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


Journal

Health care management science
ISSN: 1572-9389
Titre abrégé: Health Care Manag Sci
Pays: Netherlands
ID NLM: 9815649

Informations de publication

Date de publication:
Jun 2021
Historique:
received: 14 09 2020
accepted: 16 12 2020
pubmed: 17 2 2021
medline: 3 7 2021
entrez: 16 2 2021
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 33590417
doi: 10.1007/s10729-020-09542-0
pii: 10.1007/s10729-020-09542-0
pmc: PMC7883965
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

253-272

Subventions

Organisme : National Science Foundation
ID : #174530

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Auteurs

Dimitris Bertsimas (D)

Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142, USA. dbertsim@mit.edu.
Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. dbertsim@mit.edu.

Leonard Boussioux (L)

Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Ryan Cory-Wright (R)

Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Arthur Delarue (A)

Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Vassilis Digalakis (V)

Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Alexandre Jacquillat (A)

Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Driss Lahlou Kitane (DL)

Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Galit Lukin (G)

Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Michael Li (M)

Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Luca Mingardi (L)

Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Omid Nohadani (O)

Benefits Science Technologies, Boston, MA 02110, USA.

Agni Orfanoudaki (A)

Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Theodore Papalexopoulos (T)

Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Ivan Paskov (I)

Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Jean Pauphilet (J)

London Business School, London, NW1 4SA, UK.

Omar Skali Lami (OS)

Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Bartolomeo Stellato (B)

Operations Research and Financial EngineeringPrinceton University, Princeton, NJ, 08544, USA.

Hamza Tazi Bouardi (HT)

Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Kimberly Villalobos Carballo (KV)

Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Holly Wiberg (H)

Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Cynthia Zeng (C)

Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

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Classifications MeSH