The Power of Analytics in Epidemiology for COVID 19

Mohammed Amine Bennouna, David Alexandre Nze Ndong, Georgia Perakis, Divya Singhvi, Omar Skali Lami (), Ioannis Spantidakis, Leann Thayaparan, Asterios Tsiourvas and Shane Weisberg
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Mohammed Amine Bennouna: Massachusetts Institute of Technology
David Alexandre Nze Ndong: Massachusetts Institute of Technology
Georgia Perakis: Massachusetts Institute of Technology
Divya Singhvi: NYU Stern School of Business
Omar Skali Lami: Massachusetts Institute of Technology
Ioannis Spantidakis: Massachusetts Institute of Technology
Leann Thayaparan: Massachusetts Institute of Technology
Asterios Tsiourvas: Massachusetts Institute of Technology
Shane Weisberg: Massachusetts Institute of Technology

A chapter in AI and Analytics for Smart Cities and Service Systems, 2021, pp 254-268 from Springer

Abstract: Abstract Mitigating the COVID-19 pandemic poses a series of unprecedented challenges, including predicting new cases and deaths, understanding the true prevalence, and allocating the different vaccines across regions. In this paper, we describe our efforts to tackle these issues. We first discuss the methods we developed for predicting cases and deaths using a novel ML based method we call MIT-Cassandra. MIT-Cassandra is currently being used by the CDC and is consistently among the top 10 methods in accuracy, often ranking 1st amongst all submitted methods. We then use this prediction to model the true prevalence of COVID 19 and incorporate this prevalence into an optimization model for fair vaccine allocation. The latter part of the paper also gives insights on how prevalence and exposure of the disease in different parts of the population can affect the distribution of different vaccine doses in a fairway. Finally, and importantly, our work has specifically been used as part of a collaboration with MIT's Quest for Intelligence and as part of MIT's process to reopen the institute.

Keywords: COVID-19; Prevalence; Machine learning; Epidemiology; Ensemble methods; Optimization of vaccine distribution (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-030-90275-9_21

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DOI: 10.1007/978-3-030-90275-9_21

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