README.Rmd

---
title: "README"
author: "Robert Kubinec"
date: "3/28/2020"
output: github_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## About This Repository

This repository contains data and code to fit the model described in "A Retrospective Bayesian Model for Measuring Covariate Effects on Observed COVID-19 Test and Case Counts", [link here](https://osf.io/preprints/socarxiv/jp4wk). The following is a brief list of the files in the repo relevant to the paper. There are also other files in the repo related to the collection of government policy data.

 1. **corona_tscs_betab.stan**: This Stan model contains a partially-identified model of COVID-19 that permits relative distinctions to be made between areas/countries/states' infection rates. The parameter `num_infected_high` indexes the infection rate by time point and country. As the latent process is on the logit scale, it must be converted via the inverse-logit function to a proportion. However, the resulting estimate should not be interpreted as the total infected in a country, but rather a relative ranking of which countries/areas are the most infected up to the current time point.
 
 2. **corona_tscs_betab_scale.stan**: This Stan model extends the partially-identified model with the 10% lower threshold for tests to infections ratio described in the paper. This model will produce an estimate for `num_infected_high` that when converted with the inverse-logit function will represent the proportion infected in a country conditional on the model's prior concerning the tests to infections ratio.
 
 3. **kubinec_model_preprint.Rmd**: A copy of the paper draft with embedded R code. Because the fitted model objects are too large to put on Github, all Stan models must be re-fit to compile the paper. The process will take approximately 2 hours. 
 
 4. **kubinec_model_SI.Rmd**: This file contains an Rmarkdown file with embedded R code showing how to simulate the model. It is the supplementary information for the paper. See the compiled .pdf version as well.
 
 4. **data**: The data folder contains CSVs of tests and cases for US states that were used to fit the models in the paper. 
 
 5. **BibTexDatabase.bib**: This file contains the Bibtex bibliography for the paper.