(reg_p))]\n",
    "\n",
    "adjusted_p_values = []\n",
    "for comparison in comparisons:\n",
    "    chi2, p, _, _ = stats.chi2_contingency([reg_p[comparison[0]], reg_p[comparison[1]]])\n",
    "    adjusted_p = p * len(comparisons)  # Bonferroni校正\n",
    "    adjusted_p_values.append((f'Group{comparison[0]+1} vs Group{comparison[1]+1}', adjusted_p))\n",
    "\n",
    "for comparison, p in adjusted_p_values:\n",
    "    print(f'{comparison}: {p}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddb020ae",
   "metadata": {},
   "source": [
    "## 2.2 all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3b540eb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:30.142065Z",
     "start_time": "2024-06-17T12:57:30.124955Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "data.groupby(['RI_status','Years'])['Years'].count().unstack('Years') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87d1dcee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:32.268515Z",
     "start_time": "2024-06-17T12:57:32.252447Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "a =  [[327,48], [191,43], [136,5]]\n",
    "chi2_contingency(a, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d5567d6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:33.119452Z",
     "start_time": "2024-06-17T12:57:33.108316Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p =  [[327,48], [191,43], [136,5]]\n",
    "\n",
    "comparisons = [(i, j) for i in range(len(reg_p)) for j in range(i+1, len(reg_p))]\n",
    "\n",
    "adjusted_p_values = []\n",
    "for comparison in comparisons:\n",
    "    chi2, p, _, _ = stats.chi2_contingency([reg_p[comparison[0]], reg_p[comparison[1]]])\n",
    "    adjusted_p = p * len(comparisons)  # Bonferroni校正\n",
    "    adjusted_p_values.append((f'Group{comparison[0]+1} vs Group{comparison[1]+1}', adjusted_p))\n",
    "\n",
    "for comparison, p in adjusted_p_values:\n",
    "    print(f'{comparison}: {p}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "795adfb0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:34.747452Z",
     "start_time": "2024-06-17T12:57:34.731718Z"
    }
   },
   "outputs": [],
   "source": [
    "a =  [[191,43], [136,5]]\n",
    "chi2_contingency(a, correction=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f01aa681",
   "metadata": {},
   "source": [
    "## 2.3 sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b909ab1b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:37.260527Z",
     "start_time": "2024-06-17T12:57:37.244459Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "data.groupby(['RI_status','sex'])['RI_status'].count().unstack('sex') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a490276c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:37.827942Z",
     "start_time": "2024-06-17T12:57:37.809416Z"
    }
   },
   "outputs": [],
   "source": [
    "data.groupby(['RI_status','Years','sex'])['Years'].count().unstack('sex') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8cae436",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:38.365145Z",
     "start_time": "2024-06-17T12:57:38.360145Z"
    }
   },
   "outputs": [],
   "source": [
    "sex =  [[149,21], [91,19], [58,2]]\n",
    "chi2_contingency(sex, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d31cb78",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:39.434275Z",
     "start_time": "2024-06-17T12:57:39.419901Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p =  [[149,21], [91,19], [58,2]]\n",
    "\n",
    "comparisons = [(i, j) for i in range(len(reg_p)) for j in range(i+1, len(reg_p))]\n",
    "\n",
    "adjusted_p_values = []\n",
    "for comparison in comparisons:\n",
    "    chi2, p, _, _ = stats.chi2_contingency([reg_p[comparison[0]], reg_p[comparison[1]]])\n",
    "    adjusted_p = p * len(comparisons)  # Bonferroni校正\n",
    "    adjusted_p_values.append((f'Group{comparison[0]+1} vs Group{comparison[1]+1}', adjusted_p))\n",
    "\n",
    "for comparison, p in adjusted_p_values:\n",
    "    print(f'{comparison}: {p}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e4b8555",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:41.448899Z",
     "start_time": "2024-06-17T12:57:41.436740Z"
    }
   },
   "outputs": [],
   "source": [
    "sex =  [[91,19], [58,2]]\n",
    "chi2_contingency(sex, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "148a5bfa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:42.006651Z",
     "start_time": "2024-06-17T12:57:41.999651Z"
    }
   },
   "outputs": [],
   "source": [
    "sex =  [[178,27], [100,24], [78,3]]\n",
    "chi2_contingency(sex, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b287beb0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:42.756882Z",
     "start_time": "2024-06-17T12:57:42.739848Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p =   [[178,27], [100,24], [78,3]]\n",
    "\n",
    "comparisons = [(i, j) for i in range(len(reg_p)) for j in range(i+1, len(reg_p))]\n",
    "\n",
    "adjusted_p_values = []\n",
    "for comparison in comparisons:\n",
    "    chi2, p, _, _ = stats.chi2_contingency([reg_p[comparison[0]], reg_p[comparison[1]]])\n",
    "    adjusted_p = p * len(comparisons)  # Bonferroni校正\n",
    "    adjusted_p_values.append((f'Group{comparison[0]+1} vs Group{comparison[1]+1}', adjusted_p))\n",
    "\n",
    "for comparison, p in adjusted_p_values:\n",
    "    print(f'{comparison}: {p}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72683da5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:44.527580Z",
     "start_time": "2024-06-17T12:57:44.516427Z"
    }
   },
   "outputs": [],
   "source": [
    "sex =  [[100,24], [78,3]]\n",
    "chi2_contingency(sex, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73252d00",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:45.015570Z",
     "start_time": "2024-06-17T12:57:45.006446Z"
    }
   },
   "outputs": [],
   "source": [
    "sex =  [[149,21], [178,27]]\n",
    "chi2_contingency(sex, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f9ba5da",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:45.720670Z",
     "start_time": "2024-06-17T12:57:45.702573Z"
    }
   },
   "outputs": [],
   "source": [
    "sex =  [[91,19], [100,24]]\n",
    "chi2_contingency(sex, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14433fba",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:46.211616Z",
     "start_time": "2024-06-17T12:57:46.197079Z"
    }
   },
   "outputs": [],
   "source": [
    "sex =  [[58,2], [78,3]]\n",
    "chi2_contingency(sex, correction=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "204716ae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T13:50:29.916284Z",
     "start_time": "2024-02-15T13:50:29.909678Z"
    }
   },
   "source": [
    "## 2.4 age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "991acd0a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:49.874928Z",
     "start_time": "2024-06-17T12:57:49.857462Z"
    }
   },
   "outputs": [],
   "source": [
    "x1 = data['age']\n",
    "x2 = data[data['Years'] == 2021]['age']\n",
    "x3 = data[data['Years'] == 2022]['age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f97bbf72",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:50.058659Z",
     "start_time": "2024-06-17T12:57:50.046808Z"
    }
   },
   "outputs": [],
   "source": [
    "x3.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2f3f2b2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:50.224718Z",
     "start_time": "2024-06-17T12:57:50.210633Z"
    }
   },
   "outputs": [],
   "source": [
    "x2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0b5f789",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:50.468047Z",
     "start_time": "2024-06-17T12:57:50.452012Z"
    }
   },
   "outputs": [],
   "source": [
    "x1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9a60f2c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:51.126669Z",
     "start_time": "2024-06-17T12:57:51.119717Z"
    }
   },
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "stats.shapiro(x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff3f6a51",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:53.090071Z",
     "start_time": "2024-06-17T12:57:53.084541Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.shapiro(x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5ae4c36",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:53.257762Z",
     "start_time": "2024-06-17T12:57:53.250593Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.shapiro(x3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "420adafb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:53.579197Z",
     "start_time": "2024-06-17T12:57:53.569686Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.levene(x1, x2, x3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28709356",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:53.825538Z",
     "start_time": "2024-06-17T12:57:53.819529Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.levene(x2, x3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "892eebe9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:54.010503Z",
     "start_time": "2024-06-17T12:57:53.998954Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.kruskal(x1, x2, x3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e662dab8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:54.733167Z",
     "start_time": "2024-06-17T12:57:54.715096Z"
    }
   },
   "outputs": [],
   "source": [
    "from scipy.stats import mannwhitneyu\n",
    "mannwhitneyu(x2, x3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed9e4256",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:55.316988Z",
     "start_time": "2024-06-17T12:57:55.261656Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from statsmodels.stats.multitest import multipletests\n",
    "\n",
    "stats.kruskal(x1, x2, x3)\n",
    "ts_data = np.concatenate((x1, x2, x3))\n",
    "\n",
    "# Create labels for the groups\n",
    "labels = ['Group 1'] * len(x1) + ['Group 2'] * len(x2) + ['Group 3'] * len(x3)\n",
    "\n",
    "# Perform Kruskal-Wallis test\n",
    "_, p_value = stats.kruskal(x1, x2,x3)\n",
    "\n",
    "# Perform pairwise Mann-Whitney U test and adjust p-values using the Holm method\n",
    "if p_value < 0.05:\n",
    "    comparisons = []\n",
    "    p_values = []\n",
    "    for i in range(len(np.unique(labels))):\n",
    "        for j in range(i+1, len(np.unique(labels))):\n",
    "            group1_data = ts_data[np.array(labels) == np.unique(labels)[i]]\n",
    "            group2_data = ts_data[np.array(labels) == np.unique(labels)[j]]\n",
    "            _, p_value = stats.mannwhitneyu(group1_data, group2_data, alternative='two-sided')\n",
    "            comparisons.append((np.unique(labels)[i], np.unique(labels)[j]))\n",
    "            p_values.append(p_value)\n",
    "\n",
    "    # Apply Holm correction\n",
    "    reject, pvals_corrected, _, _ = multipletests(p_values, method='holm')\n",
    "\n",
    "    # Create a DataFrame to store the results\n",
    "    results = pd.DataFrame({'Groups': comparisons, 'P-value': p_values, 'P-value (Holm-adjusted)': pvals_corrected, 'Reject H0': reject})\n",
    "\n",
    "    # Print the pairwise comparisons\n",
    "    print(\"Pairwise Comparisons:\")\n",
    "    print(results)\n",
    "else:\n",
    "    print(\"No significant differences found.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38508d42",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:57.689558Z",
     "start_time": "2024-06-17T12:57:57.676411Z"
    }
   },
   "outputs": [],
   "source": [
    "x1 = data[data['RI_status'] == 1]['age']\n",
    "x2 = data[(data['RI_status'] == 1) & (data['Years'] == 2021)]['age']\n",
    "x3 = data[(data['RI_status'] == 1) & (data['Years'] == 2022)]['age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d15aa5d2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:58.283948Z",
     "start_time": "2024-06-17T12:57:58.267427Z"
    }
   },
   "outputs": [],
   "source": [
    "x1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a03bf7a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:58.803925Z",
     "start_time": "2024-06-17T12:57:58.789413Z"
    }
   },
   "outputs": [],
   "source": [
    "x2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0865bd85",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:59.034938Z",
     "start_time": "2024-06-17T12:57:59.017421Z"
    }
   },
   "outputs": [],
   "source": [
    "x3.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e19cc923",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:57:59.204525Z",
     "start_time": "2024-06-17T12:57:59.186444Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.shapiro(x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1a7a585",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:00.475472Z",
     "start_time": "2024-06-17T12:58:00.468469Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.shapiro(x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c569be9a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:00.704078Z",
     "start_time": "2024-06-17T12:58:00.694554Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.shapiro(x3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77ec6cee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:00.857674Z",
     "start_time": "2024-06-17T12:58:00.854108Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.levene(x1, x2, x3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1191abe3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:01.696502Z",
     "start_time": "2024-06-17T12:58:01.675425Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.kruskal(x1, x2, x3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7250277b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:07.954356Z",
     "start_time": "2024-06-17T12:58:07.947458Z"
    }
   },
   "outputs": [],
   "source": [
    "total =  [[48, 327], [43, 191], [5, 136]]\n",
    "chi2_contingency(total, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03b73162",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:08.551606Z",
     "start_time": "2024-06-17T12:58:08.541528Z"
    }
   },
   "outputs": [],
   "source": [
    "sex =[[21, 27], [19, 24], [2, 3]]\n",
    "chi2_contingency(sex, correction=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b9e50b6",
   "metadata": {},
   "source": [
    "## 2.5 age groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b3f3002",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:15.008216Z",
     "start_time": "2024-06-17T12:58:15.001673Z"
    }
   },
   "outputs": [],
   "source": [
    "age_p_1=[[19,1], [14, 1], [5, 0]]\n",
    "chi2_contingency(age_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a43dbdd1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:15.574591Z",
     "start_time": "2024-06-17T12:58:15.559016Z"
    }
   },
   "outputs": [],
   "source": [
    "age_p_1=[ [14, 1], [5, 0]]\n",
    "chi2_contingency(age_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "408c7ecb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:15.804005Z",
     "start_time": "2024-06-17T12:58:15.795478Z"
    }
   },
   "outputs": [],
   "source": [
    "age_p_1=[[284,44], [167, 40], [117, 4]]\n",
    "chi2_contingency(age_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e59db3d7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:15.988640Z",
     "start_time": "2024-06-17T12:58:15.972533Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p =  [[284,44], [167, 40], [117, 4]]\n",
    "\n",
    "comparisons = [(i, j) for i in range(len(reg_p)) for j in range(i+1, len(reg_p))]\n",
    "\n",
    "adjusted_p_values = []\n",
    "for comparison in comparisons:\n",
    "    chi2, p, _, _ = stats.chi2_contingency([reg_p[comparison[0]], reg_p[comparison[1]]])\n",
    "    adjusted_p = p * len(comparisons)  # Bonferroni校正\n",
    "    adjusted_p_values.append((f'Group{comparison[0]+1} vs Group{comparison[1]+1}', adjusted_p))\n",
    "\n",
    "for comparison, p in adjusted_p_values:\n",
    "    print(f'{comparison}: {p}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2147594a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:17.989865Z",
     "start_time": "2024-06-17T12:58:17.980271Z"
    }
   },
   "outputs": [],
   "source": [
    "age_p_1=[[167, 40], [117, 4]]\n",
    "chi2_contingency(age_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23d98c5a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:18.158865Z",
     "start_time": "2024-06-17T12:58:18.142037Z"
    }
   },
   "outputs": [],
   "source": [
    "age_p_1=[[24,3], [10, 2], [14, 1]]\n",
    "chi2_contingency(age_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a01e3ec0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:18.328686Z",
     "start_time": "2024-06-17T12:58:18.310561Z"
    }
   },
   "outputs": [],
   "source": [
    "age_p_1=[[10, 2], [14, 1]]\n",
    "chi2_contingency(age_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fed8a662",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:18.483013Z",
     "start_time": "2024-06-17T12:58:18.477002Z"
    }
   },
   "outputs": [],
   "source": [
    "age_p_1=[[19,1], [284, 44], [24, 3]]\n",
    "chi2_contingency(age_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81f27556",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:20.218653Z",
     "start_time": "2024-06-17T12:58:20.204527Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "age_p_1=[[14,1], [167, 40], [10, 2]]\n",
    "chi2_contingency(age_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b16aa31",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:20.387023Z",
     "start_time": "2024-06-17T12:58:20.369913Z"
    }
   },
   "outputs": [],
   "source": [
    "age_p_1=[[5,0], [117, 4], [14, 1]]\n",
    "chi2_contingency(age_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63bef657",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:20.571091Z",
     "start_time": "2024-06-17T12:58:20.554533Z"
    }
   },
   "outputs": [],
   "source": [
    "age_p_1=[[1,47], [1, 42], [0, 5]]\n",
    "chi2_contingency(age_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7efa639c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:20.693251Z",
     "start_time": "2024-06-17T12:58:20.676705Z"
    }
   },
   "outputs": [],
   "source": [
    "age_p_2=[[44,4], [40, 3], [4, 1]]\n",
    "chi2_contingency(age_p_2, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eff5d7de",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:22.898670Z",
     "start_time": "2024-06-17T12:58:22.884610Z"
    }
   },
   "outputs": [],
   "source": [
    "age_p_3=[[3,45], [2, 41], [1, 4]]\n",
    "chi2_contingency(age_p_3, correction=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d25c68f3",
   "metadata": {},
   "source": [
    "## 2.6 region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72d4d82b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:26.753905Z",
     "start_time": "2024-06-17T12:58:26.740693Z"
    }
   },
   "outputs": [],
   "source": [
    "data.groupby(['Region'])['Region'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "360f90ed",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:29.078426Z",
     "start_time": "2024-06-17T12:58:29.067869Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data.groupby(['RI_status','Years','Region'])['Years'].count().unstack('Region') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1dbcf1d1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:30.818203Z",
     "start_time": "2024-06-17T12:58:30.812696Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p_1=[[81,6], [9, 4], [72, 2]]\n",
    "chi2_contingency(reg_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57bbb715",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:31.000734Z",
     "start_time": "2024-06-17T12:58:30.982673Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p = [[81,6], [9, 4], [72, 2]]\n",
    "\n",
    "comparisons = [(i, j) for i in range(len(reg_p)) for j in range(i+1, len(reg_p))]\n",
    "\n",
    "adjusted_p_values = []\n",
    "for comparison in comparisons:\n",
    "    chi2, p, _, _ = stats.chi2_contingency([reg_p[comparison[0]], reg_p[comparison[1]]])\n",
    "    adjusted_p = p * len(comparisons)  # Bonferroni校正\n",
    "    adjusted_p_values.append((f'Group{comparison[0]+1} vs Group{comparison[1]+1}', adjusted_p))\n",
    "\n",
    "for comparison, p in adjusted_p_values:\n",
    "    print(f'{comparison}: {p}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70250606",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:31.168322Z",
     "start_time": "2024-06-17T12:58:31.156230Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p_1=[[9, 4], [72, 2]]\n",
    "chi2_contingency(reg_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb1fdbc1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:31.338587Z",
     "start_time": "2024-06-17T12:58:31.329062Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p_1=[[31,3], [6, 2], [25, 1]]\n",
    "chi2_contingency(reg_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ced4298",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:33.139761Z",
     "start_time": "2024-06-17T12:58:33.133224Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p_1=[[12,4], [2, 2], [10, 2]]\n",
    "chi2_contingency(reg_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "268bc313",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:33.368776Z",
     "start_time": "2024-06-17T12:58:33.362699Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p_1=[[2, 2], [10, 2]]\n",
    "chi2_contingency(reg_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b86226d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:33.535738Z",
     "start_time": "2024-06-17T12:58:33.524434Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "reg_p_1=[[19,5], [6, 5], [13, 0]]\n",
    "chi2_contingency(reg_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb41ebeb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:33.641616Z",
     "start_time": "2024-06-17T12:58:33.633087Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p = [[19,5], [6, 5], [13, 0]]\n",
    "\n",
    "comparisons = [(i, j) for i in range(len(reg_p)) for j in range(i+1, len(reg_p))]\n",
    "\n",
    "adjusted_p_values = []\n",
    "for comparison in comparisons:\n",
    "    chi2, p, _, _ = stats.chi2_contingency([reg_p[comparison[0]], reg_p[comparison[1]]])\n",
    "    adjusted_p = p * len(comparisons)  # Bonferroni校正\n",
    "    adjusted_p_values.append((f'Group{comparison[0]+1} vs Group{comparison[1]+1}', adjusted_p))\n",
    "\n",
    "for comparison, p in adjusted_p_values:\n",
    "    print(f'{comparison}: {p}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ba6c2b5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:36.027441Z",
     "start_time": "2024-06-17T12:58:36.012653Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p_1=[[6, 5], [13, 0]]\n",
    "chi2_contingency(reg_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49897cbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:36.254761Z",
     "start_time": "2024-06-17T12:58:36.239854Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p_1=[[175,30], [168, 30], [7, 0]]\n",
    "chi2_contingency(reg_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40c30809",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:36.421676Z",
     "start_time": "2024-06-17T12:58:36.407139Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p_1=[[168, 30], [7, 0]]\n",
    "chi2_contingency(reg_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72bda283",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:36.589466Z",
     "start_time": "2024-06-17T12:58:36.581647Z"
    }
   },
   "outputs": [],
   "source": [
    "from scipy.stats import fisher_exact\n",
    "reg_p_1=[[0, 0], [9, 0]]\n",
    "fisher_exact(reg_p_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2efae33e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:37.337800Z",
     "start_time": "2024-06-17T12:58:37.332291Z"
    }
   },
   "outputs": [],
   "source": [
    "from scipy.stats import fisher_exact\n",
    "reg_p_1=[[5, 0], [19, 1],[14, 1]]\n",
    "chi2_contingency(reg_p_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c7b32c5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:37.643689Z",
     "start_time": "2024-06-17T12:58:37.628411Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p_1=[[81, 6], [31, 3],[12, 4], [19, 5],[175, 30], [9, 0]]\n",
    "chi2_contingency(reg_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a472112",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:38.818402Z",
     "start_time": "2024-06-17T12:58:38.812775Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p_1=[[9,4], [6, 2],[2, 2], [6, 5],[168, 30]]\n",
    "chi2_contingency(reg_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ae79374",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:40.477870Z",
     "start_time": "2024-06-17T12:58:40.460793Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "reg_p_1=[[72, 2], [25, 1],[10, 2], [13, 0],[7, 0], [9, 0]]\n",
    "chi2_contingency(reg_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95c5100a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:40.813144Z",
     "start_time": "2024-06-17T12:58:40.801634Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p_1=[[81, 6], [31, 3],[12, 4], [19, 5],[175, 30], [9, 0]]\n",
    "chi2_contingency(reg_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb774b67",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:43.694675Z",
     "start_time": "2024-06-17T12:58:43.689675Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p_1=[[6, 2], [25, 1]]\n",
    "chi2_contingency(reg_p_1, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0eed126b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:43.923948Z",
     "start_time": "2024-06-17T12:58:43.911742Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p_2=[[3,45], [2, 41], [1, 4]]\n",
    "chi2_contingency(reg_p_2, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fe9e0ed",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:44.076478Z",
     "start_time": "2024-06-17T12:58:44.071614Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p_3=[[4,44], [2, 41], [2, 3]]\n",
    "chi2_contingency(reg_p_3, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb4a93fb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:45.978832Z",
     "start_time": "2024-06-17T12:58:45.971807Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p_4=[[5,43], [5, 38], [0, 5]]\n",
    "chi2_contingency(reg_p_4, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66e2de86",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:46.147632Z",
     "start_time": "2024-06-17T12:58:46.133775Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p_5=[[30,18], [30, 13], [0, 5]]\n",
    "chi2_contingency(reg_p_5, correction=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34418bac",
   "metadata": {},
   "source": [
    "## 2.7 vac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d73a297",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:47.577338Z",
     "start_time": "2024-06-17T12:58:47.569752Z"
    }
   },
   "outputs": [],
   "source": [
    "data.groupby(['Vaccine_status'])['Vaccine_status'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce8262a7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:48.232495Z",
     "start_time": "2024-06-17T12:58:48.220758Z"
    }
   },
   "outputs": [],
   "source": [
    "data.groupby(['RI_status','Years','Vaccine_status'])['RI_status'].count().unstack('Vaccine_status') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f580f35c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:50.595262Z",
     "start_time": "2024-06-17T12:58:50.579878Z"
    }
   },
   "outputs": [],
   "source": [
    "vac =[[11,1], [11, 1]]\n",
    "chi2_contingency(vac, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25c7b514",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:50.763770Z",
     "start_time": "2024-06-17T12:58:50.744705Z"
    }
   },
   "outputs": [],
   "source": [
    "vac =[[17,3], [12, 3],[5, 0]]\n",
    "chi2_contingency(vac, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03f85d5e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:50.916570Z",
     "start_time": "2024-06-17T12:58:50.907563Z"
    }
   },
   "outputs": [],
   "source": [
    "vac =[  [12, 3],[5, 0]]\n",
    "chi2_contingency(vac, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecbda440",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:51.084302Z",
     "start_time": "2024-06-17T12:58:51.078961Z"
    }
   },
   "outputs": [],
   "source": [
    "vac =[[99,23], [69, 22],[30, 1]]\n",
    "chi2_contingency(vac, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fdf9de7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:53.405167Z",
     "start_time": "2024-06-17T12:58:53.387454Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p = [[99,23], [69, 22],[30, 1]]\n",
    "\n",
    "comparisons = [(i, j) for i in range(len(reg_p)) for j in range(i+1, len(reg_p))]\n",
    "\n",
    "adjusted_p_values = []\n",
    "for comparison in comparisons:\n",
    "    chi2, p, _, _ = stats.chi2_contingency([reg_p[comparison[0]], reg_p[comparison[1]]])\n",
    "    adjusted_p = p * len(comparisons)  # Bonferroni校正\n",
    "    adjusted_p_values.append((f'Group{comparison[0]+1} vs Group{comparison[1]+1}', adjusted_p))\n",
    "\n",
    "for comparison, p in adjusted_p_values:\n",
    "    print(f'{comparison}: {p}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7bdede3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:53.633452Z",
     "start_time": "2024-06-17T12:58:53.624214Z"
    }
   },
   "outputs": [],
   "source": [
    "vac =[ [69, 22],[30, 1]]\n",
    "chi2_contingency(vac, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6810120d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:54.107767Z",
     "start_time": "2024-06-17T12:58:54.103259Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "vac =[[157,20], [98, 17],[59, 3]]\n",
    "chi2_contingency(vac, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91f97c60",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:54.338564Z",
     "start_time": "2024-06-17T12:58:54.329411Z"
    }
   },
   "outputs": [],
   "source": [
    "vac =[ [98, 17],[59, 3]]\n",
    "chi2_contingency(vac, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05705402",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:56.115584Z",
     "start_time": "2024-06-17T12:58:56.100022Z"
    }
   },
   "outputs": [],
   "source": [
    "vac =[[11,1], [42, 1],[17, 3],[99,23], [157, 20],[1, 0]]\n",
    "chi2_contingency(vac, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33ee72ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:56.328017Z",
     "start_time": "2024-06-17T12:58:56.317506Z"
    }
   },
   "outputs": [],
   "source": [
    "vac =[[11,1], [12, 3],[69, 22],[98,17],  [1, 0]]\n",
    "chi2_contingency(vac, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6208c685",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:56.480637Z",
     "start_time": "2024-06-17T12:58:56.474830Z"
    }
   },
   "outputs": [],
   "source": [
    "vac =[[42,1], [5, 0],[30, 1],[59,3]]\n",
    "chi2_contingency(vac, correction=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e03e0c16",
   "metadata": {},
   "source": [
    "## 2.8 WORKPALCES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae9370f2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:59.269319Z",
     "start_time": "2024-06-17T12:58:59.259768Z"
    }
   },
   "outputs": [],
   "source": [
    "data.groupby(['Years','workpalce'])['workpalce'].count().unstack('workpalce') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4bb0701",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:59.422568Z",
     "start_time": "2024-06-17T12:58:59.414989Z"
    }
   },
   "outputs": [],
   "source": [
    "data.groupby(['RI_status','Years','workpalce'])['RI_status'].count().unstack('workpalce') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55f78ef6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:59.592252Z",
     "start_time": "2024-06-17T12:58:59.579879Z"
    }
   },
   "outputs": [],
   "source": [
    "wp =[[11,4], [5, 4],[6, 0]]\n",
    "chi2_contingency(wp, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9580a1c6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:59.774681Z",
     "start_time": "2024-06-17T12:58:59.762595Z"
    }
   },
   "outputs": [],
   "source": [
    "wp =[ [5, 4],[6, 0]]\n",
    "chi2_contingency(wp, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d60fb48",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:58:59.882328Z",
     "start_time": "2024-06-17T12:58:59.873724Z"
    }
   },
   "outputs": [],
   "source": [
    "wp =[[55,5], [13, 2],[42, 3]]\n",
    "chi2_contingency(wp, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f14790d7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:59:00.079432Z",
     "start_time": "2024-06-17T12:59:00.063607Z"
    }
   },
   "outputs": [],
   "source": [
    "wp =[  [13, 2],[42, 3]]\n",
    "chi2_contingency(wp, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "589085cb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:59:00.233456Z",
     "start_time": "2024-06-17T12:59:00.224927Z"
    }
   },
   "outputs": [],
   "source": [
    "wp =[[160,25], [125, 25],[35, 0]]\n",
    "chi2_contingency(wp, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b926a88d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:59:00.399830Z",
     "start_time": "2024-06-17T12:59:00.381968Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p =[[160,25], [125, 25],[35, 0]]\n",
    "\n",
    "comparisons = [(i, j) for i in range(len(reg_p)) for j in range(i+1, len(reg_p))]\n",
    "\n",
    "adjusted_p_values = []\n",
    "for comparison in comparisons:\n",
    "    chi2, p, _, _ = stats.chi2_contingency([reg_p[comparison[0]], reg_p[comparison[1]]])\n",
    "    adjusted_p = p * len(comparisons)  # Bonferroni校正\n",
    "    adjusted_p_values.append((f'Group{comparison[0]+1} vs Group{comparison[1]+1}', adjusted_p))\n",
    "\n",
    "for comparison, p in adjusted_p_values:\n",
    "    print(f'{comparison}: {p}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "864cf387",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:59:03.989553Z",
     "start_time": "2024-06-17T12:59:03.972143Z"
    }
   },
   "outputs": [],
   "source": [
    "wp =[ [125, 25],[35, 0]]\n",
    "chi2_contingency(wp, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2505628a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:59:04.218693Z",
     "start_time": "2024-06-17T12:59:04.211737Z"
    }
   },
   "outputs": [],
   "source": [
    "from scipy.stats import fisher_exact\n",
    "wp =[[2,0], [2, 0]]\n",
    "fisher_exact(wp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c756f0ae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:59:04.526429Z",
     "start_time": "2024-06-17T12:59:04.508574Z"
    }
   },
   "outputs": [],
   "source": [
    "wp =[[10,5], [6,5],[4, 0]]\n",
    "chi2_contingency(wp, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97ae857d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:59:04.678957Z",
     "start_time": "2024-06-17T12:59:04.668447Z"
    }
   },
   "outputs": [],
   "source": [
    "wp =[[6,5],[4, 0]]\n",
    "chi2_contingency(wp, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2871c29d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:59:04.863188Z",
     "start_time": "2024-06-17T12:59:04.853447Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "wp =[[57,6], [20,4],[37, 2]]\n",
    "chi2_contingency(wp, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e500e9f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:59:06.572790Z",
     "start_time": "2024-06-17T12:59:06.564281Z"
    }
   },
   "outputs": [],
   "source": [
    "wp =[ [20,4],[37, 2]]\n",
    "chi2_contingency(wp, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "deaf0f45",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:59:06.738484Z",
     "start_time": "2024-06-17T12:59:06.722615Z"
    }
   },
   "outputs": [],
   "source": [
    "wp =[ [32,3],[22, 3],[10, 0]]\n",
    "chi2_contingency(wp, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f75a931d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:59:07.058656Z",
     "start_time": "2024-06-17T12:59:07.048145Z"
    }
   },
   "outputs": [],
   "source": [
    "wp =[  [22, 3],[10, 0]]\n",
    "chi2_contingency(wp, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fd2c4c3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:59:07.212804Z",
     "start_time": "2024-06-17T12:59:07.206408Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "wp =[ [11,4],[55, 5],[160, 25],[2, 0],[10, 5],[57, 6],[32, 3]]\n",
    "chi2_contingency(wp, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "285a1f8d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:59:08.602768Z",
     "start_time": "2024-06-17T12:59:08.596764Z"
    }
   },
   "outputs": [],
   "source": [
    "wp =[ [5,4],[13, 2],[125, 25],[6, 5],[20,4],[22, 3] ]\n",
    "chi2_contingency(wp, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39773807",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:59:08.784909Z",
     "start_time": "2024-06-17T12:59:08.772216Z"
    }
   },
   "outputs": [],
   "source": [
    "wp =[ [6,0],[42, 3],[35, 0],[2, 0],[4, 0],[37, 2],[10,0]]\n",
    "chi2_contingency(wp, correction=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e5bcfe9",
   "metadata": {},
   "source": [
    "## 2.9 symp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ce5f532",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:59:10.926477Z",
     "start_time": "2024-06-17T12:59:10.906325Z"
    }
   },
   "outputs": [],
   "source": [
    "data.groupby(['Years','symp_pi'])['Years'].count().unstack('symp_pi') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45eae1dc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:59:11.077466Z",
     "start_time": "2024-06-17T12:59:11.066343Z"
    }
   },
   "outputs": [],
   "source": [
    "data.groupby(['RI_status','Years','symp_pi'])['RI_status'].count().unstack('symp_pi') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "723cfb01",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:59:23.449307Z",
     "start_time": "2024-06-17T12:59:23.435256Z"
    }
   },
   "outputs": [],
   "source": [
    "sy =[ [326,1],[23, 25] ]\n",
    "chi2_contingency(sy, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3aa6363f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:59:23.649868Z",
     "start_time": "2024-06-17T12:59:23.641359Z"
    }
   },
   "outputs": [],
   "source": [
    "sy =[ [191,0],[19, 24] ]\n",
    "chi2_contingency(sy, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f260c9b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:59:23.834798Z",
     "start_time": "2024-06-17T12:59:23.818032Z"
    }
   },
   "outputs": [],
   "source": [
    "sy =[ [135,1],[4 ,1] ]\n",
    "chi2_contingency(sy, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c672627e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:59:23.985037Z",
     "start_time": "2024-06-17T12:59:23.978384Z"
    }
   },
   "outputs": [],
   "source": [
    "sy =[ [23,25],[19,24] ,[4,1]]\n",
    "chi2_contingency(sy, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "944a826d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:59:25.598784Z",
     "start_time": "2024-06-17T12:59:25.580671Z"
    }
   },
   "outputs": [],
   "source": [
    "sy =[  [19,24] ,[4,1]]\n",
    "chi2_contingency(sy, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d383efe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:59:25.752706Z",
     "start_time": "2024-06-17T12:59:25.740577Z"
    }
   },
   "outputs": [],
   "source": [
    "sy =[ [326,1],[191,0] ,[135,1]]\n",
    "chi2_contingency(sy, correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa0e9b3b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T12:59:25.905376Z",
     "start_time": "2024-06-17T12:59:25.901849Z"
    }
   },
   "outputs": [],
   "source": [
    "sy =[  [191,0] ,[135,1]]\n",
    "chi2_contingency(sy, correction=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b57dd21",
   "metadata": {},
   "source": [
    "## 2.10 hospital"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afc676f6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:00:09.868677Z",
     "start_time": "2024-06-17T13:00:09.861136Z"
    }
   },
   "outputs": [],
   "source": [
    "data.groupby([ 'Years','Hospitalization'])['Hospitalization'].count().unstack('Hospitalization') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97138fe0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:00:39.551434Z",
     "start_time": "2024-06-17T13:00:39.533692Z"
    }
   },
   "outputs": [],
   "source": [
    "data.groupby(['RI_status','Years','Hospitalization'])['RI_status'].count().unstack('Hospitalization') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60fadcc7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:00:56.843599Z",
     "start_time": "2024-06-17T13:00:56.826979Z"
    }
   },
   "outputs": [],
   "source": [
    "hos =[  [112,215] ,[2,46]]\n",
    "chi2_contingency(hos , correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8c9e95b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:00:57.056396Z",
     "start_time": "2024-06-17T13:00:57.051781Z"
    }
   },
   "outputs": [],
   "source": [
    "hos =[  [108,83] ,[2,41]]\n",
    "chi2_contingency(hos , correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcbaa4d7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:00:57.224449Z",
     "start_time": "2024-06-17T13:00:57.207910Z"
    }
   },
   "outputs": [],
   "source": [
    "hos =[  [4,132] ,[0,5]]\n",
    "chi2_contingency(hos , correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ea02357",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:00:57.315449Z",
     "start_time": "2024-06-17T13:00:57.307437Z"
    }
   },
   "outputs": [],
   "source": [
    "hos =[ [112,215],[108,83],[4,132]]\n",
    "chi2_contingency(hos , correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e2576b8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:00:57.498620Z",
     "start_time": "2024-06-17T13:00:57.490026Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p =[ [112,215],[108,83],[4,132]]\n",
    "\n",
    "comparisons = [(i, j) for i in range(len(reg_p)) for j in range(i+1, len(reg_p))]\n",
    "\n",
    "adjusted_p_values = []\n",
    "for comparison in comparisons:\n",
    "    chi2, p, _, _ = stats.chi2_contingency([reg_p[comparison[0]], reg_p[comparison[1]]])\n",
    "    adjusted_p = p * len(comparisons)  # Bonferroni校正\n",
    "    adjusted_p_values.append((f'Group{comparison[0]+1} vs Group{comparison[1]+1}', adjusted_p))\n",
    "\n",
    "for comparison, p in adjusted_p_values:\n",
    "    print(f'{comparison}: {p}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "266ca25a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:00:59.424136Z",
     "start_time": "2024-06-17T13:00:59.413559Z"
    }
   },
   "outputs": [],
   "source": [
    "hos =[  [108,83],[4,132]]\n",
    "chi2_contingency(hos , correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "586763fd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:00:59.608834Z",
     "start_time": "2024-06-17T13:00:59.596271Z"
    }
   },
   "outputs": [],
   "source": [
    "hos =[ [2,46],[2,41],[0,5]]\n",
    "chi2_contingency(hos , correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d68f0a1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:00:59.730437Z",
     "start_time": "2024-06-17T13:00:59.716891Z"
    }
   },
   "outputs": [],
   "source": [
    "hos =[  [2,41],[0,5]]\n",
    "chi2_contingency(hos , correction=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40863037",
   "metadata": {},
   "source": [
    "## 2.11 pesis inf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a992cfd6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:01:02.349740Z",
     "start_time": "2024-06-17T13:01:02.340229Z"
    }
   },
   "outputs": [],
   "source": [
    "data.groupby([ 'Years','Persistent infection'])['Persistent infection'].count().unstack('Persistent infection') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "055354ae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:01:02.501677Z",
     "start_time": "2024-06-17T13:01:02.493045Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data.groupby(['RI_status','Years','Persistent infection'])['RI_status'].count().unstack('Persistent infection') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e69d67c4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:01:02.730198Z",
     "start_time": "2024-06-17T13:01:02.724544Z"
    }
   },
   "outputs": [],
   "source": [
    "p =[  [112,215],[21,27]]\n",
    "chi2_contingency(p , correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2e99fe1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:01:02.837270Z",
     "start_time": "2024-06-17T13:01:02.830665Z"
    }
   },
   "outputs": [],
   "source": [
    "p =[  [111,80],[21,22]]\n",
    "chi2_contingency(p , correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1597ff9e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:01:03.082900Z",
     "start_time": "2024-06-17T13:01:03.076335Z"
    }
   },
   "outputs": [],
   "source": [
    "p =[  [1,135],[0,5]]\n",
    "chi2_contingency(p , correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4b9cdf8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:01:04.579339Z",
     "start_time": "2024-06-17T13:01:04.564262Z"
    }
   },
   "outputs": [],
   "source": [
    "p =[  [112,215],[111,80],[1,135]]\n",
    "chi2_contingency(p , correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50874049",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:01:04.733952Z",
     "start_time": "2024-06-17T13:01:04.719443Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_p =[  [112,215],[111,80],[1,135]]\n",
    "\n",
    "comparisons = [(i, j) for i in range(len(reg_p)) for j in range(i+1, len(reg_p))]\n",
    "\n",
    "adjusted_p_values = []\n",
    "for comparison in comparisons:\n",
    "    chi2, p, _, _ = stats.chi2_contingency([reg_p[comparison[0]], reg_p[comparison[1]]])\n",
    "    adjusted_p = p * len(comparisons)  # Bonferroni校正\n",
    "    adjusted_p_values.append((f'Group{comparison[0]+1} vs Group{comparison[1]+1}', adjusted_p))\n",
    "\n",
    "for comparison, p in adjusted_p_values:\n",
    "    print(f'{comparison}: {p}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c21e0cf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:01:04.888017Z",
     "start_time": "2024-06-17T13:01:04.876925Z"
    }
   },
   "outputs": [],
   "source": [
    "p =[   [111,80],[1,135]]\n",
    "chi2_contingency(p , correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf028b29",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:01:06.530121Z",
     "start_time": "2024-06-17T13:01:06.516533Z"
    }
   },
   "outputs": [],
   "source": [
    "p =[  [21,27],[21,22],[0,5]]\n",
    "chi2_contingency(p , correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6eee8b7c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:01:06.728784Z",
     "start_time": "2024-06-17T13:01:06.716237Z"
    }
   },
   "outputs": [],
   "source": [
    "p =[  [21,22],[0,5]]\n",
    "chi2_contingency(p , correction=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "056e0291",
   "metadata": {},
   "source": [
    "# 3.Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ed192ca",
   "metadata": {},
   "source": [
    "## 3.1 curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e45d6be6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:03:46.016197Z",
     "start_time": "2024-06-17T13:03:28.500834Z"
    }
   },
   "outputs": [],
   "source": [
    "phsm = pd.read_excel(\"D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\data\\\\PHSM.xlsx\",index_col = 0)\n",
    "phsm.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12ab6ba7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:03:49.446119Z",
     "start_time": "2024-06-17T13:03:49.420630Z"
    }
   },
   "outputs": [],
   "source": [
    "phsm_select = phsm.loc['2021-09-01':'2023-02-28']\n",
    "phsm_weekly = phsm_select.resample('W').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a6ab381",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:03:50.054690Z",
     "start_time": "2024-06-17T13:03:50.044180Z"
    }
   },
   "outputs": [],
   "source": [
    "phsm_weekly.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d1bbdc3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:03:50.841952Z",
     "start_time": "2024-06-17T13:03:50.827951Z"
    }
   },
   "outputs": [],
   "source": [
    "Tspi = data.groupby(['Tpi'])['Tpi'].count()\n",
    "Tsri = data[data['RI_status'] == 1].groupby('Tri')['Tri'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30104ec8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:03:51.841180Z",
     "start_time": "2024-06-17T13:03:51.828181Z"
    }
   },
   "outputs": [],
   "source": [
    "pi_weekly = Tspi.resample('W').sum()\n",
    "ri_weekly = Tsri.resample('W').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32e259a5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:03:54.752519Z",
     "start_time": "2024-06-17T13:03:54.739921Z"
    }
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "x1 = 20210901\n",
    "x2 = 20220225\n",
    "x3 = 20230228\n",
    "x4 = 20221207\n",
    "\n",
    "\n",
    "x1 = datetime.strptime(str(x1),'%Y%m%d')\n",
    "x2 = datetime.strptime(str(x2),'%Y%m%d')\n",
    "x3 = datetime.strptime(str(x3),'%Y%m%d')\n",
    "x4 = datetime.strptime(str(x4),'%Y%m%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee47095e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:04:00.001801Z",
     "start_time": "2024-06-17T13:03:59.788557Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['Times New Roman']\n",
    "plt.rcParams.update({'font.weight': 'bold'})\n",
    "plt.rcParams['font.size'] = 25\n",
    "fig, ax1 = plt.subplots(figsize=(10,4)) \n",
    "\n",
    "plt.axvspan(x1, x2, facecolor='#FEFEE6', label='Detla dominant period')\n",
    "plt.axvspan(x2, x3, facecolor='#F7C2A0', alpha=0.5, label='Omicron dominant period')\n",
    "\n",
    "# 堆叠的柱形图\n",
    "ax1.bar(pi_weekly.index, pi_weekly, color='#0A72D5', width=6, label='PI cases') \n",
    "ax1.bar(ri_weekly.index, ri_weekly, color='#C12592', width=6, alpha=0.8,  label='RI cases')     \n",
    "\n",
    "ax2 = ax1.twinx() \n",
    "ax2.plot(phsm_weekly['StringencyIndex_Average'], color='#FEBB30', label='COVID-19 Stringency Index', linewidth=3)\n",
    "\n",
    "ax2.vlines([x4], 0, 100, linestyles='dashed', colors='red', alpha=0.3, linewidth = 3,label='10 new measures  released')\n",
    "\n",
    "plt.xlim(x1, x3) \n",
    "\n",
    "ax1.set_xlabel('Time', fontsize=25, fontweight='bold')  \n",
    "ax1.set_ylabel('Number of cases', fontsize=25, fontweight='bold')  \n",
    "ax2.set_ylabel('Index values', fontsize=25, fontweight='bold')\n",
    "\n",
    "ax1.set_ylim(0, 150)  \n",
    "ax2.set_ylim(0, 80)  \n",
    "\n",
    "ax1.xaxis.set_major_locator(mdates.MonthLocator(interval=3)) \n",
    "\n",
    "lines, labels = ax1.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax2.legend(lines + lines2, labels + labels2, loc='center', bbox_to_anchor=(0.5, -0.5), ncol=2, \n",
    "           prop={'size': 25, 'family': 'Times New Roman'})\n",
    "\n",
    "plt.savefig('D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\Figs\\\\curve.pdf',dpi=300,  bbox_inches='tight') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b34e332a",
   "metadata": {},
   "source": [
    "## 3.2 Key_interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7731bf55",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:18:18.131225Z",
     "start_time": "2024-06-17T13:18:18.102587Z"
    }
   },
   "outputs": [],
   "source": [
    "tpr = data[data['RI_status'] == 1]['TPR']\n",
    "tpr_v0 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 0)]['TPR']\n",
    "tpr_v1 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 1)]['TPR']\n",
    "tpr_v2 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 2)]['TPR']\n",
    "tpr_v3 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 3)]['TPR']\n",
    "tpr_v4 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 4)]['TPR']\n",
    "\n",
    "\n",
    "tpr_c1 = data[(data['RI_status'] == 1) & (data['Years'] == 2021)]['TPR']\n",
    "tpr_v0_c1 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 0) & (data['Years'] == 2021)]['TPR']\n",
    "tpr_v1_c1 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 1) & (data['Years'] == 2021)]['TPR']\n",
    "tpr_v2_c1 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 2) & (data['Years'] == 2021)]['TPR']\n",
    "tpr_v3_c1 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 3) & (data['Years'] == 2021)]['TPR']\n",
    "tpr_v4_c1 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 4) & (data['Years'] == 2021)]['TPR']\n",
    "\n",
    "\n",
    "tpr_c2 = data[(data['RI_status'] == 1) & (data['Years'] == 2022)]['TPR']\n",
    "tpr_v0_c2 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 0) & (data['Years'] == 2022)]['TPR']\n",
    "tpr_v1_c2 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 1) & (data['Years'] == 2022)]['TPR']\n",
    "tpr_v2_c2 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 2) & (data['Years'] == 2022)]['TPR']\n",
    "tpr_v3_c2 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 3) & (data['Years'] == 2022)]['TPR']\n",
    "tpr_v4_c2 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 4) & (data['Years'] == 2022)]['TPR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e511498c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:18:19.306246Z",
     "start_time": "2024-06-17T13:18:19.259818Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from scipy.stats import shapiro\n",
    "\n",
    "# 定义条件列表\n",
    "conditions = [\n",
    "    ('tpr', 'RI_status == 1'),\n",
    "    ('tpr_v0', '(RI_status == 1) & (Vaccine_status == 0)'),\n",
    "    ('tpr_v1', '(RI_status == 1) & (Vaccine_status == 1)'),\n",
    "    ('tpr_v2', '(RI_status == 1) & (Vaccine_status == 2)'),\n",
    "    ('tpr_v3', '(RI_status == 1) & (Vaccine_status == 3)'),\n",
    "    ('tpr_v4', '(RI_status == 1) & (Vaccine_status == 4)'),\n",
    "    ('tpr_c1', '(RI_status == 1) & (Years == 2021)'),\n",
    "    ('tpr_v0_c1', '(RI_status == 1) & (Vaccine_status == 0) & (Years == 2021)'),\n",
    "    ('tpr_v1_c1', '(RI_status == 1) & (Vaccine_status == 1) & (Years == 2021)'),\n",
    "    ('tpr_v2_c1', '(RI_status == 1) & (Vaccine_status == 2) & (Years == 2021)'),\n",
    "    ('tpr_v3_c1', '(RI_status == 1) & (Vaccine_status == 3) & (Years == 2021)'),\n",
    "    ('tpr_v4_c1', '(RI_status == 1) & (Vaccine_status == 4) & (Years == 2021)'),\n",
    "    ('tpr_c2', '(RI_status == 1) & (Years == 2022)'),\n",
    "    ('tpr_v0_c2', '(RI_status == 1) & (Vaccine_status == 0) & (Years == 2022)'),\n",
    "    ('tpr_v1_c2', '(RI_status == 1) & (Vaccine_status == 1) & (Years == 2022)'),\n",
    "    ('tpr_v2_c2', '(RI_status == 1) & (Vaccine_status == 2) & (Years == 2022)'),\n",
    "    ('tpr_v3_c2', '(RI_status == 1) & (Vaccine_status == 3) & (Years == 2022)'),\n",
    "    ('tpr_v4_c2', '(RI_status == 1) & (Vaccine_status == 4) & (Years == 2022)')\n",
    "]\n",
    "\n",
    "# 存储结果的字典\n",
    "results = {}\n",
    "\n",
    "for condition_name, condition_query in conditions:\n",
    "    try:\n",
    "        # 获取数据\n",
    "        subset_data = data.query(condition_query)['TPR']\n",
    "        \n",
    "        # 描述统计\n",
    "        describe_stats = subset_data.describe()\n",
    "        \n",
    "        # 正态性检验\n",
    "        shapiro_stat, shapiro_p = shapiro(subset_data)\n",
    "        \n",
    "        # 存储结果\n",
    "        results[condition_name] = {\n",
    "            'describe_stats': describe_stats,\n",
    "            'shapiro_stat': shapiro_stat,\n",
    "            'shapiro_p': shapiro_p\n",
    "        }\n",
    "    except ValueError as e:\n",
    "        print(f\"An error occurred for condition {condition_name}: {e}\")\n",
    "\n",
    "# 打印结果\n",
    "for condition_name, result in results.items():\n",
    "    print(f\"Condition: {condition_name}\")\n",
    "    print(\"Describe Statistics:\")\n",
    "    print(result['describe_stats'])\n",
    "    print(\"Shapiro-Wilk Normality Test:\")\n",
    "    print(f\"Statistic: {result['shapiro_stat']}, p-value: {result['shapiro_p']}\")\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "397e737b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:18:20.058134Z",
     "start_time": "2024-06-17T13:18:20.051584Z"
    }
   },
   "outputs": [],
   "source": [
    "tpr_c1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3df82aa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:18:20.793279Z",
     "start_time": "2024-06-17T13:18:20.778709Z"
    }
   },
   "outputs": [],
   "source": [
    "tpr_na = data[(data['RI_status'] == 1) & (data['Vaccine_status'].isnull())]['TPR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8f359aa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:18:20.961750Z",
     "start_time": "2024-06-17T13:18:20.947514Z"
    }
   },
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "stats.levene(tpr, tpr_v0 , tpr_v1,tpr_v2 ,tpr_v3, tpr_na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6708f31a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:18:21.145962Z",
     "start_time": "2024-06-17T13:18:21.138407Z"
    }
   },
   "outputs": [],
   "source": [
    "from scipy.stats import kruskal\n",
    "kruskal(tpr, tpr_v0 , tpr_v1,tpr_v2 ,tpr_v3, tpr_na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4291e886",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:18:21.890436Z",
     "start_time": "2024-06-17T13:18:21.875858Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.levene(tpr_c1,   tpr_v1_c1,tpr_v2_c1,tpr_v3_c1, tpr_na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e143c67d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:18:23.214298Z",
     "start_time": "2024-06-17T13:18:23.203147Z"
    }
   },
   "outputs": [],
   "source": [
    "kruskal(tpr_c1,   tpr_v1_c1,tpr_v2_c1,tpr_v3_c1, tpr_na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c57a126",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:18:23.368165Z",
     "start_time": "2024-06-17T13:18:23.361125Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.levene(tpr_c2,   tpr_v0_c2,tpr_v2_c2,tpr_v3_c2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f7a9933",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:18:23.505031Z",
     "start_time": "2024-06-17T13:18:23.489960Z"
    }
   },
   "outputs": [],
   "source": [
    "kruskal(tpr_c2,   tpr_v0_c2,tpr_v2_c2,tpr_v3_c2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e8a05cd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:18:23.659061Z",
     "start_time": "2024-06-17T13:18:23.652921Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.levene(tpr,   tpr_c2, tpr_c1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5f9d5cd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:10:21.153021Z",
     "start_time": "2024-06-17T13:10:21.144519Z"
    }
   },
   "outputs": [],
   "source": [
    "kruskal(tpr,   tpr_c2, tpr_c1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7445b2e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:10:44.530961Z",
     "start_time": "2024-06-17T13:10:44.520801Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.levene(tpr_v1,   tpr_v1_c1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65d10803",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:10:45.115457Z",
     "start_time": "2024-06-17T13:10:45.106902Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.levene(tpr_v2,  tpr_v2_c2,  tpr_v2_c1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3abad432",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:10:45.941220Z",
     "start_time": "2024-06-17T13:10:45.930710Z"
    }
   },
   "outputs": [],
   "source": [
    "kruskal(tpr_v2,  tpr_v2_c2,  tpr_v2_c1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fdea644",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:10:46.524497Z",
     "start_time": "2024-06-17T13:10:46.506481Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.levene(tpr_v3,  tpr_v3_c2,  tpr_v3_c1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7df03180",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:10:47.330208Z",
     "start_time": "2024-06-17T13:10:47.323698Z"
    }
   },
   "outputs": [],
   "source": [
    "kruskal(tpr_v3,  tpr_v3_c2,  tpr_v3_c1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f421b781",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:10:54.286769Z",
     "start_time": "2024-06-17T13:10:54.219762Z"
    }
   },
   "outputs": [],
   "source": [
    "import scikit_posthocs as sp\n",
    "df = {\n",
    "    'group': np.concatenate([np.repeat('tpr_v3', len(tpr_v3)), np.repeat('tpr_v3_c2', len(tpr_v3_c2)), np.repeat('tpr_v3_c1', len(tpr_v3_c1))]),\n",
    "    'data': np.concatenate([tpr_v3,  tpr_v3_c2,  tpr_v3_c1])\n",
    "}\n",
    "df = pd.DataFrame(df)\n",
    "\n",
    "posthoc = sp.posthoc_dunn(df, val_col='data', group_col='group', p_adjust='holm')\n",
    "\n",
    "print(posthoc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbf04a6e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:18:29.958884Z",
     "start_time": "2024-06-17T13:18:29.939502Z"
    }
   },
   "outputs": [],
   "source": [
    "Tvr = data[data['RI_status'] == 1]['Tvr'].dropna()\n",
    "Tvr_v0 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 0)]['Tvr']\n",
    "Tvr_v1 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 1)]['Tvr']\n",
    "Tvr_v2 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 2)]['Tvr']\n",
    "Tvr_v3 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 3)]['Tvr']\n",
    "Tvr_v4 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 4)]['Tvr']\n",
    "\n",
    "\n",
    "Tvr_c1 = data[(data['RI_status'] == 1) & (data['Years'] == 2021)]['Tvr'].dropna()\n",
    "Tvr_v0_c1 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 0) & (data['Years'] == 2021)]['Tvr']\n",
    "Tvr_v1_c1 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 1) & (data['Years'] == 2021)]['Tvr']\n",
    "Tvr_v2_c1 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 2) & (data['Years'] == 2021)]['Tvr']\n",
    "Tvr_v3_c1 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 3) & (data['Years'] == 2021)]['Tvr']\n",
    "Tvr_v4_c1 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 4) & (data['Years'] == 2021)]['Tvr']\n",
    "\n",
    "\n",
    "Tvr_c2 = data[(data['RI_status'] == 1) & (data['Years'] == 2022)]['Tvr'].dropna()\n",
    "Tvr_v0_c2 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 0) & (data['Years'] == 2022)]['Tvr']\n",
    "Tvr_v1_c2 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 1) & (data['Years'] == 2022)]['Tvr']\n",
    "Tvr_v2_c2 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 2) & (data['Years'] == 2022)]['Tvr']\n",
    "Tvr_v3_c2 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 3) & (data['Years'] == 2022)]['Tvr']\n",
    "Tvr_v4_c2 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 4) & (data['Years'] == 2022)]['Tvr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22bd21f8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:18:33.134070Z",
     "start_time": "2024-06-17T13:18:33.084195Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "conditions = [\n",
    "    ('Tvr', 'RI_status == 1'),\n",
    "    ('Tvr_v0', '(RI_status == 1) & (Vaccine_status == 0)'),\n",
    "    ('Tvr_v1', '(RI_status == 1) & (Vaccine_status == 1)'),\n",
    "    ('Tvr_v2', '(RI_status == 1) & (Vaccine_status == 2)'),\n",
    "    ('Tvr_v3', '(RI_status == 1) & (Vaccine_status == 3)'),\n",
    "    ('Tvr_v4', '(RI_status == 1) & (Vaccine_status == 4)'),\n",
    "    ('Tvr_c1', '(RI_status == 1) & (Years == 2021)'),\n",
    "    ('Tvr_v0_c1', '(RI_status == 1) & (Vaccine_status == 0) & (Years == 2021)'),\n",
    "    ('Tvr_v1_c1', '(RI_status == 1) & (Vaccine_status == 1) & (Years == 2021)'),\n",
    "    ('Tvr_v2_c1', '(RI_status == 1) & (Vaccine_status == 2) & (Years == 2021)'),\n",
    "    ('Tvr_v3_c1', '(RI_status == 1) & (Vaccine_status == 3) & (Years == 2021)'),\n",
    "    ('Tvr_v4_c1', '(RI_status == 1) & (Vaccine_status == 4) & (Years == 2021)'),\n",
    "    ('Tvr_c2', '(RI_status == 1) & (Years == 2022)'),\n",
    "    ('Tvr_v0_c2', '(RI_status == 1) & (Vaccine_status == 0) & (Years == 2022)'),\n",
    "    ('Tvr_v1_c2', '(RI_status == 1) & (Vaccine_status == 1) & (Years == 2022)'),\n",
    "    ('Tvr_v2_c2', '(RI_status == 1) & (Vaccine_status == 2) & (Years == 2022)'),\n",
    "    ('Tvr_v3_c2', '(RI_status == 1) & (Vaccine_status == 3) & (Years == 2022)'),\n",
    "    ('Tvr_v4_c2', '(RI_status == 1) & (Vaccine_status == 4) & (Years == 2022)')\n",
    "]\n",
    "\n",
    "# 存储结果的字典\n",
    "results = {}\n",
    "\n",
    "for condition_name, condition_query in conditions:\n",
    "    try:\n",
    "        # 获取数据\n",
    "        subset_data = data.query(condition_query)['Tvr']\n",
    "        \n",
    "        # 描述统计\n",
    "        describe_stats = subset_data.describe()\n",
    "        \n",
    "        # 正态性检验\n",
    "        shapiro_stat, shapiro_p = shapiro(subset_data)\n",
    "        \n",
    "        # 存储结果\n",
    "        results[condition_name] = {\n",
    "            'describe_stats': describe_stats,\n",
    "            'shapiro_stat': shapiro_stat,\n",
    "            'shapiro_p': shapiro_p\n",
    "        }\n",
    "    except ValueError as e:\n",
    "        print(f\"An error occurred for condition {condition_name}: {e}\")\n",
    "\n",
    "# 打印结果\n",
    "for condition_name, result in results.items():\n",
    "    print(f\"Condition: {condition_name}\")\n",
    "    print(\"Describe Statistics:\")\n",
    "    print(result['describe_stats'])\n",
    "    print(\"Shapiro-Wilk Normality Test:\")\n",
    "    print(f\"Statistic: {result['shapiro_stat']}, p-value: {result['shapiro_p']}\")\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74be18b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:11:06.707721Z",
     "start_time": "2024-06-17T13:11:06.691622Z"
    }
   },
   "outputs": [],
   "source": [
    "Tvr_v2_c2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac03aa09",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:11:07.547718Z",
     "start_time": "2024-06-17T13:11:07.531593Z"
    }
   },
   "outputs": [],
   "source": [
    "data['Tvr'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aded6728",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:11:09.360557Z",
     "start_time": "2024-06-17T13:11:09.346955Z"
    }
   },
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "stats.levene(Tvr, Tvr_v1,Tvr_v2 ,Tvr_v3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "493c9acf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:11:10.051362Z",
     "start_time": "2024-06-17T13:11:10.042852Z"
    }
   },
   "outputs": [],
   "source": [
    "from scipy.stats import kruskal\n",
    "kruskal(Tvr, Tvr_v1,Tvr_v2 ,Tvr_v3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd7595bc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:11:11.508118Z",
     "start_time": "2024-06-17T13:11:11.442687Z"
    }
   },
   "outputs": [],
   "source": [
    "import scikit_posthocs as sp\n",
    "df = {\n",
    "    'group': np.concatenate([np.repeat('Tvr', len(Tvr)), np.repeat('Tvr_v1', len(Tvr_v1)), np.repeat('Tvr_v2', len(Tvr_v2)),\n",
    "                             np.repeat('Tvr_v3', len(Tvr_v3))]),\n",
    "    'data': np.concatenate([Tvr, Tvr_v1,Tvr_v2 ,Tvr_v3])\n",
    "}\n",
    "df = pd.DataFrame(df)\n",
    "\n",
    "posthoc = sp.posthoc_dunn(df, val_col='data', group_col='group', p_adjust='holm')\n",
    "\n",
    "print(posthoc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96173012",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:11:13.084691Z",
     "start_time": "2024-06-17T13:11:13.075102Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.levene(Tvr_c1, Tvr_v1_c1,Tvr_v2_c1 ,Tvr_v3_c1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3eb2cc10",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:11:14.044837Z",
     "start_time": "2024-06-17T13:11:14.036247Z"
    }
   },
   "outputs": [],
   "source": [
    "kruskal(Tvr_c1, Tvr_v1_c1,Tvr_v2_c1 ,Tvr_v3_c1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22e30090",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:11:14.996074Z",
     "start_time": "2024-06-17T13:11:14.931049Z"
    }
   },
   "outputs": [],
   "source": [
    "import scikit_posthocs as sp\n",
    "df = {\n",
    "    'group': np.concatenate([np.repeat('Tvr_c1', len(Tvr_c1)), np.repeat('Tvr_v1_c1', len(Tvr_v1_c1)),\n",
    "                             np.repeat('Tvr_v2_c1', len(Tvr_v2_c1)),\n",
    "                             np.repeat('Tvr_v3_c1', len(Tvr_v3_c1))]),\n",
    "    'data': np.concatenate([Tvr_c1, Tvr_v1_c1,Tvr_v2_c1 ,Tvr_v3_c1])\n",
    "}\n",
    "df = pd.DataFrame(df)\n",
    "\n",
    "posthoc = sp.posthoc_dunn(df, val_col='data', group_col='group', p_adjust='holm')\n",
    "\n",
    "print(posthoc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a54912eb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:11:17.042289Z",
     "start_time": "2024-06-17T13:11:17.035287Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.levene(Tvr_c2,  Tvr_v2_c2 ,Tvr_v3_c2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e099f964",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:11:18.061915Z",
     "start_time": "2024-06-17T13:11:18.052404Z"
    }
   },
   "outputs": [],
   "source": [
    "kruskal(Tvr_c2,  Tvr_v2_c2 ,Tvr_v3_c2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4220aa9c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:11:20.154583Z",
     "start_time": "2024-06-17T13:11:20.145580Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.levene(Tvr,  Tvr_c2 ,Tvr_c1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b024beeb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:11:23.178883Z",
     "start_time": "2024-06-17T13:11:23.163274Z"
    }
   },
   "outputs": [],
   "source": [
    "from scipy.stats import f_oneway\n",
    "\n",
    "f_oneway(Tvr, Tvr_c2, Tvr_c1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4678354",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:11:23.758410Z",
     "start_time": "2024-06-17T13:11:23.754907Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.levene(Tvr_v1,   Tvr_v1_c1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55819314",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:11:24.354934Z",
     "start_time": "2024-06-17T13:11:24.339823Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.levene(Tvr_v2,   Tvr_v2_c2,Tvr_v2_c1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3bad285",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:11:24.584722Z",
     "start_time": "2024-06-17T13:11:24.572595Z"
    }
   },
   "outputs": [],
   "source": [
    "kruskal(Tvr_v2,   Tvr_v2_c2,Tvr_v2_c1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36ec7a7f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:11:24.722535Z",
     "start_time": "2024-06-17T13:11:24.714508Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.levene(Tvr_v3,   Tvr_v3_c2,Tvr_v3_c1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea1306cd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:11:24.873555Z",
     "start_time": "2024-06-17T13:11:24.867555Z"
    }
   },
   "outputs": [],
   "source": [
    "f_oneway(Tvr_v3,   Tvr_v3_c2,Tvr_v3_c1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77677666",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:18:49.071421Z",
     "start_time": "2024-06-17T13:18:48.836520Z"
    }
   },
   "outputs": [],
   "source": [
    "from statsmodels.stats.multicomp import pairwise_tukeyhsd\n",
    "\n",
    "mmp = np.concatenate([Tvr_v3, Tvr_v3_c2, Tvr_v3_c1])\n",
    "labels = ['Tvr_v3'] * len(Tvr_v3) + ['Tvr_v3_c2'] * len(Tvr_v3_c2) + ['Tvr_v3_c1'] * len(Tvr_v3_c1)\n",
    "\n",
    "\n",
    "tukey_results = pairwise_tukeyhsd(mmp, labels, alpha=0.05)\n",
    "print(tukey_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8ff5c5b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:18:55.070155Z",
     "start_time": "2024-06-17T13:18:55.046090Z"
    }
   },
   "outputs": [],
   "source": [
    "tpr = data[data['RI_status'] == 1]['TPR']\n",
    "tpr_v0 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 0)]['TPR']\n",
    "tpr_v1 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 1)]['TPR']\n",
    "tpr_v2 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 2)]['TPR']\n",
    "tpr_v3 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 3)]['TPR']\n",
    "tpr_v4 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 4)]['TPR']\n",
    "\n",
    "\n",
    "tpr_c1 = data[(data['RI_status'] == 1) & (data['Years'] == 2021)]['TPR']\n",
    "tpr_v0_c1 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 0) & (data['Years'] == 2021)]['TPR']\n",
    "tpr_v1_c1 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 1) & (data['Years'] == 2021)]['TPR']\n",
    "tpr_v2_c1 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 2) & (data['Years'] == 2021)]['TPR']\n",
    "tpr_v3_c1 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 3) & (data['Years'] == 2021)]['TPR']\n",
    "tpr_v4_c1 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 4) & (data['Years'] == 2021)]['TPR']\n",
    "\n",
    "\n",
    "tpr_c2 = data[(data['RI_status'] == 1) & (data['Years'] == 2022)]['TPR']\n",
    "tpr_v0_c2 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 0) & (data['Years'] == 2022)]['TPR']\n",
    "tpr_v1_c2 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 1) & (data['Years'] == 2022)]['TPR']\n",
    "tpr_v2_c2 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 2) & (data['Years'] == 2022)]['TPR']\n",
    "tpr_v3_c2 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 3) & (data['Years'] == 2022)]['TPR']\n",
    "tpr_v4_c2 = data[(data['RI_status'] == 1) & (data['Vaccine_status'] == 4) & (data['Years'] == 2022)]['TPR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e8c71ac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:18:56.591527Z",
     "start_time": "2024-06-17T13:18:56.579506Z"
    }
   },
   "outputs": [],
   "source": [
    "df1 = data[data['RI_status'] == 1][['Vaccine_status', 'TPR']]\n",
    "df1['Years'] = 2010\n",
    "df1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f85c205",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:19:01.960931Z",
     "start_time": "2024-06-17T13:19:01.947390Z"
    }
   },
   "outputs": [],
   "source": [
    "tpr_df = pd.concat([df1, data[data['RI_status'] == 1][['Years', 'Vaccine_status', 'TPR']]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "716303ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:19:03.057221Z",
     "start_time": "2024-06-17T13:19:03.050660Z"
    }
   },
   "outputs": [],
   "source": [
    "tpr_df = tpr_df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8883b55a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:19:03.624837Z",
     "start_time": "2024-06-17T13:19:03.603257Z"
    }
   },
   "outputs": [],
   "source": [
    "tpr_df = tpr_df.sort_values(by='Years')\n",
    "tpr_df['Years'] = tpr_df['Years'].replace({2021: 'Cohort 1', 2022: 'Cohort 2',2010:'Overall'})\n",
    "tpr_df['Vaccine_status'] = tpr_df['Vaccine_status'].replace({0:'Unvaccinated', 1: '1 dose',\n",
    "                                                             2:'2 dose',3: '3 dose',\n",
    "                                                             4: '4 dose'})\n",
    "\n",
    "\n",
    "\n",
    "tpr_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e2470f0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:19:08.798001Z",
     "start_time": "2024-06-17T13:19:08.667650Z"
    }
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['Times New Roman']\n",
    "plt.rcParams.update({'font.weight': 'bold'})\n",
    "plt.rcParams['font.size'] = 20\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "\n",
    "sns.boxplot(x=tpr_df[\"Years\"], y=tpr_df[\"TPR\"], hue=tpr_df['Vaccine_status'], data=data, palette=\"rocket\")\n",
    "plt.ylabel(\"Tpr (Days)\", fontsize=22, fontweight='bold')\n",
    "plt.xlabel(\"\", fontsize=15, fontweight='bold')   \n",
    "plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), ncol=2, prop={'family': 'Times New Roman', 'size': 20, 'weight': 'bold'})\n",
    "plt.ylim(0, 500)\n",
    "plt.savefig(\"D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\Figs\\\\TPR.pdf\",\n",
    "           dpi=300,bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "823c9533",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:19:15.016161Z",
     "start_time": "2024-06-17T13:19:14.998047Z"
    }
   },
   "outputs": [],
   "source": [
    "df1 = data[data['RI_status'] == 1][['Vaccine_status', 'Tvr']]\n",
    "df1['Years'] = 2010\n",
    "df1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51685947",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:19:18.197028Z",
     "start_time": "2024-06-17T13:19:18.186441Z"
    }
   },
   "outputs": [],
   "source": [
    "tvr_df = pd.concat([df1, data[data['RI_status'] == 1][['Years', 'Vaccine_status', 'Tvr']]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b1eadde",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:19:18.899743Z",
     "start_time": "2024-06-17T13:19:18.884514Z"
    }
   },
   "outputs": [],
   "source": [
    "tvr_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c06c6190",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:19:21.983052Z",
     "start_time": "2024-06-17T13:19:21.962970Z"
    }
   },
   "outputs": [],
   "source": [
    "tvr_df = tvr_df.reset_index()\n",
    "tvr_df = tvr_df.sort_values(by='Years')\n",
    "tvr_df['Years'] = tvr_df['Years'].replace({2021: 'Cohort 1', 2022: 'Cohort 2',2010:'Overall'})\n",
    "tvr_df['Vaccine_status'] = tvr_df['Vaccine_status'].replace({0:'Unvaccinated', 1: '1 dose',\n",
    "                                                             2:'2 dose',3: '3 dose',\n",
    "                                                             4: '4 dose'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea5cd2b7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:19:22.550660Z",
     "start_time": "2024-06-17T13:19:22.531517Z"
    }
   },
   "outputs": [],
   "source": [
    "tvr_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40d442c9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:19:26.449133Z",
     "start_time": "2024-06-17T13:19:26.339376Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "\n",
    "sns.boxplot(x=tvr_df[\"Years\"], y=tvr_df[\"Tvr\"], hue=tvr_df['Vaccine_status'], data=data, palette=\"rocket\")\n",
    "plt.ylabel(\"Tvr (Days)\", fontsize=22, fontweight='bold')\n",
    "plt.xlabel(\"\", fontsize=15, fontweight='bold')   \n",
    "plt.legend(loc='upper right', prop={'family': 'Times New Roman', 'size': 1, 'weight': 'bold'})\n",
    "plt.ylim(0, 750)\n",
    "plt.savefig(\"D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\Figs\\\\tvr.pdf\",\n",
    "          dpi=300,bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f3e84f9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:19:38.174656Z",
     "start_time": "2024-06-17T13:19:38.155591Z"
    }
   },
   "outputs": [],
   "source": [
    "pot_df = data[data['RI_status'] == 1][['Years', 'Vaccine_status', 'Tvr','TPR']]   \n",
    "pot_df = pot_df.sort_values(by='Vaccine_status', ascending=False)\n",
    "\n",
    "\n",
    "pot_df['Years'] = pot_df['Years'].replace({2021: 'Cohort 1', 2022: 'Cohort 2',2010:'Overall'})\n",
    "pot_df['Vaccine_status'] = pot_df['Vaccine_status'].replace({0:'Unvaccinated', 1: '1 dose',\n",
    "                                                             2:'2 dose',3: '3 dose',\n",
    "                                                             4: '4 dose'})\n",
    "\n",
    "\n",
    "pot_df = pot_df.rename(columns={\"Years\": \"Cohorts\", \"Vaccine_status\": \"Vaccination\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b64734cc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:19:41.448244Z",
     "start_time": "2024-06-17T13:19:41.291359Z"
    }
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['Times New Roman']\n",
    "plt.rcParams.update({'font.weight': 'bold'})\n",
    "plt.rcParams['font.size'] = 25\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "\n",
    "color_palette = \"twilight_shifted\"\n",
    "\n",
    "sns.scatterplot(data=pot_df, x=\"TPR\", y=\"Tvr\", size=\"Vaccination\", hue=\"Cohorts\", alpha=0.5, sizes=(20, 400), palette=color_palette)\n",
    "plt.ylim(-22, 800)\n",
    "plt.xlim(0, 600)\n",
    "plt.legend(prop={'family': 'Times New Roman', 'size': 25, 'weight': 'bold'}, bbox_to_anchor=(1.05, 1))\n",
    "plt.xlabel(\"Tpr (day)\", fontsize=25, fontweight='bold')\n",
    "plt.ylabel(\"Tvr (day)\", fontsize=25, fontweight='bold')\n",
    "plt.savefig(\"D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\Figs\\\\dur.pdf\",\n",
    "          # dpi=600,bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52bd0956",
   "metadata": {},
   "source": [
    "# 4. Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9534a1f0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:49:51.207832Z",
     "start_time": "2024-06-17T13:49:51.186832Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df = data[['sex', 'age_group','age', 'workpalce','Region','Persistent infection', 'symp_pi', 'Vaccine_status','Hospitalization',\n",
    "                 'Years','RI_status', 'T_ending']]    \n",
    "reg_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90eed853",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:49:52.130433Z",
     "start_time": "2024-06-17T13:49:52.119424Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df['Region'] = reg_df['Region'].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ae9b4fc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:49:53.061811Z",
     "start_time": "2024-06-17T13:49:53.041302Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "reg_df['sex'] = reg_df['sex'].replace({'Male': 0, 'Female': 1})\n",
    "reg_df['age_group'] = reg_df['age_group'].replace({'<14': 0, '15-64': 1, '65+': 2})\n",
    "\n",
    "reg_df['workpalce'] = reg_df['workpalce'].replace({'farm': 0, 'Government': 1,'business area': 2, 'company and factories': 3,\n",
    "                                        'household':4, 'school': 5,'hospital': 6 })\n",
    "\n",
    "reg_df['Region'] = reg_df['Region'].replace({'Xiangan district': 0, 'Tongan district': 1,'Haicang district': 2, 'Jimei district': 3,\n",
    "                                                  'Huli district':4, 'Siming district': 5 })\n",
    "\n",
    "\n",
    "reg_df['symp_pi'] = reg_df['symp_pi'].replace({'Asymptomatic': 0, 'Mild': 1,'Moderate': 2, 'Severe': 3})\n",
    "\n",
    "\n",
    "reg_df['Years'] = reg_df['Years'].replace({2021: 0, 2022: 1})\n",
    "reg_df['Vaccine_status'] = reg_df['Vaccine_status'].replace({np.nan: 5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3be5786d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:50:16.322271Z",
     "start_time": "2024-06-17T13:50:16.306271Z"
    }
   },
   "outputs": [],
   "source": [
    "def map_region_to_group(region):\n",
    "    if region in [0, 1]:\n",
    "        return 0\n",
    "    elif region in [2, 3]:\n",
    "        return 1\n",
    "    elif region in [4, 5]:\n",
    "        return 2\n",
    "    else:\n",
    "        return -1  # 处理未覆盖的情况，返回一个默认值或其他处理方式\n",
    "\n",
    "# 创建新列 'Reg_U'\n",
    "reg_df['Reg_U'] = reg_df['Region'].apply(map_region_to_group)\n",
    "\n",
    "reg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2fa43ca",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:50:27.466856Z",
     "start_time": "2024-06-17T13:50:27.448857Z"
    }
   },
   "outputs": [],
   "source": [
    "def map_vac_to_group(vac):\n",
    "    if vac in [0]:\n",
    "        return 0\n",
    "    elif vac in [1, 2]:\n",
    "        return 1\n",
    "    elif vac in [3, 4]:\n",
    "        return 2\n",
    "    else:\n",
    "        return 3\n",
    "\n",
    "reg_df['vac2'] = reg_df['Vaccine_status'].apply(map_vac_to_group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06676a26",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:50:58.546739Z",
     "start_time": "2024-06-17T13:50:58.408714Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df.to_excel('D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\reg_Data.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8398362",
   "metadata": {},
   "source": [
    "## 4.1 Univariate analysis with logistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7eeae85b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:35.874018Z",
     "start_time": "2024-06-17T13:51:35.856688Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ba1ddd6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:36.687988Z",
     "start_time": "2024-06-17T13:51:36.666988Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df_with_dummies = pd.get_dummies(reg_df, columns=['sex', 'age_group', 'workpalce', 'Region', 'Reg_U',\n",
    "       'Persistent infection', 'symp_pi', 'Vaccine_status', 'vac2', 'Hospitalization',\n",
    "       'Years'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab31e125",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:38.408382Z",
     "start_time": "2024-06-17T13:51:38.252820Z"
    }
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "results_df = pd.DataFrame(columns=['Variables', 'coef', 'std', 'z', 'p', 'OR', 'OR_95_upper', 'OR_95_lower'])\n",
    "\n",
    "for i in reg_df_with_dummies.columns:\n",
    "    X = reg_df_with_dummies[i]  # Select the current column as the independent variable\n",
    "    y = reg_df_with_dummies['RI_status']\n",
    "    X = sm.add_constant(X)\n",
    "    \n",
    "    try:\n",
    "        model = sm.Logit(y, X).fit()\n",
    "        \n",
    "        params = model.params\n",
    "        conf_int = model.conf_int()\n",
    "        conf_int['Odds Ratio'] = params\n",
    "        conf_int.columns = ['2.5%', '97.5%', 'Odds Ratio']\n",
    "        \n",
    "        conf_int['Odds Ratio'] = np.exp(conf_int['Odds Ratio'])\n",
    "        conf_int['2.5%'] = np.exp(conf_int['2.5%'])\n",
    "        conf_int['97.5%'] = np.exp(conf_int['97.5%'])\n",
    "        \n",
    "        # Append the results to the results DataFrame\n",
    "        results_df = results_df.append({\n",
    "            'Variables': i,\n",
    "            'coef': params[1],  # Get coefficient\n",
    "            'std': model.bse[1],  # Get standard error\n",
    "            'z': model.tvalues[1],  # Get z value\n",
    "            'p': model.pvalues[1],  # Get p value\n",
    "            'OR': conf_int.loc[i, 'Odds Ratio'],\n",
    "            'OR_95_upper': conf_int.loc[i, '97.5%'],\n",
    "            'OR_95_lower': conf_int.loc[i, '2.5%']\n",
    "        }, ignore_index=True)\n",
    "    \n",
    "    except np.linalg.LinAlgError as e:\n",
    "        print(f\"An error occurred for variable {i}: {e}\")\n",
    "\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf11eaa9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:38.621232Z",
     "start_time": "2024-06-17T13:51:38.605723Z"
    }
   },
   "outputs": [],
   "source": [
    "'#0A72D5'\n",
    "'#C12592'\n",
    "'#FEBB30'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b42f92a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:41.644528Z",
     "start_time": "2024-06-17T13:51:41.450127Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "plt.rcParams['font.sans-serif'] = ['Times New Roman']\n",
    "plt.rcParams.update({'font.weight': 'bold'})\n",
    "plt.rcParams['font.size'] = 30\n",
    "# 创建数据\n",
    "data = {\n",
    "    'Variable': [\n",
    "        'Regions (Developing areas)', 'Regions (Less developed areas)','Regions (Developed areas)',\n",
    "         'Persistent infection (No)', 'Persistent infection (Yes)',\n",
    "        'Hospitalization (No)','Hospitalization (Yes)',\n",
    "        'Variants (Delta)','Variants (Omicron)'\n",
    "    ],\n",
    "    'OR': [\n",
    "        1.00,19.09, 3.85,1,0.40,1,0.40,1,0.03,\n",
    "    ],\n",
    "    'CI_lower': [\n",
    "        1, 4.67, 1.36,1,0.17,1,0.87,1,0.01,\n",
    "    ],\n",
    "    'CI_upper': [\n",
    "        1, 78.06,  10.91, 1, 0.94,1, 4.90,1,0.11,\n",
    "        \n",
    "\n",
    "    ]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 绘制森林图\n",
    "fig, ax = plt.subplots(figsize=(10,12))\n",
    "ax.axvline(x=1, linestyle='--', color='k',alpha=0.6)\n",
    "# 绘制误差线\n",
    "ax.errorbar(df['OR'], df.index, xerr=[df['OR'] - df['CI_lower'], df['CI_upper'] - df['OR']], color='#C12592', ecolor='#C12592',fmt='o', \n",
    "            capsize=15, elinewidth=4, capthick=2, markersize=15)\n",
    "#ax.scatter(df['OR'], df.index, facecolors='none', edgecolors='#C12592', s=200)\n",
    "# 绘制垂直参考线\n",
    "#ax.axvline(x=1, linestyle='--', color='#0A72D5')\n",
    "\n",
    "# 设置y轴\n",
    "ax.set_yticks(df.index)\n",
    "ax.set_yticklabels(df['Variable'])\n",
    "\n",
    "# 设置x轴\n",
    "ax.set_xlabel('Odds Ratio (OR)', fontdict={'family': 'Times New Roman', 'weight': 'bold', 'size': 30})\n",
    "\n",
    "# 反转y轴\n",
    "ax.invert_yaxis()\n",
    "\n",
    "#plt.title('Forest Plot of Logistic Regression Results')\n",
    "plt.savefig('D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\Figs\\\\多因素logistic.pdf',dpi=300,  bbox_inches='tight') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cd3a235",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:29:54.070525Z",
     "start_time": "2024-06-17T13:29:53.763261Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "plt.rcParams['font.sans-serif'] = ['Times New Roman']\n",
    "plt.rcParams.update({'font.weight': 'bold'})\n",
    "plt.rcParams['font.size'] = 30\n",
    "# 创建数据\n",
    "data = {\n",
    "    'Variable': [ 'Sex (Male)', 'Sex (Female))', 'Age',\n",
    "        'Regions (Developing areas)', 'Regions (Less developed areas)','Regions (Developed areas)',\n",
    "         'Persistent infection (No)', 'Persistent infection (Yes)',\n",
    "          'Unvaccination', 'Vaccination (1-2 doses)','Vaccination (> 2 doses)',    'Vaccination (Unknown)',    \n",
    "        'Hospitalization (No)','Hospitalization (Yes)',\n",
    "        'Variants (Delta)','Variants (Omicron)'\n",
    "    ],\n",
    "    'RR': [\n",
    "        1.00, 0.93,\n",
    "        1,\n",
    "        1,5.87,2.62,\n",
    "        1,0.50,\n",
    "        1, 2.87,1.67,1.06, \n",
    "        1,1.46,\n",
    "        1,20\n",
    "    ],\n",
    "    'CI_lower': [\n",
    "        1, 0.52, \n",
    "        0.98,\n",
    "        1,2.54,1.13,\n",
    "        1,0.25,\n",
    "        1,0.29,0.17,0.05,\n",
    "        1,0.71,\n",
    "        1,0\n",
    "    ],\n",
    "    'CI_upper': [\n",
    "        1, 1.68, \n",
    "        1.02, \n",
    "        1, 13.58,6.10,\n",
    "        1, 0.99,\n",
    "        1,28.14,16.04, 22.45,\n",
    "        1,3,\n",
    "        1,40\n",
    "        \n",
    "\n",
    "    ]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 绘制森林图\n",
    "fig, ax = plt.subplots(figsize=(10,12))\n",
    "ax.axvline(x=1, linestyle='--', color='k',alpha=0.6)\n",
    "# 绘制误差线\n",
    "ax.errorbar(df['RR'], df.index, xerr=[df['RR'] - df['CI_lower'], df['CI_upper'] - df['RR']], color='#0A72D5', ecolor='#0A72D5',fmt='o', \n",
    "            capsize=15, elinewidth=4, capthick=2, markersize=15)\n",
    "#ax.scatter(df['OR'], df.index, facecolors='none', edgecolors='#C12592', s=200)\n",
    "# 绘制垂直参考线\n",
    "\n",
    "\n",
    "# 设置y轴\n",
    "ax.set_yticks(df.index)\n",
    "ax.set_yticklabels(df['Variable'])\n",
    "\n",
    "# 设置x轴\n",
    "ax.set_xlabel('Risk Ratio (RR)', fontdict={'family': 'Times New Roman', 'weight': 'bold', 'size': 30})\n",
    "\n",
    "# 反转y轴\n",
    "ax.invert_yaxis()\n",
    "\n",
    "#plt.title('Forest Plot of Logistic Regression Results')\n",
    "plt.savefig('D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\Figs\\\\多因素COX.pdf',dpi=300,  bbox_inches='tight') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78c1c97e",
   "metadata": {},
   "source": [
    "## 4.2 muti-logistics reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7293f9ca",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:45.920433Z",
     "start_time": "2024-06-17T13:51:45.910433Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d914fcc0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:46.499143Z",
     "start_time": "2024-06-17T13:51:46.491143Z"
    }
   },
   "outputs": [],
   "source": [
    "columns_to_convert = [ 'sex', 'age_group',  'workpalce', 'Region', 'Reg_U',\n",
    "       'Persistent infection', 'symp_pi', 'Vaccine_status', 'vac2', 'Hospitalization',\n",
    "       'Years']\n",
    "reg_df[columns_to_convert] = reg_df[columns_to_convert].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58d1a33b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:47.155716Z",
     "start_time": "2024-06-17T13:51:47.137716Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "reg_df = reg_df.rename(columns={'Persistent infection': 'Persistent_infection'})\n",
    "reg_df = reg_df.rename(columns={'RI_status': 'out'})\n",
    "reg_df = reg_df.rename(columns={'Hospitalization': 'Hosp'})\n",
    "reg_df = reg_df.rename(columns={'Years': 'Variants'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52d525f5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:47.885899Z",
     "start_time": "2024-06-17T13:51:47.841890Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "\n",
    "f1 = 'out ~ sex + age + workpalce + Reg_U +  Persistent_infection + symp_pi + vac2 +  Hosp + Variants'\n",
    "model = smf.logit(f1, data = reg_df )\n",
    "glm1 = model.fit()\n",
    "glm1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6205e52f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:48.926350Z",
     "start_time": "2024-06-17T13:51:48.882338Z"
    }
   },
   "outputs": [],
   "source": [
    "f2 = 'out ~ sex + age + Reg_U +  Persistent_infection + symp_pi + vac2 +  Hosp + Variants'\n",
    "model = smf.logit(f2, data = reg_df )\n",
    "glm2 = model.fit()\n",
    "glm2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb83debc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:49.573502Z",
     "start_time": "2024-06-17T13:51:49.545503Z"
    }
   },
   "outputs": [],
   "source": [
    "f3 = 'out ~ sex + age + Reg_U +  Persistent_infection  + vac2 +  Hosp + Variants'\n",
    "model = smf.logit(f3, data = reg_df )\n",
    "glm3 = model.fit()\n",
    "glm3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b36410f3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:49.769704Z",
     "start_time": "2024-06-17T13:51:49.742704Z"
    }
   },
   "outputs": [],
   "source": [
    "f4 = 'out ~  age + Reg_U +  Persistent_infection  + vac2 +  Hosp + Variants'\n",
    "model = smf.logit(f4, data = reg_df )\n",
    "glm4 = model.fit()\n",
    "glm4.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b57a5a28",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:49.921557Z",
     "start_time": "2024-06-17T13:51:49.890620Z"
    }
   },
   "outputs": [],
   "source": [
    "f5 = 'out ~  Reg_U +  Persistent_infection  + vac2 +  Hosp + Variants'\n",
    "model = smf.logit(f5, data = reg_df )\n",
    "glm5 = model.fit()\n",
    "glm5.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73ab8e2a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:50.072188Z",
     "start_time": "2024-06-17T13:51:50.051178Z"
    }
   },
   "outputs": [],
   "source": [
    "f6 = 'out ~  Reg_U +  Persistent_infection   +  Hosp + Variants'\n",
    "model = smf.logit(f6, data = reg_df )\n",
    "glm6 = model.fit()\n",
    "glm6.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27f37ec1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:50.207512Z",
     "start_time": "2024-06-17T13:51:50.187502Z"
    }
   },
   "outputs": [],
   "source": [
    "f7 = 'out ~  Reg_U +  Persistent_infection     + Variants'\n",
    "model = smf.logit(f7, data = reg_df )\n",
    "glm7 = model.fit()\n",
    "glm7.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "777a78fd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:50.404433Z",
     "start_time": "2024-06-17T13:51:50.372956Z"
    }
   },
   "outputs": [],
   "source": [
    "f8 = 'out ~  Reg_U      + Variants'\n",
    "model = smf.logit(f8, data = reg_df )\n",
    "glm8 = model.fit()\n",
    "glm8.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ea1e5c6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:50.617037Z",
     "start_time": "2024-06-17T13:51:50.586017Z"
    }
   },
   "outputs": [],
   "source": [
    "f9 = 'out ~  Reg_U +   Variants + Reg_U *Variants'\n",
    "model = smf.logit(f9, data = reg_df )\n",
    "glm9 = model.fit()\n",
    "glm9.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da390ec6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:51.694427Z",
     "start_time": "2024-06-17T13:51:51.666037Z"
    }
   },
   "outputs": [],
   "source": [
    "f10 = 'out ~  Reg_U'\n",
    "model = smf.logit(f10, data = reg_df )\n",
    "glm10 = model.fit()\n",
    "glm10.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14edcc82",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:51.884982Z",
     "start_time": "2024-06-17T13:51:51.865889Z"
    }
   },
   "outputs": [],
   "source": [
    "f11 = 'out ~  Variants'\n",
    "model = smf.logit(f11, data = reg_df )\n",
    "glm11 = model.fit()\n",
    "glm11.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88dc0bdb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:52.040572Z",
     "start_time": "2024-06-17T13:51:52.028063Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "\n",
    "predicted_values = glm1.predict(reg_df)\n",
    "\n",
    "predicted_classes = (predicted_values > 0.5).astype(int)\n",
    "\n",
    "\n",
    "conf_matrix = confusion_matrix(reg_df['out'], predicted_classes)\n",
    "\n",
    "accuracy = accuracy_score(reg_df['out'], predicted_classes)\n",
    "\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ed04c1b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:52.865041Z",
     "start_time": "2024-06-17T13:51:52.842032Z"
    }
   },
   "outputs": [],
   "source": [
    "regression_metrics = pd.DataFrame(columns=['included_variables ','AIC', 'BIC', 'R-squared'])\n",
    "\n",
    "glm_models = [glm1, glm2, glm3, glm4, glm5, glm6, glm7, glm8,glm9,glm10,glm11]  \n",
    "\n",
    "for i in range(11):\n",
    "\n",
    "    glm = glm_models[i]\n",
    "    coefficients = glm.params\n",
    "    included_variables = coefficients[coefficients != 0].index.tolist()\n",
    "    AIC = glm.aic\n",
    "    BIC = glm.bic\n",
    "    r_squared = glm.prsquared\n",
    "    \n",
    "\n",
    "   \n",
    " \n",
    "    regression_metrics.loc[f'glm {i+1}', :] = [included_variables,AIC, BIC, r_squared]\n",
    "\n",
    "regression_metrics_df  = pd.concat([regression_metrics], axis=1)\n",
    "regression_metrics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "578eb034",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:53.561619Z",
     "start_time": "2024-06-17T13:51:53.528110Z"
    }
   },
   "outputs": [],
   "source": [
    "regression_metrics_df.to_excel('D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\log_reg_index.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d1c77e0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:55.225077Z",
     "start_time": "2024-06-17T13:51:55.202050Z"
    }
   },
   "outputs": [],
   "source": [
    "params = glm1.params\n",
    "conf_int = glm1.conf_int()\n",
    "std_err = glm1.bse\n",
    "\n",
    "# 计算 odds ratio\n",
    "odds_ratio = np.exp(params)\n",
    "\n",
    "# 计算置信区间\n",
    "ci_lower = np.exp(conf_int[0])\n",
    "ci_upper = np.exp(conf_int[1])\n",
    "\n",
    "# 计算95%置信区间\n",
    "ci_lower_err = np.exp(params - 1.96 * std_err)\n",
    "ci_upper_err = np.exp(params + 1.96 * std_err)\n",
    "\n",
    "# 创建包含结果的数据框\n",
    "ORresults = pd.DataFrame({'OR': odds_ratio, '95% CI Lower':ci_lower, '95% CI Upper': ci_upper})\n",
    "ORresults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "515fbb4f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:55.406801Z",
     "start_time": "2024-06-17T13:51:55.387801Z"
    }
   },
   "outputs": [],
   "source": [
    "params = glm6.params\n",
    "conf_int = glm6.conf_int()\n",
    "std_err = glm6.bse\n",
    "\n",
    "# 计算 odds ratio\n",
    "odds_ratio = np.exp(params)\n",
    "\n",
    "# 计算置信区间\n",
    "ci_lower = np.exp(conf_int[0])\n",
    "ci_upper = np.exp(conf_int[1])\n",
    "\n",
    "# 计算95%置信区间\n",
    "ci_lower_err = np.exp(params - 1.96 * std_err)\n",
    "ci_upper_err = np.exp(params + 1.96 * std_err)\n",
    "\n",
    "# 创建包含结果的数据框\n",
    "ORresults = pd.DataFrame({'OR': odds_ratio, '95% CI Lower':ci_lower, '95% CI Upper': ci_upper})\n",
    "ORresults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "570fea18",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:55.588733Z",
     "start_time": "2024-06-17T13:51:55.555733Z"
    }
   },
   "outputs": [],
   "source": [
    "summary_data = glm6.summary()\n",
    "\n",
    "# 将摘要信息转换为 DataFrame\n",
    "summary_df = pd.DataFrame(summary_data.tables[1])\n",
    "\n",
    "# 将 DataFrame 导出到 Excel 文件\n",
    "summary_df.to_excel('D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\muti_reg_res.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d144ff6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:51:58.637505Z",
     "start_time": "2024-06-17T13:51:58.599503Z"
    }
   },
   "outputs": [],
   "source": [
    "ORresults.to_excel('D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\muti_reg_OR.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb5e9ab5",
   "metadata": {},
   "source": [
    "## 4.3 Collinearity diagnostics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75aca052",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:52:00.115384Z",
     "start_time": "2024-06-17T13:52:00.104374Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2006cf4d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:52:01.558229Z",
     "start_time": "2024-06-17T13:52:01.536174Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df_with_dummies = pd.get_dummies(reg_df, columns=['sex', 'age_group', 'age', 'workpalce', 'Region',\n",
    "       'Persistent_infection', 'symp_pi', 'Vaccine_status', 'Hosp', 'Variants',\n",
    "        'Reg_U', 'vac2'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a804dc27",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:52:02.305422Z",
     "start_time": "2024-06-17T13:52:02.294421Z"
    }
   },
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from statsmodels.tools.tools import add_constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da828936",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:52:03.257733Z",
     "start_time": "2024-06-17T13:52:03.235186Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "X = reg_df[[  'Reg_U',\n",
    "       'Persistent_infection']]\n",
    "X = add_constant(X)  \n",
    "vif_data = pd.DataFrame()\n",
    "vif_data['feature'] = X.columns\n",
    "vif_data['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "vif_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88fde878",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:52:04.749596Z",
     "start_time": "2024-06-17T13:52:04.736514Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "vif = pd.DataFrame()\n",
    "vif[\"variables\"] = X.columns\n",
    "vif[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "tolerance = pd.DataFrame()\n",
    "tolerance[\"variables\"] = X.columns\n",
    "tolerance[\"tolerance\"] = [1 / vif_value for vif_value in vif[\"VIF\"]]\n",
    "tolerance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c4c721c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:52:09.451448Z",
     "start_time": "2024-06-17T13:52:05.098038Z"
    }
   },
   "outputs": [],
   "source": [
    "X = add_constant(reg_df_with_dummies )  \n",
    "vif_data1 = pd.DataFrame()\n",
    "vif_data1['feature'] = reg_df_with_dummies .columns\n",
    "vif_data1['VIF'] = [variance_inflation_factor(reg_df_with_dummies .values, i) for i in range(reg_df_with_dummies .shape[1])]\n",
    "vif_data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a3434d3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:52:09.466191Z",
     "start_time": "2024-06-17T13:52:09.452794Z"
    }
   },
   "outputs": [],
   "source": [
    "X = add_constant(reg_df[['wp2','reg3','perinf', 'hosp','var']])  \n",
    "vif_data = pd.DataFrame()\n",
    "vif_data['feature'] = ['wp2','reg3','perinf', 'hosp','var']\n",
    "vif_data['VIF'] = [variance_inflation_factor(reg_df[['wp2','reg3','perinf', 'hosp','var']].values, i) for i in range(reg_df[['wp2','reg3','perinf', 'hosp','var']].shape[1])]\n",
    "vif_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc349f70",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:52:09.467191Z",
     "start_time": "2024-06-17T13:52:09.467191Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df_with_dummies.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ead9e111",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:52:09.468562Z",
     "start_time": "2024-06-17T13:52:09.468562Z"
    }
   },
   "outputs": [],
   "source": [
    "X = add_constant(reg_df_with_dummies[['wp2_1','reg3_1', 'reg3_2','perinf_1', 'hosp_1', 'var_1']])  \n",
    "vif_data2 = pd.DataFrame()\n",
    "vif_data2['feature'] = ['wp2_1','reg3_1', 'reg3_2','perinf_1', 'hosp_1', 'var_1']\n",
    "vif_data2['VIF'] = [variance_inflation_factor(reg_df_with_dummies[['wp2_1','reg3_1', 'reg3_2','perinf_1', 'hosp_1', 'var_1']].values, i) \n",
    "                   for i in range(reg_df_with_dummies[['wp2_1','reg3_1', 'reg3_2','perinf_1', 'hosp_1', 'var_1']].shape[1])]\n",
    "vif_data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2a8618d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-21T12:47:52.061847Z",
     "start_time": "2024-02-21T12:47:52.008869Z"
    }
   },
   "outputs": [],
   "source": [
    "vif_data1.to_excel('D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\muti_reg_多重共线性诊断.xlsx')\n",
    "vif_data2.to_excel('D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\muti_reg_多重共线性诊断2.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb0b2f0e",
   "metadata": {},
   "source": [
    "## 4.4 gam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c80674e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:52:10.372227Z",
     "start_time": "2024-06-17T13:52:10.336217Z"
    }
   },
   "outputs": [],
   "source": [
    "from pygam import LinearGAM,LogisticGAM,  s, f, GAM, te, l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34dd21ec",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:52:11.164285Z",
     "start_time": "2024-06-17T13:52:11.138776Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f5f0f24",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:52:25.608364Z",
     "start_time": "2024-06-17T13:52:18.345775Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "lam = np.logspace(-6, 1, 10)\n",
    "lams = [lam]*5\n",
    "X_reg =  reg_df[[  'Reg_U', 'Persistent_infection','symp_pi',   'Hosp', 'Variants']].values\n",
    "Y = reg_df['out']\n",
    "\n",
    "gam = LogisticGAM( f(0) + f(1)+ f(2) + f(3)+ f(4)   ).gridsearch(X_reg, Y,lam =lams)\n",
    "gam.summary()\n",
    "\n",
    "print(f\"\\R: {gam.statistics_['pseudo_r2']['McFadden']}\")\n",
    "print(f\"\\Gcv: {gam.statistics_['GCV']}\")\n",
    "print(f\"\\AIC: {gam.statistics_['AIC']}\")\n",
    "print(f\"\\Accuracy: {gam.accuracy(X_reg, Y)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c6f8e8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-02T13:06:01.210262Z",
     "start_time": "2024-04-02T13:06:00.729349Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "fig, axs = plt.subplots(1,5, figsize=(28, 10));\n",
    "\n",
    "titles = ['Region', 'Persistent infection', 'Symptoms','Hospitalization', 'Variants']\n",
    "for i, ax in enumerate(axs):\n",
    "    XX = gam.generate_X_grid(term=i)\n",
    "    ax.plot(XX[:, i], gam.partial_dependence(term=i, X=XX),linewidth = 6 ,color = '#0000FF', alpha=0.5)\n",
    "    b = gam.partial_dependence(term = i, X=XX, width=.95)[1]\n",
    "    y1 = b[0:,:1]\n",
    "    y2 = b[0:,1:]\n",
    "    y1 = y1.reshape(-1)\n",
    "    y2 = y2.reshape(-1)\n",
    "    ax.fill_between(XX[:, i], y1, y2, facecolor='#9AC9DB', alpha=0.3)\n",
    "    if i == 0:\n",
    "        ax.set_xlim(0,2)\n",
    "        ax.set_ylabel('Risk of reinfection', fontdict={'fontsize': 35, 'fontweight': 'bold'})\n",
    "        #ax.set_xlabel('T (℃)', fontdict={'fontsize': 26, 'fontweight': 'bold'})\n",
    "        #ax.set_xticks(np.arange(15, 31, 5))\n",
    "        #ax.set_xticklabels([str(x) for x in np.arange(15, 31, 5)])\n",
    "    if i == 1:\n",
    "        ax.set_xlim(0,1)\n",
    "        #ax.set_xticks(np.arange(70, 91, 5))\n",
    "        #ax.set_xticklabels([str(x) for x in np.arange(70, 91, 5)])   \n",
    "        #ax.set_xlabel('U (%)', fontdict={'fontsize': 26, 'fontweight': 'bold'})\n",
    "    if i == 2:\n",
    "        ax.set_xlim(0,3)\n",
    "        #ax.set_xticks(np.arange(0, 2501, 1000))\n",
    "        #ax.set_xticklabels([str(x) for x in np.arange(0, 2501, 1000)])    \n",
    "        #ax.set_xlabel('RRR (mm)', fontdict={'fontsize': 26, 'fontweight': 'bold'})\n",
    "    if i == 3:\n",
    "        ax.set_xlim(0,1)\n",
    "       # ax.set_xlabel('Ff (m/s)', fontdict={'fontsize': 26, 'fontweight': 'bold'})\n",
    "       # ax.set_xticks(np.arange(1.5, 3.1, 0.5))\n",
    "       # ax.set_xticklabels([str(x) for x in np.arange(1.5, 3.5, 0.5)])   \n",
    "    if i == 4:\n",
    "        ax.set_xlim(0,1)   \n",
    "    ax.set_title(titles[i], fontdict={'fontname': 'Times New Roman', 'fontsize': 35, 'fontweight': 'bold'})\n",
    "    fig.subplots_adjust(wspace=0.25, hspace=0.5)\n",
    "    ax.tick_params(axis='x', labelsize=30)\n",
    "    ax.tick_params(axis='y', labelsize=30)\n",
    "plt.savefig(f\"D:\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\广义回归.pdf\", dpi=450, bbox_inches='tight') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5f5c3aa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T14:34:44.393361Z",
     "start_time": "2024-03-14T14:34:44.393361Z"
    }
   },
   "outputs": [],
   "source": [
    "constraints_list = ['convex', 'concave', 'monotonic_inc', 'monotonic_dec', 'none']\n",
    "lambda_list = [0.001,0.01,0.1,1,10,100,1000]\n",
    "#n_splines_list = [[4]*13, [10]*13, [100]*13, [1000]*13]\n",
    "\n",
    "\n",
    "X_reg = reg_df[[ 'reg3',   'var']].values\n",
    "Y = reg_df['out']\n",
    "\n",
    "\n",
    "\n",
    "results_df = pd.DataFrame(columns=['lambda', 'R','AIC', 'Accuracy'])\n",
    "\n",
    "for lambda_ in lambda_list:\n",
    "    constraints = None\n",
    "    gam =  LogisticGAM(f(0) + f(1) ,\n",
    "                      constraints=constraints, \n",
    "                    lam=lambda_).fit(X_reg, Y)\n",
    "\n",
    "    # 将结果存储到数据帧中\n",
    "    results_df = results_df.append({'lambda': lambda_,\n",
    "                                    'R': gam.statistics_['pseudo_r2']['McFadden'],\n",
    "                                    'AIC': gam.statistics_['AIC'],\n",
    "                                    'Accuracy': gam.accuracy(X_reg, Y)}, ignore_index=True)\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1d394dd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-02T08:42:16.105384Z",
     "start_time": "2024-04-02T08:42:16.095000Z"
    }
   },
   "outputs": [],
   "source": [
    "1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64cb52bc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-02T09:00:38.937727Z",
     "start_time": "2024-04-02T09:00:38.309427Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "constraints_list = ['convex', 'concave', 'monotonic_inc', 'monotonic_dec', 'none']\n",
    "lambda_list = [0.001,0.01,0.1,1,10,100,1000]\n",
    "n_splines_list = [[1000]*9,[250]*9, [25]*9, [15]*9,[10]*9,[5]*9,[4]*9,]\n",
    "\n",
    "\n",
    "X_reg = reg_df[['sex', 'age','workpalce','Reg_U', 'Persistent_infection','symp_pi', 'vac2', 'Hosp', 'Variants']].values\n",
    "Y = reg_df['out']\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "results_df = pd.DataFrame(columns=['lambda', 'R','AIC', 'Accuracy'])\n",
    "\n",
    "for lambda_ in lambda_list:\n",
    "    constraints = None\n",
    "    gam =  LogisticGAM(f(0) + s(1) + f(2) + f(3) + f(4) + f(5) + f(6)+ f(7) + f(8)  ,\n",
    "                      constraints= None, \n",
    "                    lam=lambda_).fit(X_reg, Y)\n",
    "\n",
    "    # 将结果存储到数据帧中\n",
    "    results_df = results_df.append({'lambda': lambda_,\n",
    "                                    'R': gam.statistics_['pseudo_r2']['McFadden'],\n",
    "                                    'AIC': gam.statistics_['AIC'],\n",
    "                                    'Accuracy': gam.accuracy(X_reg, Y)}, ignore_index=True)\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59ae3d61",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T12:58:49.568410Z",
     "start_time": "2024-03-14T12:58:43.203310Z"
    }
   },
   "outputs": [],
   "source": [
    "constraints_list = ['convex', 'concave', 'monotonic_inc', 'monotonic_dec', 'none']\n",
    "#lambda_list = [0.001,0.01,0.1,1,10,100,1000]\n",
    "#n_splines_list = [[10]*9,[9]*9, [8]*9, [7]*9,[6]*9,[5]*9,[4]*9,]\n",
    "\n",
    "\n",
    "X_reg = reg_df[['sex', 'age','workpalce','reg3', 'perinf','symp', 'vac', 'hosp', 'var']].values\n",
    "Y = reg_df['out']\n",
    "\n",
    "\n",
    "results_df = pd.DataFrame(columns=['lambda', 'R','AIC', 'Accuracy'])\n",
    "\n",
    "for constraints in constraints_list :\n",
    "    gam =  LogisticGAM(f(0) + s(1) + f(2) + f(3) + f(4) + f(5) + f(6)+ f(7) + f(8),\n",
    "                      constraints=constraints).fit(X_reg, Y)\n",
    "\n",
    "    # 将结果存储到数据帧中\n",
    "    results_df = results_df.append({'lambda': constraints,\n",
    "                                    'R': gam.statistics_['pseudo_r2']['McFadden'],\n",
    "                                    'AIC': gam.statistics_['AIC'],\n",
    "                                    'Accuracy': gam.accuracy(X_reg, Y)}, ignore_index=True)\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02c9bb77",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T04:22:31.499432Z",
     "start_time": "2024-03-14T04:22:17.056374Z"
    }
   },
   "outputs": [],
   "source": [
    "constraints_list = ['convex', 'concave', 'monotonic_inc', 'monotonic_dec', 'none']\n",
    "lambda_list = [0.001,0.01,0.1,1,10,100,1000]\n",
    "#n_splines_list = [[4]*13, [10]*13, [100]*13, [1000]*13]\n",
    "\n",
    "\n",
    "X_reg = reg_df[['sex', 'age','workpalce','reg3', 'perinf','symp', 'vac', 'hosp', 'var']].values\n",
    "Y = reg_df['out']\n",
    "\n",
    "\n",
    "for lambda_ in lambda_list:\n",
    "    constraints = None\n",
    "    gam =  LogisticGAM(f(0) + s(1) + f(2) + f(3) + f(4) + f(5) + f(6)+ f(7) + f(8),\n",
    "                      constraints=constraints, \n",
    "                    lam=lambda_).fit(X_reg, Y)\n",
    "\n",
    "    print(f\"\\nlambda_: {lambda_}\")\n",
    "    print(f\"\\R: {gam.statistics_['pseudo_r2']['McFadden']}\")\n",
    "    print(f\"\\Gcv: {gam.statistics_['GCV']}\")\n",
    "    print(f\"\\AIC: {gam.statistics_['AIC']}\")\n",
    "    print(f\"\\Accuracy: {gam.accuracy(X_reg, Y)}\")\n",
    "    \n",
    "    \n",
    "    plt.figure()\n",
    "    fig, axs = plt.subplots(1,9, figsize=(15, 8));\n",
    "\n",
    "    #titles = ['Temperature','Relative humidity', 'Precipitation', 'Windspeed' ]\n",
    "    for i, ax in enumerate(axs):\n",
    "        XX = gam.generate_X_grid(term=i)\n",
    "        ax.plot(XX[:, i], gam.partial_dependence(term=i, X=XX))\n",
    "        b = gam.partial_dependence(term = i, X=XX, width=.95)[1]\n",
    "        y1 = b[0:,:1]\n",
    "        y2 = b[0:,1:]\n",
    "        y1 = y1.reshape(-1)\n",
    "        y2 = y2.reshape(-1)\n",
    "        ax.fill_between(XX[:, i], y1, y2, facecolor='#9AC9DB', alpha=0.3)\n",
    "        #ax.set_title(titles[i], fontdict={'fontname': 'Times New Roman', 'fontsize': 30, 'fontweight': 'bold'})\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42a05712",
   "metadata": {},
   "source": [
    "## 4.4 survival analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "887bd13a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:52:35.644383Z",
     "start_time": "2024-06-17T13:52:35.519517Z"
    }
   },
   "outputs": [],
   "source": [
    "from lifelines import KaplanMeierFitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b179dffe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:52:36.068463Z",
     "start_time": "2024-06-17T13:52:36.059449Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d81b7012",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:52:36.733282Z",
     "start_time": "2024-06-17T13:52:36.721281Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4242309",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:52:59.749845Z",
     "start_time": "2024-06-17T13:52:59.728845Z"
    }
   },
   "outputs": [],
   "source": [
    "kmf = KaplanMeierFitter()\n",
    "fit = kmf.fit(reg_df.T_ending, reg_df.out)\n",
    "fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4928402d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:53:04.263702Z",
     "start_time": "2024-06-17T13:53:04.113165Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fig,ax = plt.subplots(figsize=(6,6))\n",
    "plt.rc('font', family='Times New Roman', weight='bold', size=30)\n",
    "kmf.plot_survival_function() \n",
    "\n",
    "plt.ylim(0.5,1)\n",
    "plt.xlim(0,500)\n",
    "plt.legend(loc = 'lower left',prop={'size': 20})\n",
    "ax.tick_params(axis='x', labelsize=25)\n",
    "ax.tick_params(axis='y', labelsize=25)\n",
    "plt.xlabel('Time (days)', fontweight='bold', fontsize=26)\n",
    "plt.ylabel('Cum Survival', fontweight='bold', fontsize=26)\n",
    "plt.tick_params(axis='x', labelsize=25)\n",
    "plt.tick_params(axis='y', labelsize=25)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\生存曲线.pdf\", dpi=450, bbox_inches='tight')  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20e6802a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:53:08.330974Z",
     "start_time": "2024-06-17T13:53:08.259445Z"
    }
   },
   "outputs": [],
   "source": [
    "kmf.cumulative_density_   # 累计概率密度图\n",
    "\n",
    "kmf.plot_cumulative_density() #\n",
    "plt.legend(loc = 'upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a01cff13",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:53:10.175818Z",
     "start_time": "2024-06-17T13:53:10.104780Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "kmf.plot(show_censors = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5131c72",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:53:13.233853Z",
     "start_time": "2024-06-17T13:53:13.216837Z"
    }
   },
   "outputs": [],
   "source": [
    " kmf.median_survival_time_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98c680b5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-15T07:08:26.867374Z",
     "start_time": "2024-04-15T07:08:26.829366Z"
    }
   },
   "outputs": [],
   "source": [
    "y =kmf.confidence_interval_\n",
    "y .to_excel('D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\生存CI.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee3df25f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-15T07:08:27.470390Z",
     "start_time": "2024-04-15T07:08:27.429861Z"
    }
   },
   "outputs": [],
   "source": [
    "x = kmf.survival_function_\n",
    "x.to_excel('D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\生存函数.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91605b4a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-15T07:08:28.303725Z",
     "start_time": "2024-04-15T07:08:28.261143Z"
    }
   },
   "outputs": [],
   "source": [
    "Z = kmf.event_table\n",
    "Z.to_excel('D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\生存event_table.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a812ed3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-15T07:08:29.900840Z",
     "start_time": "2024-04-15T07:08:29.868553Z"
    }
   },
   "outputs": [],
   "source": [
    "res = pd.concat([fit.event_table, fit.survival_function_], axis = 1)\n",
    "res.to_excel('D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\生存分析.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8156779",
   "metadata": {},
   "source": [
    "### 4.4.1 log rank tets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4725be3d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:53:15.180150Z",
     "start_time": "2024-06-17T13:53:15.160140Z"
    }
   },
   "outputs": [],
   "source": [
    "from lifelines import KaplanMeierFitter, CoxPHFitter        \n",
    "from lifelines.statistics import logrank_test, multivariate_logrank_test,pairwise_logrank_test \n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from lifelines.utils.sklearn_adapter import sklearn_adapter\n",
    "from lifelines.plotting import add_at_risk_counts\n",
    "from sklearn import metrics\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe13a32b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:53:17.006701Z",
     "start_time": "2024-06-17T13:53:17.000698Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba359602",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:53:17.675403Z",
     "start_time": "2024-06-17T13:53:17.664389Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df = reg_df .rename(columns={'sex':'Sex','Persistent_infection': 'Persistent infection', 'symp_pi': 'Symptoms',\n",
    "                                'Hosp':'Hospitalization', 'Variants':'Variants','age_group':'Age group','workpalce':'Workplace'})\n",
    "reg_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eee31491",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:53:18.586322Z",
     "start_time": "2024-06-17T13:53:18.577322Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15686437",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:53:24.570651Z",
     "start_time": "2024-06-17T13:53:24.567651Z"
    }
   },
   "outputs": [],
   "source": [
    "obj_list =['Sex', 'Age group', 'Workplace', 'Region', 'Reg_U','Persistent infection','Symptoms',\n",
    "           'Vaccine_status','Hospitalization','Variants']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fbc9a9e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:53:25.987471Z",
     "start_time": "2024-06-17T13:53:25.969432Z"
    }
   },
   "outputs": [],
   "source": [
    "obj_list = reg_df.select_dtypes(include=\"category\").columns\n",
    "obj_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4313f86",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:53:26.998928Z",
     "start_time": "2024-06-17T13:53:26.983929Z"
    }
   },
   "outputs": [],
   "source": [
    "obj_list =['Sex', 'Age group', 'Workplace', 'Reg_U','Persistent infection','Symptoms',\n",
    "           'Vaccine_status','Hospitalization','Variants']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fa0ca92",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:55:10.605325Z",
     "start_time": "2024-06-17T13:55:10.600316Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df = reg_df.rename(columns={'T_ending': 't'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acd9f373",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:58:35.959834Z",
     "start_time": "2024-06-17T13:58:33.881803Z"
    }
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.set(style='ticks', palette='Set2')\n",
    "plt.rc('font', family='Times New Roman', weight='bold', size=10)\n",
    "titles = ['Sex', 'Age', 'Workpalace', 'Region','Persistent infection','Symptoms','Vaccination','Hospitalization','Variants']\n",
    "fig, ax = plt.subplots(nrows=3, ncols=3, figsize=(12,12))  \n",
    "log_df = pd.DataFrame(columns=['feature', 'degrees_of_freedom','test_statistic','p-value'])\n",
    "pairs_dfs = pd.DataFrame()\n",
    "for nrow in range(3):\n",
    "    for ncol in range(3):\n",
    "        feature = obj_list[ncol*3 + nrow]            \n",
    "        for i in reg_df[feature].unique():\n",
    "            \n",
    "            kmf=KaplanMeierFitter()  \n",
    "            df_tmp =  reg_df.loc[ reg_df[feature] == i]\n",
    "            kmf.fit(df_tmp.t,  # 代表生存时长\n",
    "                    event_observed=df_tmp.out,  # 代表检验的组别\n",
    "                  label=i)  # 设置标签为 'Male' 或 'Female'\n",
    "            # 绘制生存曲线\n",
    "            kmf.plot_survival_function(ci_show=True, show_censors=False, ax=ax[nrow][ncol],label=i)  # 设置标签为 'Male' 或 'Female'\n",
    "    \n",
    "        \n",
    "        p_value = multivariate_logrank_test(event_durations=reg_df.t,\n",
    "                                            groups=reg_df[feature],\n",
    "                                            event_observed=reg_df.out).p_value\n",
    "        \n",
    "        test_statistic = multivariate_logrank_test(event_durations=reg_df.t,\n",
    "                                            groups=reg_df[feature],\n",
    "                                            event_observed=reg_df.out).test_statistic\n",
    "        degrees_of_freedom = multivariate_logrank_test(event_durations=reg_df.t,\n",
    "                                            groups=reg_df[feature],\n",
    "                                            event_observed=reg_df.out).degrees_of_freedom\n",
    "        \n",
    "        # 将结果添加到 DataFrame 中\n",
    "        log_df = log_df.append({'feature': feature,'degrees_of_freedom':degrees_of_freedom, 'test_statistic':test_statistic, \n",
    "                                'p-value': p_value}, ignore_index=True)\n",
    "        \n",
    "        if p_value < 0.05:\n",
    "            star = '*'\n",
    "            pairs = pairwise_logrank_test(event_durations=reg_df.t,\n",
    "                                   groups=reg_df[feature],\n",
    "                                   event_observed=reg_df.out)\n",
    "            pairs_df = pd.DataFrame(pairs.summary)\n",
    "            \n",
    "            pairs_df = pairs_df.reset_index()\n",
    "            pairs_df.columns = ['group1', 'group2', 'test_statistic', 'p-value', '-log2(p)']\n",
    "            pairs_df['feature'] = feature\n",
    "            pairs_dfs = pd.concat([pairs_dfs,pairs_df], ignore_index=True)\n",
    "         \n",
    "        else:\n",
    "            star = ''\n",
    "            \n",
    "    \n",
    "        ax[nrow][ncol].set_xlim(0,520,100)\n",
    "        ax[nrow][ncol].set_ylim(0,1.05)\n",
    "        ax[nrow][ncol].set_title(\"{}{}\".format(titles[ncol*3 + nrow], star), fontweight='bold', fontsize=16)  \n",
    "    \n",
    "        \n",
    "        if nrow != 2 or ncol != 1:\n",
    "            ax[nrow][ncol].set_xlabel('', fontweight='bold', fontsize=10)\n",
    "        else:\n",
    "            ax[nrow][ncol].set_xlabel('Time (days)', fontweight='bold', fontsize=16)\n",
    "            \n",
    "        if nrow != 1 or ncol != 0:\n",
    "            ax[nrow][ncol].set_ylabel('', fontweight='bold', fontsize=10)\n",
    "        else:\n",
    "            ax[nrow][ncol].set_ylabel('Cum Survival', fontweight='bold', fontsize=16)\n",
    "        \n",
    "        handles, labels = ax[nrow][ncol].get_legend_handles_labels()\n",
    "        handles = [handles[i] for i in np.argsort(labels)]\n",
    "        labels = sorted(labels)\n",
    "        ax[nrow][ncol].legend(handles, labels, loc='lower left')  # 对图例进行排序并更改标签值\n",
    "        \n",
    "        \n",
    "        if nrow == 0 and ncol == 0:\n",
    "            handles, labels = ax[nrow][ncol].get_legend_handles_labels()\n",
    "            labels = ['Male' if label == '0' else 'Female' for label in labels]\n",
    "            ax[nrow][ncol].legend(handles, labels)\n",
    "            \n",
    "        if nrow == 0 and ncol == 1:\n",
    "            handles, labels = ax[nrow][ncol].get_legend_handles_labels()\n",
    "            labels = [\n",
    "        'Developing areas' if label == '0'else\n",
    "        'Less developed areas' if label == '1' else\n",
    "        'Developed areas' if label == '2' else label\n",
    "        for label in labels]\n",
    "            ax[nrow][ncol].legend(handles, labels)\n",
    "        \n",
    "        if nrow == 0 and ncol == 2:\n",
    "            handles, labels = ax[nrow][ncol].get_legend_handles_labels()\n",
    "            labels = [\n",
    "        'Unvaccinated' if label == '0.0' else\n",
    "        '1 dose' if label == '1.0' else\n",
    "        '2 dose' if label == '2.0' else\n",
    "        '3 dose' if label == '3.0' else \n",
    "        '4 dose' if label == '4.0' else        \n",
    "        'Unknown' if label == '5.0' else label\n",
    "        for label in labels]\n",
    "            ax[nrow][ncol].legend(handles, labels)\n",
    "        \n",
    "        if nrow == 1 and ncol == 0:\n",
    "            handles, labels = ax[nrow][ncol].get_legend_handles_labels()\n",
    "            labels = [\n",
    "        '<14' if label == '0' else\n",
    "        '15-64' if label == '1' else\n",
    "        '65+' if label == '2' else label\n",
    "        for label in labels]\n",
    "            ax[nrow][ncol].legend(handles, labels)\n",
    "        \n",
    "        if nrow == 1 and ncol == 1:\n",
    "            handles, labels = ax[nrow][ncol].get_legend_handles_labels()\n",
    "            labels = ['No' if label == '0' else 'Yes' for label in labels]\n",
    "            ax[nrow][ncol].legend(handles, labels)\n",
    "        \n",
    "        if nrow == 1 and ncol == 2:\n",
    "            handles, labels = ax[nrow][ncol].get_legend_handles_labels()\n",
    "            labels = ['No' if label == '0' else 'Yes' for label in labels]\n",
    "            ax[nrow][ncol].legend(handles, labels)\n",
    "            \n",
    "        if nrow == 2 and ncol == 0:\n",
    "            handles, labels = ax[nrow][ncol].get_legend_handles_labels()\n",
    "            labels = [\n",
    "        'Farm' if label == '0' else\n",
    "        'Government' if label == '1' else\n",
    "        'Commercial center' if label == '2' else\n",
    "        'Enterprises' if label == '3' else \n",
    "        'Household' if label == '4' else\n",
    "        'School' if label == '5' else        \n",
    "        'Hospital' if label == '6' else label\n",
    "        for label in labels]\n",
    "            ax[nrow][ncol].legend(handles, labels)\n",
    "            \n",
    "        if nrow == 2 and ncol == 1:\n",
    "            handles, labels = ax[nrow][ncol].get_legend_handles_labels()\n",
    "            labels = [\n",
    "        'Asymptomatic' if label == '0' else\n",
    "        'Mild' if label == '1' else\n",
    "        'Moderate' if label == '2' else       \n",
    "        'Severe' if label == '3' else label\n",
    "        for label in labels]\n",
    "            ax[nrow][ncol].legend(handles, labels)\n",
    "            \n",
    "        if nrow == 2 and ncol == 2:\n",
    "            handles, labels = ax[nrow][ncol].get_legend_handles_labels()\n",
    "            labels = ['Delta' if label == '0' else 'Omicron' for label in labels]\n",
    "            ax[nrow][ncol].legend(handles, labels)    \n",
    "            \n",
    "            \n",
    "fig.text(0.1, 0.05, '* p < 0.05', fontweight='bold', fontsize=16)\n",
    "\n",
    "plt.savefig(f\"D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\log_rank.pdf\", dpi=450, bbox_inches='tight')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "623dcc06",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T03:55:02.769871Z",
     "start_time": "2024-05-29T03:55:02.601196Z"
    }
   },
   "outputs": [],
   "source": [
    "log_df.to_excel(('D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\log_df_test.xlsx'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd40f0b5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T03:55:03.315657Z",
     "start_time": "2024-05-29T03:55:03.265906Z"
    }
   },
   "outputs": [],
   "source": [
    "pairs_dfs.to_excel(('D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\Log_rank22.xlsx'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56c3ef0b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T03:55:15.521368Z",
     "start_time": "2024-05-29T03:55:15.313119Z"
    }
   },
   "outputs": [],
   "source": [
    " curve_df.to_excel(('D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\Log_rank生存曲线.xlsx'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ee8d45b",
   "metadata": {},
   "source": [
    "### 4.4.2 Hierarchical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49c784d8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:59:03.882202Z",
     "start_time": "2024-06-17T13:59:03.879202Z"
    }
   },
   "outputs": [],
   "source": [
    "obj_list =['Sex', 'Age group', 'Workplace', 'Reg_U','Persistent infection','Symptoms',\n",
    "           'Vaccine_status','Hospitalization','Variants']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fe6b341",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:59:04.440283Z",
     "start_time": "2024-06-17T13:59:04.423282Z"
    }
   },
   "outputs": [],
   "source": [
    "obj_list = pd.Index(obj_list)\n",
    "Nobj_list = obj_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c37adaf6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:59:04.925208Z",
     "start_time": "2024-06-17T13:59:04.920208Z"
    }
   },
   "outputs": [],
   "source": [
    "obj_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "358fe88f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:59:06.010170Z",
     "start_time": "2024-06-17T13:59:05.703429Z"
    }
   },
   "outputs": [],
   "source": [
    "for feature in Nobj_list.drop('Sex'):\n",
    "    for i in reg_df[feature].unique():\n",
    "        df_tmp = reg_df.loc[reg_df[feature] == i]\n",
    "        p_value = multivariate_logrank_test(event_durations = df_tmp.t,\n",
    "                                            groups=df_tmp.Sex,\n",
    "                                            event_observed=df_tmp.out \n",
    "                                           ).p_value\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f23e0bb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:59:07.752168Z",
     "start_time": "2024-06-17T13:59:07.425268Z"
    }
   },
   "outputs": [],
   "source": [
    "pairs_dfs = pd.DataFrame()  # initialize an empty DataFrame to store the results of pairwise_logrank_test\n",
    "\n",
    "for feature in Nobj_list.drop('Age group'):\n",
    "    for i in reg_df[feature].unique():\n",
    "        df_tmp = reg_df.loc[reg_df[feature] == i]\n",
    "        test_results = multivariate_logrank_test(event_durations = df_tmp.t,\n",
    "                                                 groups=df_tmp['Age group'],\n",
    "                                                 event_observed=df_tmp.out)\n",
    "        p_value = test_results.p_value\n",
    "        test_statistic = test_results.test_statistic\n",
    "        degrees_of_freedom = test_results.degrees_of_freedom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "500ccbb2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:59:10.167517Z",
     "start_time": "2024-06-17T13:59:09.815728Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pairs_dfs = pd.DataFrame()  # initialize an empty DataFrame to store the results of pairwise_logrank_test\n",
    "\n",
    "for feature in Nobj_list.drop('Hospitalization'):\n",
    "    for i in reg_df[feature].unique():\n",
    "        df_tmp = reg_df.loc[reg_df[feature] == i]\n",
    "        test_results = multivariate_logrank_test(event_durations = df_tmp.t,\n",
    "                                                 groups=df_tmp['Hospitalization'],\n",
    "                                                 event_observed=df_tmp.out)\n",
    "        p_value = test_results.p_value\n",
    "        test_statistic = test_results.test_statistic\n",
    "        degrees_of_freedom = test_results.degrees_of_freedom\n",
    "\n",
    "        if p_value <= 0.05:\n",
    "            p_value_text = 'p-value < 0.05, p is {:.3f}, test statistic is {:.3f}, degrees of freedom is {}'.format(p_value, test_statistic, degrees_of_freedom) if p_value < 0.05 else 'p-value = {:.3f}'.format(p_value)\n",
    "            print('Hospitalization : {}={}, logrank test {}'.format(feature, i, p_value_text))\n",
    "\n",
    "            pairs = pairwise_logrank_test(event_durations = df_tmp.t,\n",
    "                                          groups=df_tmp['Hospitalization'],\n",
    "                                          event_observed=df_tmp.out)\n",
    "            pairs_df = pd.DataFrame(pairs.summary)\n",
    "\n",
    "            pairs_df = pairs_df.reset_index()\n",
    "            pairs_df.columns = ['group1', 'group2', 'test_statistic', 'p-value', '-log2(p)']\n",
    "            pairs_df['feature'] = feature\n",
    "            pairs_dfs = pd.concat([pairs_dfs,pairs_df], ignore_index=True)\n",
    "pairs_dfs.to_excel(('D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\分层1.xlsx'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d657807c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:59:14.550697Z",
     "start_time": "2024-06-17T13:59:14.345089Z"
    }
   },
   "outputs": [],
   "source": [
    "fig,ax = plt.subplots(figsize=(6,6))\n",
    "plt.rc('font', family='Times New Roman', weight='bold', size=30)\n",
    "reg_df1 =  reg_df.loc[(reg_df['Hospitalization']== 0) & (reg_df.Workplace == 6)]\n",
    "kmf1 = KaplanMeierFitter().fit(reg_df1.t, \n",
    "                                   reg_df1.out, \n",
    "                                   label=' Hospitalization (No)')\n",
    "ax = kmf1.plot_survival_function(ax=ax,ci_show=True,  show_censors =False)\n",
    "\n",
    "\n",
    "reg_df2 =  reg_df.loc[(reg_df['Hospitalization']== 1) & (reg_df.Workplace == 6)]\n",
    "kmf2 = KaplanMeierFitter().fit(reg_df2.t, \n",
    "                                   reg_df2.out, \n",
    "                                   label=' Hospitalization (Yes)')\n",
    "\n",
    "ax = kmf2.plot_survival_function(ax=ax,ci_show=True,  show_censors = False)\n",
    "ax.set_xlabel('Time (days)', fontweight='bold', fontsize=25)\n",
    "ax.set_ylabel('Cum Survival', fontweight='bold', fontsize=25)\n",
    "ax.legend(prop={'size': 18})\n",
    "ax.tick_params(axis='x', labelsize=25)\n",
    "ax.tick_params(axis='y', labelsize=25)\n",
    "ax.set_title('Hospital', fontweight='bold', fontsize=30)\n",
    "#from lifelines.plotting import add_at_risk_counts\n",
    "#add_at_risk_counts(kmf1,kmf2, kmf3 ,ax=ax)\n",
    "plt.savefig(f\"D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\layers_1.pdf\", dpi=450, bbox_inches='tight')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "511cbcdc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:59:17.884253Z",
     "start_time": "2024-06-17T13:59:17.008542Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pairs_dfs = pd.DataFrame()  # initialize an empty DataFrame to store the results of pairwise_logrank_test\n",
    "\n",
    "for feature in Nobj_list.drop('Vaccine_status'):\n",
    "    for i in reg_df[feature].unique():\n",
    "        df_tmp = reg_df.loc[reg_df[feature] == i]\n",
    "        test_results = multivariate_logrank_test(event_durations = df_tmp.t,\n",
    "                                                 groups=df_tmp['Vaccine_status'],\n",
    "                                                 event_observed=df_tmp.out)\n",
    "        p_value = test_results.p_value\n",
    "        test_statistic = test_results.test_statistic\n",
    "        degrees_of_freedom = test_results.degrees_of_freedom\n",
    "\n",
    "        if p_value <= 0.05:\n",
    "            p_value_text = 'p-value < 0.05, p is {:.3f}, test statistic is {:.3f}, degrees of freedom is {}'.format(p_value, test_statistic, degrees_of_freedom) if p_value < 0.05 else 'p-value = {:.3f}'.format(p_value)\n",
    "            print('Vaccine_status : {}={}, logrank test {}'.format(feature, i, p_value_text))\n",
    "\n",
    "            pairs = pairwise_logrank_test(event_durations = df_tmp.t,\n",
    "                                          groups=df_tmp['Vaccine_status'],\n",
    "                                          event_observed=df_tmp.out)\n",
    "            pairs_df = pd.DataFrame(pairs.summary)\n",
    "\n",
    "            pairs_df = pairs_df.reset_index()\n",
    "            pairs_df.columns = ['group1', 'group2', 'test_statistic', 'p-value', '-log2(p)']\n",
    "            pairs_df['feature'] = feature\n",
    "            pairs_dfs = pd.concat([pairs_dfs,pairs_df], ignore_index=True)\n",
    "pairs_dfs.to_excel(('D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\分层2.xlsx'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98957502",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:59:19.698128Z",
     "start_time": "2024-06-17T13:59:19.432000Z"
    }
   },
   "outputs": [],
   "source": [
    "fig,ax = plt.subplots(figsize=(6,6))\n",
    "plt.rc('font', family='Times New Roman', weight='bold', size=30)\n",
    "reg_df1 =  reg_df.loc[(reg_df['Vaccine_status']== 0) & (reg_df.Sex == 0)]\n",
    "kmf1 = KaplanMeierFitter().fit(reg_df1.t, \n",
    "                                   reg_df1.out, \n",
    "                                   label='Unvaccination')\n",
    "ax = kmf1.plot_survival_function(ax=ax,ci_show=True,  show_censors = False)\n",
    "\n",
    "\n",
    "reg_df2 =  reg_df.loc[(reg_df['Vaccine_status']== 1) & (reg_df.Sex == 0)]\n",
    "kmf2 = KaplanMeierFitter().fit(reg_df2.t, \n",
    "                                   reg_df2.out, \n",
    "                                   label='1 dose')\n",
    "\n",
    "ax = kmf2.plot_survival_function(ax=ax,ci_show=True,  show_censors = False)\n",
    "\n",
    "\n",
    "\n",
    "reg_df3 =  reg_df.loc[(reg_df['Vaccine_status']== 2) & (reg_df.Sex == 0)]\n",
    "kmf3 = KaplanMeierFitter().fit(reg_df3.t, \n",
    "                                   reg_df3.out, \n",
    "                                   label='2 dose')\n",
    "\n",
    "ax = kmf3.plot_survival_function(ax=ax,ci_show=True,  show_censors =False)\n",
    "\n",
    "\n",
    "reg_df4 =  reg_df.loc[(reg_df['Vaccine_status']== 3) & (reg_df.Sex == 0)]\n",
    "kmf4 = KaplanMeierFitter().fit(reg_df4.t, \n",
    "                                   reg_df4.out, \n",
    "                                   label='3 dose')\n",
    "\n",
    "ax = kmf4.plot_survival_function(ax=ax,ci_show=True,  show_censors = False)\n",
    "\n",
    "reg_df5 =  reg_df.loc[(reg_df['Vaccine_status']== 4) & (reg_df.Sex == 0)]\n",
    "kmf5 = KaplanMeierFitter().fit(reg_df5.t, \n",
    "                                   reg_df5.out, \n",
    "                                   label='4 dose')\n",
    "\n",
    "ax = kmf5.plot_survival_function(ax=ax,ci_show=True,  show_censors = False)\n",
    "\n",
    "\n",
    "reg_df6 =  reg_df.loc[(reg_df['Vaccine_status']== 5) & (reg_df.Sex == 0)]\n",
    "kmf6 = KaplanMeierFitter().fit(reg_df6.t, \n",
    "                                   reg_df6.out, \n",
    "                                   label='Unknown')\n",
    "\n",
    "ax = kmf6.plot_survival_function(ax=ax,ci_show=True,  show_censors = False)\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlabel('Time (days)', fontweight='bold', fontsize=25)\n",
    "ax.set_ylabel('Cum Survival', fontweight='bold', fontsize=25)\n",
    "ax.legend(prop={'size':16})\n",
    "ax.tick_params(axis='x', labelsize=25)\n",
    "ax.tick_params(axis='y', labelsize=25)\n",
    "ax.set_title('Male', fontweight='bold', fontsize=30)\n",
    "#from lifelines.plotting import add_at_risk_counts\n",
    "#add_at_risk_counts(kmf1,kmf2, kmf3 ,ax=ax)\n",
    "plt.savefig(f\"D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\layers_2.pdf\", dpi=450, bbox_inches='tight')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95cf1076",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:59:20.559137Z",
     "start_time": "2024-06-17T13:59:20.337139Z"
    }
   },
   "outputs": [],
   "source": [
    "fig,ax = plt.subplots(figsize=(6,6))\n",
    "plt.rc('font', family='Times New Roman', weight='bold', size=30)\n",
    "reg_df1 =  reg_df.loc[(reg_df['Vaccine_status']== 0) & (reg_df['Age group'] ==2)]\n",
    "kmf1 = KaplanMeierFitter().fit(reg_df1.t, \n",
    "                                   reg_df1.out, \n",
    "                                   label='Unvaccination')\n",
    "ax = kmf1.plot_survival_function(ax=ax,ci_show=True,  show_censors = False)\n",
    "\n",
    "\n",
    "#reg_df2 =  reg_df.loc[(reg_df['Vaccine_status']== 1) & (reg_df['Age group'] ==2)]\n",
    "#kmf2 = KaplanMeierFitter().fit(reg_df2.t, \n",
    "                                 #  reg_df2.out, \n",
    "                                 #  label='1 dose')\n",
    "\n",
    "#ax = kmf2.plot_survival_function(ax=ax,ci_show=True,  show_censors = True)\n",
    "\n",
    "\n",
    "\n",
    "reg_df3 =  reg_df.loc[(reg_df['Vaccine_status']== 2) & (reg_df['Age group'] ==2)]\n",
    "kmf3 = KaplanMeierFitter().fit(reg_df3.t, \n",
    "                                   reg_df3.out, \n",
    "                                   label='2 dose')\n",
    "\n",
    "ax = kmf3.plot_survival_function(ax=ax,ci_show=True,  show_censors = False)\n",
    "\n",
    "\n",
    "reg_df4 =  reg_df.loc[(reg_df['Vaccine_status']== 3) & (reg_df['Age group'] ==2)]\n",
    "kmf4 = KaplanMeierFitter().fit(reg_df4.t, \n",
    "                                   reg_df4.out, \n",
    "                                  label='3 dose')\n",
    "\n",
    "ax = kmf4.plot_survival_function(ax=ax,ci_show=True,  show_censors = False)\n",
    "\n",
    "#reg_df5 =  reg_df.loc[(reg_df['Vaccine_status']== 4) & (reg_df['Age group'] ==2)]\n",
    "#kmf5 = KaplanMeierFitter().fit(reg_df5.t, \n",
    "                               #    reg_df5.out, \n",
    "                                  # label='4 dose')\n",
    "\n",
    "#ax = kmf5.plot_survival_function(ax=ax,ci_show=True,  show_censors = True)\n",
    "\n",
    "\n",
    "reg_df6 =  reg_df.loc[(reg_df['Vaccine_status']== 5) &(reg_df['Age group'] ==2)]\n",
    "kmf6 = KaplanMeierFitter().fit(reg_df6.t, \n",
    "                                   reg_df6.out, \n",
    "                                   label='Unknown')\n",
    "\n",
    "ax = kmf6.plot_survival_function(ax=ax,ci_show=True,  show_censors = False)\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlabel('Time (days)', fontweight='bold', fontsize=25)\n",
    "ax.set_ylabel('Cum Survival', fontweight='bold', fontsize=25)\n",
    "ax.legend(prop={'size':16})\n",
    "ax.tick_params(axis='x', labelsize=25)\n",
    "ax.tick_params(axis='y', labelsize=25)\n",
    "ax.set_title('≥65', fontweight='bold', fontsize=30)\n",
    "#from lifelines.plotting import add_at_risk_counts\n",
    "#add_at_risk_counts(kmf1,kmf2, kmf3 ,ax=ax)\n",
    "plt.savefig(f\"D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\layers_3.pdf\", dpi=450, bbox_inches='tight')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2f98a71",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:59:21.492801Z",
     "start_time": "2024-06-17T13:59:21.232274Z"
    }
   },
   "outputs": [],
   "source": [
    "fig,ax = plt.subplots(figsize=(6,6))\n",
    "plt.rc('font', family='Times New Roman', weight='bold', size=30)\n",
    "reg_df1 =  reg_df.loc[(reg_df['Vaccine_status']== 0) & (reg_df.Hospitalization==1)]\n",
    "kmf1 = KaplanMeierFitter().fit(reg_df1.t, \n",
    "                                   reg_df1.out, \n",
    "                                   label='Unvaccination')\n",
    "ax = kmf1.plot_survival_function(ax=ax,ci_show=True,  show_censors = False)\n",
    "\n",
    "\n",
    "reg_df2 =  reg_df.loc[(reg_df['Vaccine_status']== 1) & (reg_df.Hospitalization==1)]\n",
    "kmf2 = KaplanMeierFitter().fit(reg_df2.t, \n",
    "                                  reg_df2.out, \n",
    "                                  label='1 dose')\n",
    "\n",
    "ax = kmf2.plot_survival_function(ax=ax,ci_show=True,  show_censors = False)\n",
    "\n",
    "\n",
    "\n",
    "reg_df3 =  reg_df.loc[(reg_df['Vaccine_status']== 2) & (reg_df.Hospitalization==1)]\n",
    "kmf3 = KaplanMeierFitter().fit(reg_df3.t, \n",
    "                                   reg_df3.out, \n",
    "                                   label='2 dose')\n",
    "\n",
    "ax = kmf3.plot_survival_function(ax=ax,ci_show=True,  show_censors = False)\n",
    "\n",
    "\n",
    "reg_df4 =  reg_df.loc[(reg_df['Vaccine_status']== 3) & (reg_df.Hospitalization==1)]\n",
    "kmf4 = KaplanMeierFitter().fit(reg_df4.t, \n",
    "                                   reg_df4.out, \n",
    "                                  label='3 dose')\n",
    "\n",
    "ax = kmf4.plot_survival_function(ax=ax,ci_show=True,  show_censors = False)\n",
    "\n",
    "reg_df5 =  reg_df.loc[(reg_df['Vaccine_status']== 4) & (reg_df.Hospitalization==1)]\n",
    "kmf5 = KaplanMeierFitter().fit(reg_df5.t, \n",
    "                                  reg_df5.out, \n",
    "                                  label='4 dose')\n",
    "\n",
    "ax = kmf5.plot_survival_function(ax=ax,ci_show=True,  show_censors = False)\n",
    "\n",
    "\n",
    "reg_df6 =  reg_df.loc[(reg_df['Vaccine_status']== 5) &(reg_df.Hospitalization==1)]\n",
    "kmf6 = KaplanMeierFitter().fit(reg_df6.t, \n",
    "                                   reg_df6.out, \n",
    "                                   label='Unknown')\n",
    "\n",
    "ax = kmf6.plot_survival_function(ax=ax,ci_show=True,  show_censors = False)\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlabel('Time (days)', fontweight='bold', fontsize=25)\n",
    "ax.set_ylabel('Cum Survival', fontweight='bold', fontsize=25)\n",
    "ax.legend(prop={'size':16})\n",
    "ax.tick_params(axis='x', labelsize=25)\n",
    "ax.tick_params(axis='y', labelsize=25)\n",
    "ax.set_title('(Hospitalization = Yes)', fontweight='bold', fontsize=30)\n",
    "#from lifelines.plotting import add_at_risk_counts\n",
    "#add_at_risk_counts(kmf1,kmf2, kmf3 ,ax=ax)\n",
    "plt.savefig(f\"D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\layers_4.pdf\", dpi=450, bbox_inches='tight')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cfbce7c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:59:25.119297Z",
     "start_time": "2024-06-17T13:59:24.912158Z"
    }
   },
   "outputs": [],
   "source": [
    "fig,ax = plt.subplots(figsize=(6,6))\n",
    "plt.rc('font', family='Times New Roman', weight='bold', size=30)\n",
    "reg_df1 =  reg_df.loc[(reg_df['Vaccine_status']== 0) & (reg_df['Age group'] == 2)]\n",
    "kmf1 = KaplanMeierFitter().fit(reg_df1.t, \n",
    "                                   reg_df1.out, \n",
    "                                   label='Unvaccinated')\n",
    "ax = kmf1.plot_survival_function(ax=ax,ci_show=True,  show_censors = True)\n",
    "\n",
    "\n",
    "#reg_df2 =  reg_df.loc[(reg_df['Vaccine_status']== 1) & (reg_df['Age group'] == 2)]\n",
    "#kmf2 = KaplanMeierFitter().fit(reg_df2.t, \n",
    "                                   #reg_df2.out, \n",
    "                                 #  label='1 dose')\n",
    "\n",
    "#adx = kmf2.plot_survival_function(ax=ax,ci_show=True,  show_censors = True)\n",
    "\n",
    "\n",
    "reg_df3=  reg_df.loc[(reg_df['Vaccine_status']== 2) & (reg_df['Age group'] == 2)]\n",
    "kmf3 = KaplanMeierFitter().fit(reg_df3.t, \n",
    "                                   reg_df3.out, \n",
    "                                   label='2 dose')\n",
    "\n",
    "ax = kmf3.plot_survival_function(ax=ax,ci_show=True,  show_censors = True)\n",
    "\n",
    "reg_df4=  reg_df.loc[(reg_df['Vaccine_status']== 3) & (reg_df['Age group'] == 2)]\n",
    "kmf4 = KaplanMeierFitter().fit(reg_df4.t, \n",
    "                                   reg_df4.out, \n",
    "                                   label='3 dose')\n",
    "\n",
    "ax = kmf4.plot_survival_function(ax=ax,ci_show=True,  show_censors = True)\n",
    "\n",
    "\n",
    "#reg_df5=  reg_df.loc[(reg_df['Vaccine_status']== 4) & (reg_df['Age group'] == 2)]\n",
    "#kmf5 = KaplanMeierFitter().fit(reg_df5.t, \n",
    "                                  # reg_df5.out, \n",
    "                                #   label='4 dose')\n",
    "\n",
    "#ax = kmf5.plot_survival_function(ax=ax,ci_show=True,  show_censors = True)\n",
    "\n",
    "reg_df6=  reg_df.loc[(reg_df['Vaccine_status']== 5) & (reg_df['Age group'] == 2)]\n",
    "kmf6 = KaplanMeierFitter().fit(reg_df6.t, \n",
    "                                   reg_df6.out, \n",
    "                                   label='Unknown')\n",
    "\n",
    "ax = kmf6.plot_survival_function(ax=ax,ci_show=True,  show_censors = True)\n",
    "\n",
    "ax.set_xlabel('Time (days)', fontweight='bold', fontsize=25)\n",
    "ax.set_ylabel('Cum Survival', fontweight='bold', fontsize=25)\n",
    "ax.legend(loc = 'lower left',prop={'size':18})\n",
    "ax.tick_params(axis='x', labelsize=25)\n",
    "ax.tick_params(axis='y', labelsize=25)\n",
    "ax.set_title('65+', fontweight='bold', fontsize=30)\n",
    " \n",
    "plt.savefig(f\"D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\layers_5.pdf\", dpi=450, bbox_inches='tight')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6929af9d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:59:29.307139Z",
     "start_time": "2024-06-17T13:59:29.047221Z"
    }
   },
   "outputs": [],
   "source": [
    "fig,ax = plt.subplots(figsize=(6,6))\n",
    "plt.rc('font', family='Times New Roman', weight='bold', size=30)\n",
    "reg_df1 =  reg_df.loc[(reg_df['Vaccine_status']== 0) & (reg_df['Hospitalization'] == 1)]\n",
    "kmf1 = KaplanMeierFitter().fit(reg_df1.t, \n",
    "                                   reg_df1.out, \n",
    "                                   label='Unvaccinated')\n",
    "ax = kmf1.plot_survival_function(ax=ax,ci_show=True,  show_censors = True)\n",
    "\n",
    "\n",
    "reg_df2 =  reg_df.loc[(reg_df['Vaccine_status']== 1) & (reg_df['Hospitalization'] == 1)]\n",
    "kmf2 = KaplanMeierFitter().fit(reg_df2.t, \n",
    "                                   reg_df2.out, \n",
    "                                   label='1 dose')\n",
    "\n",
    "ax = kmf2.plot_survival_function(ax=ax,ci_show=True,  show_censors = True)\n",
    "\n",
    "\n",
    "reg_df3=  reg_df.loc[(reg_df['Vaccine_status']== 2) & (reg_df['Hospitalization'] == 1)]\n",
    "kmf3 = KaplanMeierFitter().fit(reg_df3.t, \n",
    "                                   reg_df3.out, \n",
    "                                   label='2 dose')\n",
    "\n",
    "ax = kmf3.plot_survival_function(ax=ax,ci_show=True,  show_censors = True)\n",
    "\n",
    "reg_df4=  reg_df.loc[(reg_df['Vaccine_status']== 3) & (reg_df['Hospitalization'] == 1)]\n",
    "kmf4 = KaplanMeierFitter().fit(reg_df4.t, \n",
    "                                   reg_df4.out, \n",
    "                                   label='3 dose')\n",
    "\n",
    "ax = kmf4.plot_survival_function(ax=ax,ci_show=True,  show_censors = True)\n",
    "\n",
    "\n",
    "reg_df5=  reg_df.loc[(reg_df['Vaccine_status']== 4) & (reg_df['Hospitalization'] == 1)]\n",
    "kmf5 = KaplanMeierFitter().fit(reg_df5.t, \n",
    "                                  reg_df5.out, \n",
    "                                label='4 dose')\n",
    "\n",
    "ax = kmf5.plot_survival_function(ax=ax,ci_show=True,  show_censors = True)\n",
    "\n",
    "reg_df6=  reg_df.loc[(reg_df['Vaccine_status']== 5) & (reg_df['Hospitalization'] == 1)]\n",
    "kmf6 = KaplanMeierFitter().fit(reg_df6.t, \n",
    "                                   reg_df6.out, \n",
    "                                   label='Unknown')\n",
    "\n",
    "ax = kmf6.plot_survival_function(ax=ax,ci_show=True,  show_censors = True)\n",
    "\n",
    "ax.set_xlabel('Time (days)', fontweight='bold', fontsize=25)\n",
    "ax.set_ylabel('Cum Survival', fontweight='bold', fontsize=25)\n",
    "ax.legend(loc = 'lower left',prop={'size':18})\n",
    "ax.tick_params(axis='x', labelsize=25)\n",
    "ax.tick_params(axis='y', labelsize=25)\n",
    "ax.set_title('Hospitalization (Yes)', fontweight='bold', fontsize=30)\n",
    " \n",
    "plt.savefig(f\"D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\layers_6.pdf\", dpi=450, bbox_inches='tight')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "957621c4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-22T15:23:07.759062Z",
     "start_time": "2024-02-22T15:23:07.025070Z"
    }
   },
   "outputs": [],
   "source": [
    "for feature in Nobj_list.drop('Hospitalization'):\n",
    "    for i in reg_df[feature].unique():\n",
    "        df_tmp = reg_df.loc[reg_df[feature] == i]\n",
    "        p_value = multivariate_logrank_test(event_durations = df_tmp.T_ending,\n",
    "                                            groups=df_tmp.Hospitalization,\n",
    "                                            event_observed=df_tmp.RI_status \n",
    "                                           ).p_value\n",
    "        if p_value <= 0.05:\n",
    "            p_value_text = ['p-value < 0.05' if p_value < 0.05 else 'p-value = %.3F'%p_value][0]\n",
    "            print('Hospitalization : {}={}, logrank test {}'.format(feature, i, p_value_text))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "574e2201",
   "metadata": {},
   "source": [
    "### 4.4 3 muti-Cox reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57b2ff3a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:59:48.808714Z",
     "start_time": "2024-06-17T13:59:48.805714Z"
    }
   },
   "outputs": [],
   "source": [
    "from lifelines import CoxPHFitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd567c07",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:59:54.275430Z",
     "start_time": "2024-06-17T13:59:54.261430Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12c92aa9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:59:57.228829Z",
     "start_time": "2024-06-17T13:59:57.215278Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df_selected1 = reg_df[['Sex', 'age', 'Reg_U',  'Workplace','Persistent infection', 'Symptoms',  'vac2',  'Hospitalization', 'Variants', 'out', 't']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73ead648",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T13:59:59.728294Z",
     "start_time": "2024-06-17T13:59:59.710780Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df_select_with_dummies1 = pd.get_dummies(reg_df_selected1, columns=['Sex',  'Workplace', 'Reg_U', 'Persistent infection', 'Symptoms',  'vac2',  'Hospitalization', 'Variants' ],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "849629ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:01.129943Z",
     "start_time": "2024-06-17T14:00:00.849240Z"
    }
   },
   "outputs": [],
   "source": [
    "cox1 = CoxPHFitter()  \n",
    "cox1.fit(reg_df_select_with_dummies1 , duration_col = 't', event_col = 'out')  \n",
    "cox1.print_summary()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19fdaea7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:03.183518Z",
     "start_time": "2024-06-17T14:00:03.173509Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df_selected2 = reg_df[['Sex', 'age', 'Reg_U',  'Persistent infection', 'Symptoms',  'vac2',  'Hospitalization', 'Variants', 'out', 't']]\n",
    "\n",
    "reg_df_select_with_dummies2 = pd.get_dummies(reg_df_selected2, columns= ['Sex','Reg_U',  'Persistent infection', 'Symptoms',  'vac2',  'Hospitalization', 'Variants'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63476543",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:03.455085Z",
     "start_time": "2024-06-17T14:00:03.360569Z"
    }
   },
   "outputs": [],
   "source": [
    "cox2 = CoxPHFitter()  \n",
    "cox2.fit(reg_df_select_with_dummies2 , duration_col = 't', event_col = 'out')  \n",
    "cox2.print_summary()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "481029cc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:03.620609Z",
     "start_time": "2024-06-17T14:00:03.538597Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "reg_df_selected3 = reg_df[['Sex', 'age', 'Reg_U',  'Persistent infection',  'vac2',  'Hospitalization', 'Variants', 'out', 't']]\n",
    "reg_df_select_with_dummies3 = pd.get_dummies(reg_df_selected3, columns= ['Sex',  'Reg_U',  'Persistent infection',  'vac2',  'Hospitalization', 'Variants' ],drop_first=True)\n",
    "\n",
    "cox3 = CoxPHFitter()  \n",
    "cox3.fit(reg_df_select_with_dummies3 , duration_col = 't', event_col = 'out')  \n",
    "cox3.print_summary()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6b0127a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:03.847171Z",
     "start_time": "2024-06-17T14:00:03.770670Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df_selected4 = reg_df[['Sex','Reg_U',  'Persistent infection',  'vac2',  'Hospitalization', 'Variants', 'out', 't']]\n",
    "reg_df_select_with_dummies4 = pd.get_dummies(reg_df_selected4, columns= [  'Sex','Reg_U',  'Persistent infection',  'vac2',  'Hospitalization', 'Variants'],drop_first=True)\n",
    "\n",
    "cox4 = CoxPHFitter()  \n",
    "cox4.fit(reg_df_select_with_dummies4 , duration_col = 't', event_col = 'out')  \n",
    "cox4.print_summary()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83f55519",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:04.601181Z",
     "start_time": "2024-06-17T14:00:04.511666Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df_selected5 = reg_df[['Sex','Reg_U',  'Persistent infection',  'vac2',  'Hospitalization',   'out', 't']]\n",
    "reg_df_select_with_dummies5 = pd.get_dummies(reg_df_selected4, columns= [ 'Sex','Reg_U',  'Persistent infection',  'vac2', ],drop_first=True)\n",
    "\n",
    "cox5 = CoxPHFitter()  \n",
    "cox5.fit(reg_df_select_with_dummies5 , duration_col = 't', event_col = 'out')  \n",
    "cox5.print_summary()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fab41bac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:05.113570Z",
     "start_time": "2024-06-17T14:00:05.055570Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df_selected6 = reg_df[['Sex','Reg_U',  'Persistent infection', 'Hospitalization',   'out', 't']]\n",
    "reg_df_select_with_dummies6 = pd.get_dummies(reg_df_selected6, columns= [ 'Sex','Reg_U',  'Persistent infection', 'Hospitalization',],drop_first=True)\n",
    "\n",
    "cox6 = CoxPHFitter()  \n",
    "cox6.fit(reg_df_select_with_dummies6 , duration_col = 't', event_col = 'out')  \n",
    "cox6.print_summary()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28fd4154",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:05.656722Z",
     "start_time": "2024-06-17T14:00:05.594187Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df_selected7 = reg_df[['Reg_U',  'Persistent infection', 'Hospitalization',   'out', 't']]\n",
    "reg_df_select_with_dummies7 = pd.get_dummies(reg_df_selected7, columns= ['Reg_U',  'Persistent infection', 'Hospitalization', ],drop_first=True)\n",
    "\n",
    "cox7 = CoxPHFitter()  \n",
    "cox7.fit(reg_df_select_with_dummies7 , duration_col = 't', event_col = 'out')  \n",
    "cox7.print_summary()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ff46a93",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:06.244871Z",
     "start_time": "2024-06-17T14:00:06.191354Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "reg_df_selected8 = reg_df[['Reg_U',  'Persistent infection',    'out', 't']]\n",
    "reg_df_select_with_dummies8 = pd.get_dummies(reg_df_selected8, columns= [ 'Reg_U',  'Persistent infection',  ],drop_first=True)\n",
    "\n",
    "cox8 = CoxPHFitter()  \n",
    "cox8.fit(reg_df_select_with_dummies8, duration_col = 't', event_col = 'out')  \n",
    "cox8.print_summary()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8cb6363",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:06.711973Z",
     "start_time": "2024-06-17T14:00:06.654974Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df_selected9 = reg_df[['Persistent infection', 'out', 't']]\n",
    "reg_df_select_with_dummies9 = pd.get_dummies(reg_df_selected9, columns= [ 'Persistent infection'],drop_first=True)\n",
    "\n",
    "cox9 = CoxPHFitter()  \n",
    "cox9.fit(reg_df_select_with_dummies9 , duration_col = 't', event_col = 'out')  \n",
    "cox9.print_summary()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08df012b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:07.390886Z",
     "start_time": "2024-06-17T14:00:07.334886Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_df_selected10 = reg_df[['Reg_U',  'out', 't']]\n",
    "reg_df_select_with_dummies10 = pd.get_dummies(reg_df_selected10, columns= [ 'Reg_U'],drop_first=True)\n",
    "\n",
    "cox10 = CoxPHFitter()  \n",
    "cox10.fit(reg_df_select_with_dummies10 , duration_col = 't', event_col = 'out')  \n",
    "cox10.print_summary()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4ad2b17",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:08.145626Z",
     "start_time": "2024-06-17T14:00:08.127113Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "aic_values_df = pd.DataFrame(columns=['Model', 'AIC'])\n",
    "\n",
    "\n",
    "cox_AICs = [cox1.AIC_partial_, cox2.AIC_partial_, cox3.AIC_partial_, \n",
    "              cox4.AIC_partial_, cox5.AIC_partial_, cox6.AIC_partial_, cox7.AIC_partial_, cox8.AIC_partial_,cox9.AIC_partial_,cox10.AIC_partial_]\n",
    "\n",
    "for i, aic in enumerate(cox_AICs, start=1):\n",
    "    temp_series = pd.Series([f'cox{i}', aic], index=['Model', 'AIC'])\n",
    "    aic_values_df = aic_values_df.append(temp_series, ignore_index=True)\n",
    "\n",
    "\n",
    "aic_values_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7138456",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:09.320534Z",
     "start_time": "2024-06-17T14:00:09.293519Z"
    }
   },
   "outputs": [],
   "source": [
    "aic_values_df.to_excel('D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\Cox回归.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b361d095",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:11.314368Z",
     "start_time": "2024-06-17T14:00:11.309368Z"
    }
   },
   "outputs": [],
   "source": [
    "from lifelines.statistics import proportional_hazard_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "202ff681",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:11.929923Z",
     "start_time": "2024-06-17T14:00:11.910896Z"
    }
   },
   "outputs": [],
   "source": [
    "results = proportional_hazard_test(cox1, reg_df_select_with_dummies1, time_transform='rank')\n",
    "results.print_summary(decimals=3, model=\"untransformed variables\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2674e3d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:14.644204Z",
     "start_time": "2024-06-17T14:00:14.239792Z"
    }
   },
   "outputs": [],
   "source": [
    "cox1.check_assumptions(reg_df_select_with_dummies1, p_value_threshold=0.05, show_plots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77758434",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:14.689545Z",
     "start_time": "2024-06-17T14:00:14.645204Z"
    }
   },
   "outputs": [],
   "source": [
    "cox2.check_assumptions(reg_df_select_with_dummies2, p_value_threshold=0.05, show_plots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22aa1b18",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:14.841250Z",
     "start_time": "2024-06-17T14:00:14.797959Z"
    }
   },
   "outputs": [],
   "source": [
    "cox3.check_assumptions(reg_df_select_with_dummies3, p_value_threshold=0.05, show_plots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d1bf5e3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:15.670787Z",
     "start_time": "2024-06-17T14:00:15.624275Z"
    }
   },
   "outputs": [],
   "source": [
    "cox4.check_assumptions(reg_df_select_with_dummies4, p_value_threshold=0.05, show_plots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2662b0f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:15.867600Z",
     "start_time": "2024-06-17T14:00:15.830345Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cox5.check_assumptions(reg_df_select_with_dummies5, p_value_threshold=0.05, show_plots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dba330fd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:16.049645Z",
     "start_time": "2024-06-17T14:00:16.023133Z"
    }
   },
   "outputs": [],
   "source": [
    "cox6.check_assumptions(reg_df_select_with_dummies6, p_value_threshold=0.05, show_plots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88c6e520",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:16.246655Z",
     "start_time": "2024-06-17T14:00:16.216724Z"
    }
   },
   "outputs": [],
   "source": [
    "cox7.check_assumptions(reg_df_select_with_dummies7, p_value_threshold=0.05, show_plots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "277f8e91",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:16.443778Z",
     "start_time": "2024-06-17T14:00:16.407382Z"
    }
   },
   "outputs": [],
   "source": [
    "cox8.check_assumptions(reg_df_select_with_dummies8, p_value_threshold=0.05, show_plots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2087a4e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:17.051144Z",
     "start_time": "2024-06-17T14:00:17.022632Z"
    }
   },
   "outputs": [],
   "source": [
    "cox9.check_assumptions(reg_df_select_with_dummies9, p_value_threshold=0.05, show_plots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d9dac36",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:17.278779Z",
     "start_time": "2024-06-17T14:00:17.248780Z"
    }
   },
   "outputs": [],
   "source": [
    "cox10.check_assumptions(reg_df_select_with_dummies10, p_value_threshold=0.05, show_plots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2c65552",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:17.851525Z",
     "start_time": "2024-06-17T14:00:17.838017Z"
    }
   },
   "outputs": [],
   "source": [
    "cox3.params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "705ed914",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:19.937246Z",
     "start_time": "2024-06-17T14:00:19.918244Z"
    }
   },
   "outputs": [],
   "source": [
    "summary = cox3.summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e4c164e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:20.532373Z",
     "start_time": "2024-06-17T14:00:20.516370Z"
    }
   },
   "outputs": [],
   "source": [
    "summary['se(coef)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e06762d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:21.259928Z",
     "start_time": "2024-06-17T14:00:21.246928Z"
    }
   },
   "outputs": [],
   "source": [
    "summary.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a051ed68",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:22.796023Z",
     "start_time": "2024-06-17T14:00:22.663699Z"
    }
   },
   "outputs": [],
   "source": [
    "cox_results = pd.DataFrame(columns=['cox', 'variants', 'coef', 'exp(coef)', 'se(coef)',\n",
    "                                    'coef lower 95%', 'coef upper 95%', 'exp(coef) lower 95%',\n",
    "                                    'exp(coef) upper 95%', 'cmp to', 'z', 'p', '-log2(p)', 'aic'])\n",
    "\n",
    "\n",
    "cox_list = [cox1, cox2,cox3, cox4,cox5, cox6,cox7, cox8,   cox9]\n",
    "\n",
    "# 遍历 Cox 回归模型列表\n",
    "for i, cph in enumerate(cox_list):\n",
    "\n",
    "    # 获取 Cox 模型的摘要信息\n",
    "    summary = cph.summary\n",
    "\n",
    "    # 提取系数、标准误差、95% 置信区间和指数系数\n",
    "    coef = summary['coef']\n",
    "    exp_coef = summary['exp(coef)']\n",
    "    se = summary['se(coef)']\n",
    "    ci_low = summary['coef lower 95%']\n",
    "    ci_high = summary['coef upper 95%']\n",
    "    exp_ci_low = summary['exp(coef) lower 95%']\n",
    "    exp_ci_high = summary['exp(coef) upper 95%']\n",
    "    cmp_to = summary['cmp to']\n",
    "    z = summary['z']\n",
    "    p = summary['p']\n",
    "    logp = summary['-log2(p)']\n",
    "    \n",
    "    aic = cph.AIC_partial_\n",
    "\n",
    "    for j in range(len(coef)):\n",
    "        cox_results = cox_results.append({'cox': 'cox{}'.format(i+1),\n",
    "                                          'variants': coef.index[j],\n",
    "                                          'coef': coef[j],\n",
    "                                          'exp(coef)': exp_coef[j],\n",
    "                                          'se(coef)': se[j],\n",
    "                                          'coef lower 95%': ci_low[j],\n",
    "                                          'coef upper 95%': ci_high[j],\n",
    "                                          'exp(coef) lower 95%': exp_ci_low[j],\n",
    "                                          'exp(coef) upper 95%': exp_ci_high[j],\n",
    "                                          'cmp to': cmp_to[j],\n",
    "                                          'z': z[j],\n",
    "                                          'p': p[j],\n",
    "                                          '-log2(p)': logp[j],\n",
    "                                          'aic': aic},\n",
    "                                         ignore_index=True)\n",
    "cox_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f85f2569",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-09T02:32:40.696874Z",
     "start_time": "2024-04-09T02:32:40.664847Z"
    }
   },
   "outputs": [],
   "source": [
    "cox_results.to_excel('D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\cox_result.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "382ddda5",
   "metadata": {},
   "source": [
    "## 5. Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c66db44a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-17T14:00:45.033012Z",
     "start_time": "2024-06-17T14:00:44.892431Z"
    }
   },
   "outputs": [],
   "source": [
    "from pyecharts.render import make_snapshot\n",
    "from snapshot_phantomjs import snapshot\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Map\n",
    "from pyecharts.globals import ThemeType,ChartType, SymbolType, GeoType\n",
    "from pyecharts.charts import Geo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "177a7870",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-20T08:51:32.158958Z",
     "start_time": "2024-03-20T08:51:25.653185Z"
    }
   },
   "outputs": [],
   "source": [
    "c = (\n",
    "    Map(init_opts=opts.InitOpts(width=\"300px\", height=\"300px\", bg_color='white', theme=ThemeType.LIGHT))\n",
    "    .add(\n",
    "        \"\",\n",
    "        [['福建省', 10.0]],\n",
    "        \"china\",\n",
    "        is_map_symbol_show=False,\n",
    "        zoom = 1.1\n",
    "    )\n",
    "    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n",
    "    .set_global_opts(\n",
    "        visualmap_opts=opts.VisualMapOpts(\n",
    "            max_=10,\n",
    "            is_piecewise=False,\n",
    "            range_color=['#8099FF', '#F2BE9E'],\n",
    "            textstyle_opts=opts.TextStyleOpts(font_size=25, font_weight='bold', font_family='Times New Roman'),\n",
    "            is_show=False \n",
    "        ),\n",
    "        #legend_opts=opts.LegendOpts(is_show=False),\n",
    "        #title_opts=opts.TitleOpts(title=\" \", pos_left='center', title_textstyle_opts=opts.TextStyleOpts(font_size=25, font_weight='bold', font_family='Times New Roman')),\n",
    "    )\n",
    "    #.render(\"D:\\\\Research\\\\Project\\\\PROJECTS\\\\其他\\\\Dengue fever\\\\result\\\\zj.html\")\n",
    ")\n",
    "\n",
    "make_snapshot(snapshot, c.render(), \"D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\福建.pdf\", pixel_ratio = 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca221685",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-20T08:34:59.542663Z",
     "start_time": "2024-03-20T08:34:38.384262Z"
    }
   },
   "outputs": [],
   "source": [
    "c = (\n",
    "    Map(init_opts=opts.InitOpts(width=\"300px\", height=\"300px\", bg_color='white', theme=ThemeType.LIGHT))\n",
    "    .add(\n",
    "        \"\",\n",
    "        [['厦门市', 10.0]],\n",
    "        \"福建\",\n",
    "        is_map_symbol_show=False,\n",
    "        zoom = 1.1\n",
    "    )\n",
    "    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n",
    "    .set_global_opts(\n",
    "        visualmap_opts=opts.VisualMapOpts(\n",
    "            max_=10,\n",
    "            is_piecewise=False,\n",
    "            range_color=['#eff6e7', '#8099FF'],\n",
    "            textstyle_opts=opts.TextStyleOpts(font_size=25, font_weight='bold', font_family='Times New Roman'),\n",
    "            is_show=False \n",
    "        ),\n",
    "        #legend_opts=opts.LegendOpts(is_show=False),\n",
    "        #title_opts=opts.TitleOpts(title=\" \", pos_left='center', title_textstyle_opts=opts.TextStyleOpts(font_size=25, font_weight='bold', font_family='Times New Roman')),\n",
    "    )\n",
    "    #.render(\"D:\\\\Research\\\\Project\\\\PROJECTS\\\\其他\\\\Dengue fever\\\\result\\\\zj.html\")\n",
    ")\n",
    "\n",
    "make_snapshot(snapshot, c.render(), \"D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\厦门.pdf\", pixel_ratio = 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31bfc6cd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-20T08:35:15.292075Z",
     "start_time": "2024-03-20T08:35:04.513821Z"
    }
   },
   "outputs": [],
   "source": [
    "c = (\n",
    "    Map(init_opts=opts.InitOpts(width=\"300px\", height=\"300px\", bg_color='white', theme=ThemeType.LIGHT))\n",
    "    .add(\n",
    "        \"\",\n",
    "        [['思明区', 8.0],\n",
    " ['集美区', 8.0],\n",
    " ['湖里区', 8.0],\n",
    " ['同安区', 8.0],\n",
    " ['海沧区', 8.0],\n",
    " ['翔安区', 8.0],],\n",
    "    \"厦门\",\n",
    "    is_map_symbol_show=False,\n",
    "    zoom = 1.1\n",
    "    )\n",
    "    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n",
    "    .set_global_opts(\n",
    "        visualmap_opts=opts.VisualMapOpts(\n",
    "            max_=8,\n",
    "            is_piecewise=False,\n",
    "            range_color=['#eff6e7', '#FFEDA0'],\n",
    "            textstyle_opts=opts.TextStyleOpts(font_size=25, font_weight='bold', font_family='Times New Roman'),\n",
    "            is_show=False \n",
    "        ),\n",
    "        #legend_opts=opts.LegendOpts(is_show=False),\n",
    "        #title_opts=opts.TitleOpts(title=\" \", pos_left='center', title_textstyle_opts=opts.TextStyleOpts(font_size=25, font_weight='bold', font_family='Times New Roman')),\n",
    "    )\n",
    "    #.render(\"D:\\\\Research\\\\Project\\\\PROJECTS\\\\其他\\\\Dengue fever\\\\result\\\\nb.html\")\n",
    ")\n",
    "\n",
    "make_snapshot(snapshot, c.render(), \"D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\厦门1.pdf\", pixel_ratio = 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88a5ae12",
   "metadata": {},
   "source": [
    "## 6. Sup info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d46f1478",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-20T14:06:00.992887Z",
     "start_time": "2024-03-20T14:06:00.897070Z"
    }
   },
   "outputs": [],
   "source": [
    "urb = pd.read_excel(\"D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\sup.xlsx\",sheet_name = '城市化率')\n",
    "popu = pd.read_excel(\"D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\sup.xlsx\",sheet_name = '人口')\n",
    "land = pd.read_excel(\"D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\sup.xlsx\",sheet_name = '土地')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98d4e2c4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-20T14:06:01.070088Z",
     "start_time": "2024-03-20T14:06:01.051005Z"
    }
   },
   "outputs": [],
   "source": [
    "urb['Years'] = pd.to_datetime(urb['Years'])\n",
    "urb = urb.set_index(['Years'])\n",
    "\n",
    "popu['Years'] = pd.to_datetime(popu['Years'])\n",
    "popu = popu.set_index(['Years'])\n",
    "\n",
    "land ['Years'] = pd.to_datetime(land ['Years'])\n",
    "land  = land.set_index(['Years'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3203ff6c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-20T14:06:01.241584Z",
     "start_time": "2024-03-20T14:06:01.234569Z"
    }
   },
   "outputs": [],
   "source": [
    "urb.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4eeb661",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-20T14:06:01.630427Z",
     "start_time": "2024-03-20T14:06:01.626428Z"
    }
   },
   "outputs": [],
   "source": [
    "#y1 = urb['厦门市']\n",
    "y2 = urb[ '　思明区']\n",
    "y3 = urb['　湖里区']\n",
    "y4 = urb['　集美区']\n",
    "y5 = urb['　海沧区']\n",
    "y6 = urb['　同安区']\n",
    "y7 = urb['　翔安区']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf3d8411",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-20T14:06:02.004448Z",
     "start_time": "2024-03-20T14:06:01.991477Z"
    }
   },
   "outputs": [],
   "source": [
    "x2 = popu[ '　思明区']*10000/land[ '　思明区'] \n",
    "x3 = popu['　湖里区']*10000 /land['　湖里区']\n",
    "x4 = popu['　集美区']*10000/land['　集美区']\n",
    "x5 = popu['　海沧区']*10000/land[ '　海沧区'] \n",
    "x6 = popu['　同安区']*10000/land[ '　同安区'] \n",
    "x7 = popu['　翔安区']*10000/land[ '　翔安区'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ededd8e9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-20T14:06:02.542364Z",
     "start_time": "2024-03-20T14:06:02.530353Z"
    }
   },
   "outputs": [],
   "source": [
    "sum_x2_to_x7 = x2 + x3 + x4 + x5 + x6 + x7\n",
    "\n",
    "# 计算构成比\n",
    "x_2 = (x2 / sum_x2_to_x7) * 100\n",
    "x_3 = (x3 / sum_x2_to_x7) * 100\n",
    "x_4 = (x4 / sum_x2_to_x7) * 100\n",
    "x_5 = (x5 / sum_x2_to_x7) * 100\n",
    "x_6 = (x6 / sum_x2_to_x7) * 100\n",
    "x_7 = (x7 / sum_x2_to_x7) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41f1685a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-20T14:27:18.097981Z",
     "start_time": "2024-03-20T14:27:16.703800Z"
    }
   },
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(8, 8))  # 调整figsize以适应你的需要\n",
    "plt.rc('font', family='Times New Roman')\n",
    "plt.rcParams.update({'font.size': 12 })\n",
    "plt.rcParams['font.weight'] = 'bold'\n",
    "# 第一个子图\n",
    "ax1 = axs[0]\n",
    "ax1.plot( y2,  color='#B384BA',label='Siming district',linewidth =4)\n",
    "ax1.plot( y3,  color='#D9C2DD',linestyle='dashed',label='Huli district',linewidth =4)\n",
    "ax1.plot( y4,  color='#468BCA',label='Jimei district',linewidth =4)\n",
    "ax1.plot( y5,  color='#7DD2F6',label='Haicang district',linewidth =4)\n",
    "ax1.plot( y6,  color='#008A45',label='Tongan district',linewidth =4)\n",
    "ax1.plot( y7,  color='#80C5A2',label='Xiangan district',linewidth =4)\n",
    "ax1.set_ylim(-1, 105)\n",
    "#ax1.set_yticks(np.arange(0, 110, 10), fontdict={'fontname': 'Times New Roman', 'fontsize': 20, 'fontweight': 'bold'})\n",
    "#ax1.set_xticks(fontdict={'fontname': 'Times New Roman', 'fontsize': 20, 'fontweight': 'bold'})\n",
    "\n",
    "ax1.set_ylabel('Urbanization rate (%)', fontdict={'fontname': 'Times New Roman', 'fontsize': 20, 'fontweight': 'bold'})\n",
    "ax1.set_xlim(np.datetime64('2010-01-01'), np.datetime64('2024-01-01'))\n",
    "ax1.set_xlabel('Year', fontdict={'fontname': 'Times New Roman', 'fontsize': 20, 'fontweight': 'bold'})\n",
    "ax1.text(-0.02, 1.08, '(A)', fontdict={'fontname': 'Times New Roman', 'fontsize': 20, 'fontweight': 'bold'},\n",
    "         transform=ax1.transAxes, verticalalignment='top', horizontalalignment='left')\n",
    "# 第二个子图\n",
    "ax2 = axs[1]\n",
    "ax2.bar(x_2.index, x_2, color='#B384BA',label='Siming district',width=250)\n",
    "ax2.bar(x_3.index, x_3, bottom=x_2, color='#D9C2DD',label='Huli district',width=250)\n",
    "ax2.bar(x_4.index, x_4, bottom=x_2 + x_3, color='#468BCA',label='Jimei district',width=250)\n",
    "ax2.bar(x_5.index, x_5, bottom=x_2 + x_3 + x_4, color='#7DD2F6',label='Tongan district', width=250)\n",
    "ax2.bar(x_6.index, x_6, bottom=x_2 + x_3 + x_4 + x_5, color='#008A45',label='Tongan district',width=250)\n",
    "ax2.bar(x_7.index, x_7, bottom=x_2 + x_3 + x_4 + x_5 + x_6, color='#80C5A2',label='Xiangan district',width=250)\n",
    "ax2.set_ylim(-1, 105)\n",
    "ax2.set_ylabel('Proportioan of population density (%)', fontdict={'fontname': 'Times New Roman', 'fontsize': 20, 'fontweight': 'bold'})\n",
    "ax2.set_xlabel('Year', fontdict={'fontname': 'Times New Roman', 'fontsize': 20, 'fontweight': 'bold'})\n",
    "ax2.set_yticks(np.arange(0, 110, 10), fontdict={'fontname': 'Times New Roman', 'fontsize': 20, 'fontweight': 'bold'})\n",
    "ax2.set_xlim(np.datetime64('2010-01-01'), np.datetime64('2024-01-01'))\n",
    "ax2.text(-0.02, 1.08, '(B)', fontdict={'fontname': 'Times New Roman', 'fontsize': 20, 'fontweight': 'bold'},\n",
    "         transform=ax2.transAxes, verticalalignment='top', horizontalalignment='left')\n",
    "\n",
    "\n",
    "# 合并图例\n",
    "lines1, labels1 = ax1.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "\n",
    "fig.legend(loc='lower center', bbox_to_anchor=(0.5, -0.07),ncol = 4)\n",
    "plt.subplots_adjust(wspace=0.5)\n",
    "# 保存图像\n",
    "plt.savefig(\"D:\\\\Research\\\\Project\\\\PROJECTS\\\\COVID-19\\\\COVID-19_Amoy\\\\Reinfection\\\\New\\\\城市化.png\", dpi=450, bbox_inches='tight')\n",
    "plt.show()"
   ]
  }
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