From predictions to prescriptions: A data-driven response to COVID-19

Dimitris Bertsimas (), Leonard Boussioux, Ryan Cory-Wright, Arthur Delarue, Vassilis Digalakis, Alexandre Jacquillat, Driss Lahlou Kitane, Galit Lukin, Michael Li, Luca Mingardi, Omid Nohadani, Agni Orfanoudaki, Theodore Papalexopoulos, Ivan Paskov, Jean Pauphilet, Omar Skali Lami, Bartolomeo Stellato, Hamza Tazi Bouardi, Kimberly Villalobos Carballo, Holly Wiberg and Cynthia Zeng
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Dimitris Bertsimas: Massachusetts Institute of Technology
Leonard Boussioux: Massachusetts Institute of Technology
Ryan Cory-Wright: Massachusetts Institute of Technology
Arthur Delarue: Massachusetts Institute of Technology
Vassilis Digalakis: Massachusetts Institute of Technology
Alexandre Jacquillat: Massachusetts Institute of Technology
Driss Lahlou Kitane: Massachusetts Institute of Technology
Galit Lukin: Massachusetts Institute of Technology
Michael Li: Massachusetts Institute of Technology
Luca Mingardi: Massachusetts Institute of Technology
Omid Nohadani: Benefits Science Technologies
Agni Orfanoudaki: Massachusetts Institute of Technology
Theodore Papalexopoulos: Massachusetts Institute of Technology
Ivan Paskov: Massachusetts Institute of Technology
Jean Pauphilet: London Business School
Omar Skali Lami: Massachusetts Institute of Technology
Bartolomeo Stellato: Operations Research and Financial EngineeringPrinceton University
Hamza Tazi Bouardi: Massachusetts Institute of Technology
Kimberly Villalobos Carballo: Massachusetts Institute of Technology
Holly Wiberg: Massachusetts Institute of Technology
Cynthia Zeng: Massachusetts Institute of Technology

Health Care Management Science, 2021, vol. 24, issue 2, No 2, 253-272

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.

Keywords: COVID-19; Epidemiological modeling; Machine learning; Optimization (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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DOI: 10.1007/s10729-020-09542-0

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