lombardia-curva-coronavirus.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quando finirà il Coronavirus🦠?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Analisi dell'andamento** del **Coronavirus** in Lombardia🌍 \n",
    "\n",
    "I dati🔢 provengono dalla repository ufficiale della Protezione Civile italiana e vengono aggiornati ogni giorno. https://github.com/pcm-dpc/COVID-19\n",
    "\n",
    "Costruito con Python e Jupyter Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import timedelta\n",
    "from datetime import datetime\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy.optimize import fsolve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recupero degli ultimi dati"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_json(\n",
    "    \"https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-json/dpc-covid19-ita-regioni.json\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>stato</th>\n",
       "      <th>codice_regione</th>\n",
       "      <th>denominazione_regione</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>ricoverati_con_sintomi</th>\n",
       "      <th>terapia_intensiva</th>\n",
       "      <th>totale_ospedalizzati</th>\n",
       "      <th>isolamento_domiciliare</th>\n",
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       "      <th>variazione_totale_positivi</th>\n",
       "      <th>nuovi_positivi</th>\n",
       "      <th>dimessi_guariti</th>\n",
       "      <th>deceduti</th>\n",
       "      <th>totale_casi</th>\n",
       "      <th>tamponi</th>\n",
       "      <th>casi_testati</th>\n",
       "      <th>note_it</th>\n",
       "      <th>note_en</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-24T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>3</td>\n",
       "      <td>Lombardia</td>\n",
       "      <td>45.466794</td>\n",
       "      <td>9.190347</td>\n",
       "      <td>76</td>\n",
       "      <td>19</td>\n",
       "      <td>95</td>\n",
       "      <td>71</td>\n",
       "      <td>166</td>\n",
       "      <td>0</td>\n",
       "      <td>166</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>172</td>\n",
       "      <td>1463</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-25T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>3</td>\n",
       "      <td>Lombardia</td>\n",
       "      <td>45.466794</td>\n",
       "      <td>9.190347</td>\n",
       "      <td>79</td>\n",
       "      <td>25</td>\n",
       "      <td>104</td>\n",
       "      <td>127</td>\n",
       "      <td>231</td>\n",
       "      <td>65</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>240</td>\n",
       "      <td>3700</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-26T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>3</td>\n",
       "      <td>Lombardia</td>\n",
       "      <td>45.466794</td>\n",
       "      <td>9.190347</td>\n",
       "      <td>79</td>\n",
       "      <td>25</td>\n",
       "      <td>104</td>\n",
       "      <td>145</td>\n",
       "      <td>249</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>258</td>\n",
       "      <td>3208</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-27T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>3</td>\n",
       "      <td>Lombardia</td>\n",
       "      <td>45.466794</td>\n",
       "      <td>9.190347</td>\n",
       "      <td>172</td>\n",
       "      <td>41</td>\n",
       "      <td>213</td>\n",
       "      <td>136</td>\n",
       "      <td>349</td>\n",
       "      <td>100</td>\n",
       "      <td>145</td>\n",
       "      <td>40</td>\n",
       "      <td>14</td>\n",
       "      <td>403</td>\n",
       "      <td>3320</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-28T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>3</td>\n",
       "      <td>Lombardia</td>\n",
       "      <td>45.466794</td>\n",
       "      <td>9.190347</td>\n",
       "      <td>235</td>\n",
       "      <td>47</td>\n",
       "      <td>282</td>\n",
       "      <td>192</td>\n",
       "      <td>474</td>\n",
       "      <td>125</td>\n",
       "      <td>128</td>\n",
       "      <td>40</td>\n",
       "      <td>17</td>\n",
       "      <td>531</td>\n",
       "      <td>4835</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>2020-05-13T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>3</td>\n",
       "      <td>Lombardia</td>\n",
       "      <td>45.466794</td>\n",
       "      <td>9.190347</td>\n",
       "      <td>5007</td>\n",
       "      <td>307</td>\n",
       "      <td>5314</td>\n",
       "      <td>24718</td>\n",
       "      <td>30032</td>\n",
       "      <td>-643</td>\n",
       "      <td>394</td>\n",
       "      <td>38081</td>\n",
       "      <td>15185</td>\n",
       "      <td>83298</td>\n",
       "      <td>524163</td>\n",
       "      <td>310896.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2020-05-14T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>3</td>\n",
       "      <td>Lombardia</td>\n",
       "      <td>45.466794</td>\n",
       "      <td>9.190347</td>\n",
       "      <td>4818</td>\n",
       "      <td>297</td>\n",
       "      <td>5115</td>\n",
       "      <td>24841</td>\n",
       "      <td>29956</td>\n",
       "      <td>-76</td>\n",
       "      <td>522</td>\n",
       "      <td>38568</td>\n",
       "      <td>15296</td>\n",
       "      <td>83820</td>\n",
       "      <td>538243</td>\n",
       "      <td>318289.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2020-05-15T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>3</td>\n",
       "      <td>Lombardia</td>\n",
       "      <td>45.466794</td>\n",
       "      <td>9.190347</td>\n",
       "      <td>4705</td>\n",
       "      <td>276</td>\n",
       "      <td>4981</td>\n",
       "      <td>22765</td>\n",
       "      <td>27746</td>\n",
       "      <td>-2210</td>\n",
       "      <td>299</td>\n",
       "      <td>40962</td>\n",
       "      <td>15411</td>\n",
       "      <td>84119</td>\n",
       "      <td>550405</td>\n",
       "      <td>325071.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2020-05-16T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>3</td>\n",
       "      <td>Lombardia</td>\n",
       "      <td>45.466794</td>\n",
       "      <td>9.190347</td>\n",
       "      <td>4521</td>\n",
       "      <td>268</td>\n",
       "      <td>4789</td>\n",
       "      <td>22890</td>\n",
       "      <td>27679</td>\n",
       "      <td>-67</td>\n",
       "      <td>399</td>\n",
       "      <td>41389</td>\n",
       "      <td>15450</td>\n",
       "      <td>84518</td>\n",
       "      <td>564550</td>\n",
       "      <td>333243.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>2020-05-17T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>3</td>\n",
       "      <td>Lombardia</td>\n",
       "      <td>45.466794</td>\n",
       "      <td>9.190347</td>\n",
       "      <td>4480</td>\n",
       "      <td>255</td>\n",
       "      <td>4735</td>\n",
       "      <td>22695</td>\n",
       "      <td>27430</td>\n",
       "      <td>-249</td>\n",
       "      <td>326</td>\n",
       "      <td>41895</td>\n",
       "      <td>15519</td>\n",
       "      <td>84844</td>\n",
       "      <td>576359</td>\n",
       "      <td>340164.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 20 columns</p>\n",
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      ],
      "text/plain": [
       "                   data stato  codice_regione denominazione_regione  \\\n",
       "0   2020-02-24T18:00:00   ITA               3             Lombardia   \n",
       "1   2020-02-25T18:00:00   ITA               3             Lombardia   \n",
       "2   2020-02-26T18:00:00   ITA               3             Lombardia   \n",
       "3   2020-02-27T18:00:00   ITA               3             Lombardia   \n",
       "4   2020-02-28T18:00:00   ITA               3             Lombardia   \n",
       "..                  ...   ...             ...                   ...   \n",
       "79  2020-05-13T17:00:00   ITA               3             Lombardia   \n",
       "80  2020-05-14T17:00:00   ITA               3             Lombardia   \n",
       "81  2020-05-15T17:00:00   ITA               3             Lombardia   \n",
       "82  2020-05-16T17:00:00   ITA               3             Lombardia   \n",
       "83  2020-05-17T17:00:00   ITA               3             Lombardia   \n",
       "\n",
       "          lat      long  ricoverati_con_sintomi  terapia_intensiva  \\\n",
       "0   45.466794  9.190347                      76                 19   \n",
       "1   45.466794  9.190347                      79                 25   \n",
       "2   45.466794  9.190347                      79                 25   \n",
       "3   45.466794  9.190347                     172                 41   \n",
       "4   45.466794  9.190347                     235                 47   \n",
       "..        ...       ...                     ...                ...   \n",
       "79  45.466794  9.190347                    5007                307   \n",
       "80  45.466794  9.190347                    4818                297   \n",
       "81  45.466794  9.190347                    4705                276   \n",
       "82  45.466794  9.190347                    4521                268   \n",
       "83  45.466794  9.190347                    4480                255   \n",
       "\n",
       "    totale_ospedalizzati  isolamento_domiciliare  totale_positivi  \\\n",
       "0                     95                      71              166   \n",
       "1                    104                     127              231   \n",
       "2                    104                     145              249   \n",
       "3                    213                     136              349   \n",
       "4                    282                     192              474   \n",
       "..                   ...                     ...              ...   \n",
       "79                  5314                   24718            30032   \n",
       "80                  5115                   24841            29956   \n",
       "81                  4981                   22765            27746   \n",
       "82                  4789                   22890            27679   \n",
       "83                  4735                   22695            27430   \n",
       "\n",
       "    variazione_totale_positivi  nuovi_positivi  dimessi_guariti  deceduti  \\\n",
       "0                            0             166                0         6   \n",
       "1                           65              68                0         9   \n",
       "2                           18              18                0         9   \n",
       "3                          100             145               40        14   \n",
       "4                          125             128               40        17   \n",
       "..                         ...             ...              ...       ...   \n",
       "79                        -643             394            38081     15185   \n",
       "80                         -76             522            38568     15296   \n",
       "81                       -2210             299            40962     15411   \n",
       "82                         -67             399            41389     15450   \n",
       "83                        -249             326            41895     15519   \n",
       "\n",
       "    totale_casi  tamponi  casi_testati note_it note_en  \n",
       "0           172     1463           NaN                  \n",
       "1           240     3700           NaN                  \n",
       "2           258     3208           NaN                  \n",
       "3           403     3320           NaN                  \n",
       "4           531     4835           NaN                  \n",
       "..          ...      ...           ...     ...     ...  \n",
       "79        83298   524163      310896.0                  \n",
       "80        83820   538243      318289.0                  \n",
       "81        84119   550405      325071.0                  \n",
       "82        84518   564550      333243.0                  \n",
       "83        84844   576359      340164.0                  \n",
       "\n",
       "[84 rows x 20 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[data.denominazione_regione == \"Lombardia\"]\n",
    "data = data.reset_index(drop=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dati degli ultimi sette giorni📅\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>totale_casi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>131</td>\n",
       "      <td>81871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>132</td>\n",
       "      <td>82904</td>\n",
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       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>133</td>\n",
       "      <td>83298</td>\n",
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       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>134</td>\n",
       "      <td>83820</td>\n",
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       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>135</td>\n",
       "      <td>84119</td>\n",
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       "    <tr>\n",
       "      <th>82</th>\n",
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       "      <th>83</th>\n",
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      ],
      "text/plain": [
       "    data  totale_casi\n",
       "77   131        81871\n",
       "78   132        82904\n",
       "79   133        83298\n",
       "80   134        83820\n",
       "81   135        84119\n",
       "82   136        84518\n",
       "83   137        84844"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "data = data.loc[:, [\"data\", \"totale_casi\"]]\n",
    "FMT = \"%Y-%m-%dT%H:%M:%S\"\n",
    "data[\"data\"] = data[\"data\"].map(\n",
    "    lambda x: (\n",
    "        datetime.strptime(x, FMT) - datetime.strptime(\"2020-01-01T00:00:00\", FMT)\n",
    "    ).days\n",
    ")\n",
    "print(\"Dati degli ultimi sette giorni📅\")\n",
    "data[-7:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calcolo funzione logistica"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### La funzione utilizzata è una funzione sigmoidale\n",
    "$$f(x; a, b, c) = \\frac{c}{1 + e ^ -\\dfrac{x - b}{a}}$$\n",
    "\n",
    "**Vengono passati tre parametri:**\n",
    "- **a**: Velocità dell'infezione (inizialmente 1)\n",
    "- **b**: Il giorno del massimo delle infezioni\n",
    "- **c**: Il numero totale di persone infette\n",
    "\n",
    "La funzione è ricorsiva, ovvero richiama se stessa all'infinito fino a quando non si raggiunge un valore minimo -nel nostro caso 0, che corrisponde al totale appiattimento della curva."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "def logistic_model(x, a, b, c):\n",
    "    return c / (1 + np.exp(-(x - b) / a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = list(data.iloc[:, 0])\n",
    "y = list(data.iloc[:, 1])\n",
    "fit = curve_fit(logistic_model, x, y, p0=[1, max(x), max(y)])\n",
    "\n",
    "speed, x_peak, y_max = fit[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trovo il picco"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dalla funzione logistica sono stati ricavati tre vettori che rappresentano l'andamento della curva giorno per giorno: \n",
    "- Velocità di crescita (speed)\n",
    "- Punto di massimo sull'asse X (x_peak)\n",
    "- Punto di massimo sull'asse Y (y_peak)\n",
    "\n",
    "Con questi valori è possibile calcolare il punto della fine dell'epidemia. Per farlo viene inserito il punto x nella funzione e si ricava y. \n",
    "Per farlo si utilizza la funzione **fsolve** (trova le radici di una funzione non lineare) la quale prende come parametri la funzione (nel nostro caso la curva) e un punto di massimo sull'asse x. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "224"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "end = int(\n",
    "    fsolve(lambda x: logistic_model(x, speed, x_peak, y_max) - int(y_max), x_peak)\n",
    ")\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grafico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_x = list(range(max(x), end))\n",
    "\n",
    "def plot_curve(x_axis, logistic_data):\n",
    "    plt.rcParams[\"figure.figsize\"] = [7, 7]\n",
    "    plt.rc(\"font\", size=14)\n",
    "    # Real data\n",
    "    plt.scatter(x, y, label=\"Dati reali\", color=\"red\")\n",
    "    # Predicted logistic curve\n",
    "    plt.plot(\n",
    "        x_axis,\n",
    "        logistic_data,\n",
    "        linewidth=3,\n",
    "        label=\"Funzione logistica\",\n",
    "    )\n",
    "    plt.axhline(\n",
    "        y=max(logistic_data),\n",
    "        color=\"blue\",\n",
    "        linestyle=\"dashed\",\n",
    "        alpha=0.7,\n",
    "        linewidth=1.5,\n",
    "        label=\"Limite naturale\",\n",
    "    )\n",
    "\n",
    "    plt.legend()\n",
    "    plt.xlabel(\"Giorni dal 1 Gennaio 2020\")\n",
    "    plt.ylabel(\"Numero totale infetti\")\n",
    "    plt.title(\"Curva logistica\")\n",
    "    plt.ylim((min(y) * 0.9, y_max * 1.1))\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_curve(x + pred_x, [logistic_model(i, fit[0][0], fit[0][1], fit[0][2]) for i in x + pred_x])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "È possibile osservare che la curva è formata da due rami: uno esponenziale (parte bassa) e uno logaritmico (parte alta). \n",
    "Il cambio di concavità avviene in centro nel punto di flesso.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tasso di crescita"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calcolo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>totale_casi</th>\n",
       "      <th>differenza_giorno_precedente</th>\n",
       "      <th>tasso_crescita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>131</td>\n",
       "      <td>81871</td>\n",
       "      <td>364.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>132</td>\n",
       "      <td>82904</td>\n",
       "      <td>1033.0</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>133</td>\n",
       "      <td>83298</td>\n",
       "      <td>394.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>134</td>\n",
       "      <td>83820</td>\n",
       "      <td>522.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>135</td>\n",
       "      <td>84119</td>\n",
       "      <td>299.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>136</td>\n",
       "      <td>84518</td>\n",
       "      <td>399.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>137</td>\n",
       "      <td>84844</td>\n",
       "      <td>326.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    data  totale_casi  differenza_giorno_precedente  tasso_crescita\n",
       "77   131        81871                         364.0             0.4\n",
       "78   132        82904                        1033.0             1.3\n",
       "79   133        83298                         394.0             0.5\n",
       "80   134        83820                         522.0             0.6\n",
       "81   135        84119                         299.0             0.4\n",
       "82   136        84518                         399.0             0.5\n",
       "83   137        84844                         326.0             0.4"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"differenza_giorno_precedente\"] = data.diff()[\"totale_casi\"]\n",
    "data[\"tasso_crescita\"] = round(\n",
    "    data[\"differenza_giorno_precedente\"]\n",
    "    / (data[\"totale_casi\"] - data[\"differenza_giorno_precedente\"])\n",
    "    * 100,\n",
    "    1,\n",
    ")\n",
    "data[\"tasso_crescita\"].fillna(0)\n",
    "data[\"data\"].fillna(0)\n",
    "data[-7:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggiornamento dataframe con i dati ottenuti dalla funzione logistica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {\n",
    "    \"data\": x + pred_x,\n",
    "    \"totale_casi\": [\n",
    "        logistic_model(i, fit[0][0], fit[0][1], fit[0][2]) for i in x + pred_x\n",
    "    ],\n",
    "}\n",
    "df = pd.DataFrame(data=d)\n",
    "df[\"differenza_giorno_precedente\"] = df.diff()[\"totale_casi\"]\n",
    "df[\"tasso_crescita\"] = round(\n",
    "    df[\"differenza_giorno_precedente\"]\n",
    "    / (df[\"totale_casi\"] - df[\"differenza_giorno_precedente\"])\n",
    "    * 100,\n",
    "    1,\n",
    ")\n",
    "merge = pd.concat([data, df[df.data > max(data[\"data\"])]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_day(day):\n",
    "    return datetime.strptime(\"2020-1-1\", \"%Y-%m-%d\") + timedelta(days=int(round(day)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grafico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def infection_grow(x, y):\n",
    "    plt.rc(\"font\", size=14)\n",
    "    plt.figure(figsize=(12, 5))\n",
    "    plt_axes = plt.gca()\n",
    "    plt_axes.grid(axis=\"y\", color=(0.4, 0.4, 0.4), alpha=0.2)\n",
    "    plt.stackplot(x, y, color=(0.92, 0.26, 0.21, 0.3), linewidth=0)\n",
    "    plt.plot(x, y, color=(0.92, 0.26, 0.21), linewidth=2)\n",
    "    plt.scatter(x, y, color=(0.92, 0.26, 0.21), label=\"Tasso di crescita %\", linewidth=3)\n",
    "    plt.xlim(int(min(x)), int(max(x) + 5))\n",
    "    plt.ylim(int(min(y)), int(max(y)) + 5)\n",
    "    plt.title(\n",
    "        \"Percentuale di crescita giornaliera in Lombardia da \"\n",
    "        + str(get_day(min(x)).date())\n",
    "        + \" a \"\n",
    "        + str(get_day(max(x)).date())\n",
    "    )\n",
    "    plt.xlabel(\"Giorni dall'1-1-2020\")\n",
    "    plt.ylabel(\"% Crescita\")\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "infection_grow(\n",
    "    merge[merge.tasso_crescita > 0][\"data\"].values.tolist(),\n",
    "    merge[merge.tasso_crescita > 0][\"tasso_crescita\"].values.tolist()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quando raggiungeremo la fine?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L'epidemia finirà in data 12 agosto 2020\n",
      "Il numero dei contagiati sarà di 81662\n",
      "Il picco è stato il 31 marzo 2020\n"
     ]
    }
   ],
   "source": [
    "end_date = datetime.strptime(\"2020-1-1\", \"%Y-%m-%d\") + timedelta(days=end)\n",
    "months = [\n",
    "    \"gennaio\",\n",
    "    \"febbraio\",\n",
    "    \"marzo\",\n",
    "    \"aprile\",\n",
    "    \"maggio\",\n",
    "    \"giugno\",\n",
    "    \"luglio\",\n",
    "    \"agosto\",\n",
    "    \"settembre\",\n",
    "    \"ottobre\",\n",
    "    \"novembre\",\n",
    "    \"dicembre\",\n",
    "]\n",
    "print(\n",
    "    \"L'epidemia finirà in data \"\n",
    "    + str(end_date.day)\n",
    "    + \" \"\n",
    "    + months[end_date.month - 1]\n",
    "    + \" \"\n",
    "    + str(end_date.year)\n",
    ")\n",
    "print(\"Il numero dei contagiati sarà di \" + str(int(round(y_max))))\n",
    "\n",
    "print(\n",
    "    \"Il picco è stato il \"\n",
    "    + str(get_day(x_peak).day)\n",
    "    + \" \"\n",
    "    + months[get_day(x_peak).month - 1]\n",
    "    + \" \"\n",
    "    + str(get_day(x_peak).year)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Osservazioni\n",
    "La curva non rappresenta fedelmente la realtà a causa delle caratteristiche della malattia, che la rendono difficilmente individuabile prima della comparsa dei primi sintomi: molte persone sono portatori asintomatici.   \n",
    "A ciò, si aggiunge l'impossibilità di fare un censimento dei contagi su tutta la popolazione, perché cambiando il criterio tutti i dati verrebbero alterati.\n",
    "\n",
    "Altro problema che rende la curva distaccata dalla realtà, sono i dati dei primi giorni. Il primo caso è stato registrato a Codogno, ma la malattia potrebbe essere stata presente in altre zone qualche settimana prima, e ciò spiegherebbe la veloce diffusione nella prima fase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "infection_grow(\n",
    "    data[1:7][\"data\"].values.tolist(),\n",
    "    data[1:7][\"tasso_crescita\"].values.tolist(),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Report Aprile\n",
    "Osservando i dati degli ultimi giorni sembra che ci stiamo avvicinando all'asintoto della resistenza ambientale. Tuttavia non significa che siamo alla fine dell'isolamento, in quanto se le persone tornassero a circolare improvvisamente potrebbe esserci una ripresa dell'epidemia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "infection_grow(\n",
    "    data[37:67][\"data\"].values.tolist(),\n",
    "    data[37:67][\"tasso_crescita\"].values.tolist(),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Report da 4 a 17 Maggio\n",
    "Di recente è iniziata la fase 2, dove le misure del lockdown sono state allentate. Neglle ultime settimane il tasso di crescita è rimasto sotto l'1%.\n",
    "Ciò è un buon segnale, ma dovremo aspettare ancora questa settimana per confermare l'andamento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "infection_grow(\n",
    "    data[-7:84][\"data\"].values.tolist(),\n",
    "    data[-7:84][\"tasso_crescita\"].values.tolist(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}