From predictions to prescriptions: A data-driven response to COVID-19.
Aged
COVID-19
/ mortality
Databases, Factual
Female
Forecasting
Humans
Intensive Care Units
Machine Learning
Male
Middle Aged
Models, Statistical
Pandemics
Policy Making
Prognosis
Risk Assessment
/ statistics & numerical data
SARS-CoV-2
Ventilators, Mechanical
/ supply & distribution
COVID-19 Drug Treatment
COVID-19
Epidemiological modeling
Machine learning
Optimization
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
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
253-272Subventions
Organisme : National Science Foundation
ID : #174530
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