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<h1>Optimal, near-optimal, and robust epidemic control</h1> <p><a href="https://dylanhmorris.com" rel="nofollow">Dylan H. Morris</a>(1*), <a href="https://scholar.princeton.edu/ctarnita/people/fernando-rossine" rel="nofollow">Fernando W. Rossine</a>(1*), <a href="https://www.bio.upenn.edu/people/joshua-plotkin" rel="nofollow">Joshua B. Plotkin</a>(2), and <a href="https://slevin.princeton.edu/" rel="nofollow">Simon A. Levin</a>(1).</p> <p>* These authors contributed equally</p> <ol> <li><a href="http://eeb.princeton.edu/" rel="nofollow">Dept. of Ecology and Evolutionary Biology</a>, Princeton University, Princeton, NJ, USA</li> <li><a href="https://www.bio.upenn.edu/" rel="nofollow">Dept. of Biology</a> \& <a href="https://www.math.upenn.edu/" rel="nofollow">Dept. of Mathematics</a>, The University of Pennsylvania, Philadelphia, PA, USA</li> </ol> <h2>Repository information</h2> <p>This repository accompanies the article "Optimal, near-optimal, and robust epidemic control" (Morris, Rossine et al.). It provides code for reproducing numerical solutions of equations in the paper and for recreating associated display figures.</p> <h2>License and citation information</h2> <p>If you use the code or data provided here, please make sure to do so in light of the project <a href="LICENSE.txt" rel="nofollow">license</a> and please cite our work as below:</p> <ul> <li>D.H. Morris, F.W. Rossine et al. Optimal, near-optimal, and robust epidemic control. <em>OSF preprint</em>. Mar 2020. DOI:<a href="https://doi.org/10.31219/osf.io/9gr7q" rel="nofollow">10.31219/osf.io/9gr7q</a></li> </ul> <p>Bibtex record:</p> <pre class="highlight"><code>@article{morris2020optimal, Author = {Morris, Dylan H. and Rossine, Fernando W. and Plotkin, Joshua B. and Levin, Simon A.}, Title = {Optimal, near-optimal, and robust epidemic control}, Date = {2020}, journal = {arXiv and OSF preprint}, doi = {10.31219/<a href="http://osf.io/9gr7q" rel="nofollow">osf.io/9gr7q</a>}, url = {<a href="https://arxiv.org/abs/2004.02209" rel="nofollow">https://arxiv.org/abs/2004.02209</a>} }</code></pre> <h2>Article abstract</h2> <p>The COVID-19 pandemic has highlighted the importance of epidemic peak reduction (``flattening the curve''). Here we find the optimal time-limited intervention for reducing peak epidemic prevalence in the standard Susceptible-Infectious-Recovered (SIR) model. We show that coarser, more realistic interventions can emulate and perform nearly as well as the provably optimal strategy. We then show that none of these strategies are robust to timing errors. Sustained control measures, though less efficient than optimal and near-optimal time-limited interventions, are needed to mitigate the catastrophic risks of mistiming.</p> <h2>Directories</h2> <ul> <li><code>src</code>: all code, including numerics and figure generation:</li> <li><code>out</code>: output files<ul> <li><code>out/results</code>: numerical analysis outputs as comma-separated values (<code>.csv</code>) files. </li> <li><code>out/figures</code>: figures generated from results</li> </ul> </li> </ul> <h2>Reproducing analysis</h2> <p>A guide to reproducing the analysis from the paper follows.</p> <h3>Getting the code</h3> <p>First download the code. The recommended way is to <code>git clone</code> our Github repository from the command line:</p> <pre class="highlight"><code>git clone <a href="https://github.com/dylanhmorris/optimal-sir-intervention.git" rel="nofollow">https://github.com/dylanhmorris/optimal-sir-intervention.git</a></code></pre> <p>Downloading it manually via Github's download button or on OSF should also work.</p> <h3>Dependency installation</h3> <p>The analysis can be auto-run from the project <code>Makefile</code>, but you may need to install some external dependencies first. In the first instance, you'll need a working installation of Python 3 with the package manager <code>pip3</code> and a working installation of Gnu Make or similar. A few external python packages can then be installed by typing.</p> <pre class="highlight"><code>make depend</code></pre> <p>You may also need a working TeX installation to render the text for the figures. If you do not have TeX, you can get around this by setting <code>mpl.rcParams['text.usetex'] = False</code> in <code><a href="http://plotting_style.py" rel="nofollow">plotting_style.py</a></code>.</p> <h3>Running the analyses</h3> <p>The simplest approach is simply to type <code>make</code> at the command line, which should produce a full set of figures and results.</p> <p>If you want to do things piecewise, typing <code>make &lt;filename&gt;</code> for any of the files present in the complete repository uploaded here should also work.</p> <p>Some shortcuts are available:</p> <ul> <li><code>make results</code> calculates numerical results that are slower to calculate (and therefore must be saved to disk)</li> <li><code>make figures</code> produces all figures</li> <li><code>make clean</code> removes all generated files, leaving only source code (though it does not uninstall packages)</li> </ul>
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