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README.md

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 ![build](https://github.com/bolliger32/gpl-covid/workflows/CI/badge.svg)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3832226.svg)](https://doi.org/10.5281/zenodo.3832226)
 # The Effect of Large-Scale Anti-Contagion Policies on the COVID-19 Pandemic
 
 This repository contains code and data necessary to replicate the findings of [our paper](https://www.medrxiv.org/content/10.1101/2020.03.22.20040642v3).
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 The easiest way to interact with our code and data is via our CodeOcean capsule, because all of the relevant setup described below has been done for you. You may replicate the full analysis through the "Reproducible Run" feature or interact directly with our code through Jupyter Notebooks that run Python, R, and Stata. You may also utilize RStudio. If you wish to use the command line on a cloud workstation, you will want to activate our conda environment with `conda activate gpl-covid`.
 
 ### Github Repository
-You may also view and download source code from our [Github Repository](https://github.com/bolliger32/gpl-covid). "v0.4.4" is the tag that is associated with the current version of the manuscript (as of 05/18/2020), but you may also view the latest codebase and datasets on the master branch. To run this code, you will first want to create and activate our [conda](https://docs.conda.io/projects/conda/en/latest/index.html) environment.
+You may also view and download source code from our [Github Repository](https://github.com/bolliger32/gpl-covid). "v0.4.5" is the tag that is associated with the current version of the manuscript (as of 05/18/2020), but you may also view the latest codebase and datasets on the master branch. To run this code, you will first want to create and activate our [conda](https://docs.conda.io/projects/conda/en/latest/index.html) environment.
 
 ```bash
 conda env create -f environment/environment.yml

metadata/metadata.yml

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-name: The Effect of Large-Scale Anti-Contagion Policies on the Coronavirus (COVID-19) Pandemic
+name: The Effect of Large-Scale Anti-Contagion Policies on the COVID-19 Pandemic
 description: Governments around the world are responding to the novel coronavirus (COVID-19) pandemic with unprecedented policies designed to slow the growth rate of infections. Many actions, such as closing schools and restricting populations to their homes, impose large and visible costs on society, but their benefits cannot be directly observed and are currently understood only through process-based simulations. Here, we compile new data on 1,717 local, regional, and national non-pharmaceutical interventions deployed in the ongoing pandemic across localities in China, South Korea, Italy, Iran, France, and the United States (US). We then apply reduced-form econometric methods, commonly used to measure the effect of policies on economic growth, to empirically evaluate the effect that these anti-contagion policies have had on the growth rate of infections. In the absence of policy actions, we estimate that early infections of COVID-19 exhibit exponential growth rates of roughly 38% per day. We find that anti-contagion policies have significantly and substantially slowed this growth. Some policies have different impacts on different populations, but we obtain consistent evidence that the policy packages now deployed are achieving large, beneficial, and measurable health outcomes. We estimate that across these six countries, interventions prevented or delayed on the order of 62 million confirmed cases, corresponding to averting roughly 530 million total infections. These findings may help inform whether or when these policies should be deployed, intensified, or lifted, and they can support decision-making in the other 180+ countries where COVID-19 has been reported.
 tags:
 - COVID-19