from predictions to prescriptions a data driven response to covid 19

• DOI: 10.48550/arxiv.2006.16509
• Publication Date: 2020-06-29
• Authors: Bertsimas, Dimitris; Boussioux, Leonard; Cory-Wright, Ryan; Delarue, Arthur; Digalakis, Vasileios; Jacquillat, Alexa...; Kitane, Driss Lahlou; Lukin, Galit; Li, Michael; Mingardi, Luca; Nohadani, Omid; Orfanoudaki, Agni; Papalexopoulos, T...; Paskov, Ivan; Pauphilet, Jean; Lami, Omar Skali; Stellato, Bartolomeo; Bouardi, Hamza Tazi; Carballo, Kimberl...; Wiberg, Holly; Zeng, Cynthia
• Keywords: Male; FOS: Computer and information sciences; Databases, Factual; 0211 other engineering and technologies; 313; Machine Learning (stat.ML); 02 engineering and technology; 310; Statistics - Applications; Risk Assessment; 410; Article; Machine Learning; 03 medical and health sciences; 0302 clinical medicine; Crises; Statistics - Machine Learning; FOS: Mathematics; Humans; Applications (stat.AP); LWL; Quantitative Biology - Populations and Evolution; Policy Making; Mathematics - Optimization and Control; Pandemics; Aged; Models, Statistical; Ventilators, Mechanical; Data collection and recording; SARS-CoV-2; Populations and Evolution (q-bio.PE); COVID-19; Middle Aged; Prognosis; COVID-19 Drug Treatment; 3. Good health; Intensive Care Units; Optimization and Control (math.OC); FOS: Biological sciences; Health service; Female; Forecasting

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 reallocate 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.Significance StatementIn the midst of the COVID-19 pandemic, healthcare providers and policy makers are wrestling with unprecedented challenges. How to treat COVID-19 patients with equipment shortages? How to allocate resources to combat the disease? How to plan for the next stages of the pandemic? We present a data-driven approach to tackle these challenges. We gather comprehensive data from various sources, including clinical studies, electronic medical records, and census reports. We develop algorithms to understand the disease, predict its mortality, forecast its spread, inform social distancing policies, and re-distribute critical equipment. These algorithms provide decision support tools that have been deployed on our publicly available website, and are actively used by hospitals, companies, and policy makers around the globe.