From predictions to prescriptions: A data-driven response to COVID-19
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10729_2020_9542_ReferencePDF.pdf
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2.41 MB
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Checksum (MD5)
d773c9a929f8c0cc8a2492cbac58c686
Author(s)
Bertsimas, Dimitris
Boussioux, Leonard
Cory-Wright, Ryan
Delarue, Arthur
Digalakis, Vassilis
Jacquillat, Alexandre
Kitane, Driss L.
Lukin, Galit
Li, Michael
Mingardi, Luca
Date Issued
February 15, 2021
Publisher
Springer US
Version
Author's final manuscript
Abstract
Abstract
The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to slow the disease, at a steep economic price. We design analytical tools to support these decisions and combat the pandemic. Specifically, we propose a comprehensive data-driven approach to understand the clinical characteristics of COVID-19, predict its mortality, forecast its evolution, and ultimately alleviate its impact. By leveraging cohort-level clinical data, patient-level hospital data, and census-level epidemiological data, we develop an integrated four-step approach, combining descriptive, predictive and prescriptive analytics. First, we aggregate hundreds of clinical studies into the most comprehensive database on COVID-19 to paint a new macroscopic picture of the disease. Second, we build personalized calculators to predict the risk of infection and mortality as a function of demographics, symptoms, comorbidities, and lab values. Third, we develop a novel epidemiological model to project the pandemic’s spread and inform social distancing policies. Fourth, we propose an optimization model to re-allocate ventilators and alleviate shortages. Our results have been used at the clinical level by several hospitals to triage patients, guide care management, plan ICU capacity, and re-distribute ventilators. At the policy level, they are currently supporting safe back-to-work policies at a major institution and vaccine trial location planning at Janssen Pharmaceuticals, and have been integrated into the US Center for Disease Control’s pandemic forecast.
MIT Department
Sloan School of Management
Massachusetts Institute of Technology. Operations Research Center
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/s10729-020-09542-0
