Author: Bertsimas, Dimitris; Boussioux, L'eonard; Wright, Ryan Cory; Delarue, Arthur; Digalakis, Vassilis; Jacquillat, Alexandre; Kitane, Driss Lahlou; Lukin, Galit; Li, Michael Lingzhi; Mingardi, Luca; Nohadani, Omid; Orfanoudaki, Agni; Papalexopoulos, Theodore; Paskov, Ivan; Pauphilet, Jean; Lami, Omar Skali; Stellato, Bartolomeo; Bouardi, Hamza Tazi; Carballo, Kimberly Villalobos; Wiberg, Holly; Zeng, Cynthia
Title: From predictions to prescriptions: A data-driven response to COVID-19 Cord-id: 480ncsft Document date: 2020_6_30
ID: 480ncsft
Snippet: 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 i
Document: 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 equitable vaccine distribution planning at a major pharmaceutical company, and have been integrated into the US Center for Disease Control's pandemic forecast.
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