From predictions to prescriptions: A data-driven response to COVID-19
Abstract 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 stee...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer US
2021
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| Online Access: | https://hdl.handle.net/1721.1/136840 |
| _version_ | 1811080466490458112 |
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| author | Bertsimas, Dimitris Boussioux, Leonard Cory-Wright, Ryan Delarue, Arthur Digalakis, Vassilis Jacquillat, Alexandre Kitane, Driss L. Lukin, Galit Li, Michael Mingardi, Luca Nohadani, Omid Orfanoudaki, Agni Papalexopoulos, Theodore Paskov, Ivan Pauphilet, Jean Lami, Omar S. |
| author2 | Sloan School of Management |
| author_facet | Sloan School of Management Bertsimas, Dimitris Boussioux, Leonard Cory-Wright, Ryan Delarue, Arthur Digalakis, Vassilis Jacquillat, Alexandre Kitane, Driss L. Lukin, Galit Li, Michael Mingardi, Luca Nohadani, Omid Orfanoudaki, Agni Papalexopoulos, Theodore Paskov, Ivan Pauphilet, Jean Lami, Omar S. |
| author_sort | Bertsimas, Dimitris |
| collection | MIT |
| description | Abstract
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. |
| first_indexed | 2024-09-23T11:32:08Z |
| format | Article |
| id | mit-1721.1/136840 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:32:08Z |
| publishDate | 2021 |
| publisher | Springer US |
| record_format | dspace |
| spelling | mit-1721.1/1368402023-12-18T19:46:49Z From predictions to prescriptions: A data-driven response to COVID-19 Bertsimas, Dimitris Boussioux, Leonard Cory-Wright, Ryan Delarue, Arthur Digalakis, Vassilis Jacquillat, Alexandre Kitane, Driss L. Lukin, Galit Li, Michael Mingardi, Luca Nohadani, Omid Orfanoudaki, Agni Papalexopoulos, Theodore Paskov, Ivan Pauphilet, Jean Lami, Omar S. Sloan School of Management Massachusetts Institute of Technology. Operations Research Center Abstract 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. 2021-11-01T14:33:42Z 2021-11-01T14:33:42Z 2021-02-15 2021-06-29T03:37:51Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/136840 en https://doi.org/10.1007/s10729-020-09542-0 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature application/pdf Springer US Springer US |
| spellingShingle | Bertsimas, Dimitris Boussioux, Leonard Cory-Wright, Ryan Delarue, Arthur Digalakis, Vassilis Jacquillat, Alexandre Kitane, Driss L. Lukin, Galit Li, Michael Mingardi, Luca Nohadani, Omid Orfanoudaki, Agni Papalexopoulos, Theodore Paskov, Ivan Pauphilet, Jean Lami, Omar S. From predictions to prescriptions: A data-driven response to COVID-19 |
| title | From predictions to prescriptions: A data-driven response to COVID-19 |
| title_full | From predictions to prescriptions: A data-driven response to COVID-19 |
| title_fullStr | From predictions to prescriptions: A data-driven response to COVID-19 |
| title_full_unstemmed | From predictions to prescriptions: A data-driven response to COVID-19 |
| title_short | From predictions to prescriptions: A data-driven response to COVID-19 |
| title_sort | from predictions to prescriptions a data driven response to covid 19 |
| url | https://hdl.handle.net/1721.1/136840 |
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