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

Author

Listed:
  • Dimitris Bertsimas

    (Massachusetts Institute of Technology
    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
    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)

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.

Suggested Citation

  • Dimitris Bertsimas & Leonard Boussioux & Ryan Cory-Wright & Arthur Delarue & Vassilis Digalakis & Alexandre Jacquillat & Driss Lahlou Kitane & Galit Lukin & Michael Li & Luca Mingardi & Omid Nohadani , 2021. "From predictions to prescriptions: A data-driven response to COVID-19," Health Care Management Science, Springer, vol. 24(2), pages 253-272, June.
  • Handle: RePEc:kap:hcarem:v:24:y:2021:i:2:d:10.1007_s10729-020-09542-0
    DOI: 10.1007/s10729-020-09542-0
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    References listed on IDEAS

    as
    1. Warwick McKibbin & Roshen Fernando, 2021. "The Global Macroeconomic Impacts of COVID-19: Seven Scenarios," Asian Economic Papers, MIT Press, vol. 20(2), pages 1-30, Summer.
    2. Parag A. Pathak & Tayfun Sönmez & M. Utku Unver & M. Bumin Yenmez, 2020. "Leaving No Ethical Value Behind: Triage Protocol Design for Pandemic Rationing," NBER Working Papers 26951, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Navarro-García, Manuel & Guerrero, Vanesa & Durban, María, 2023. "On constrained smoothing and out-of-range prediction using P-splines: A conic optimization approach," Applied Mathematics and Computation, Elsevier, vol. 441(C).
    2. Fattahi, Mohammad & Keyvanshokooh, Esmaeil & Kannan, Devika & Govindan, Kannan, 2023. "Resource planning strategies for healthcare systems during a pandemic," European Journal of Operational Research, Elsevier, vol. 304(1), pages 192-206.
    3. Soltanisehat, Leili & González, Andrés D. & Barker, Kash, 2023. "Modeling social, economic, and health perspectives for optimal pandemic policy decision-making," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
    4. Víctor Blanco & Ricardo Gázquez & Marina Leal, 2023. "Mathematical optimization models for reallocating and sharing health equipment in pandemic situations," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 355-390, July.

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