Selected article for: "electronic supplementary material and epidemiological model"

Author: Bertsimas, Dimitris; Boussioux, Leonard; Cory-Wright, Ryan; Delarue, Arthur; Digalakis, Vassilis; Jacquillat, Alexandre; Kitane, Driss Lahlou; Lukin, Galit; Li, Michael; 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: 74owzzu3
  • Document date: 2021_2_15
  • ID: 74owzzu3
    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 vaccine trial location planning at Janssen Pharmaceuticals, and have been integrated into the US Center for Disease Control’s pandemic forecast. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10729-020-09542-0) contains supplementary material, which is available to authorized users. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1007/s10729-020-09542-0)

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