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X-WR-CALNAME;VALUE=TEXT:Maximizing the utility of a limited number of COVID tests
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SUMMARY:Maximizing the utility of a limited number of COVID tests
DESCRIPTION:<p>	<strong>Presenter</strong>:  Francisco Marmolejo and Roberto Tello<br><strong>Topic</strong>:  Maximizing the utility of a limited number of COVID tests</p><p>	<!--break--></p><h2>	<strong>Fall 2021</strong></h2><p>	The EconCS Group holds an Economics and Computer Science research seminar each semester.</p><p>	Fall 2021 meetings are held at 1 - 2:30 PM on Fridays. Seminar Coordinators for Fall '21 are <a href="mailto:saisr@g.harvard.edu">Srivatsa R Sai</a> and <a href="mailto:dhalpern@g.harvard.edu">Daniel Halpern</a>.</p><p>	<strong>Abstract: </strong>As the world continues to struggle with the ongoing COVID-19 pandemic, one of the key challenges for developing nations has been a limited access to testing resources. Testing is an essential tool for combating epidemics, as it gives crucial estimates of virus prevalence, and allows for the identification of infected individuals, thereby forming the basis of containment policies. However, testing resources are often scarce due to factors such as limited access to reagents, shortages in trained lab technicians, and deficient logistics. With limited viral surveillance, containment policies are necessarily less precise, consequently resulting in scenarios where otherwise healthy individuals are unnecessarily forced to self-isolate or quarantine. In this session, we will talk about Test &amp; Contain, a proposed strategy for increasing the utility of a limited number of COVID tests via group testing and a multi-objective optimization framework. Our strategy, which is currently being piloted in research institutions across Mexico, assists policy-makers in understanding inherent trade-offs stemming from limited testing resources while providing feasible testing strategies that exemplify their desired trade-offs. Finally, we will also discuss recent extensions of our testing allocation framework based on reinforcement learning.</p>
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STATUS:CONFIRMED
DTSTART:20210910T170000Z
DTEND:20210910T183000Z
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