ArticleHealth care management science2021
From predictions to prescriptions: A data-driven response to COVID-19.
Article in Health care management science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed.
- A stochastic programming approach for dynamic allocation of bed capacity and assignment of patients to collaborating hospitals during pandemic outbreaks.Health care management science · 2026Article
- Integrating epidemiological modeling and time-series forecasting to optimize pandemic patient allocation.International journal of health geographics · 2026Article
- Machine Learning Optimization Algorithms for Clustering Regions and Emergency Management: A Review.Journal of multidisciplinary healthcare · 2026Review
- Public health interventions for developing resilience to contagious diseases: a system dynamics approach.Health care management science · 2025Article
- Can past variants of SARS-CoV-2 predict the impact of future variants? Machine learning for early warning of US counties at risk.Health care management science · 2025Article
- An Optimization-Based Framework to Dynamically Schedule Hospital Beds in a Pandemic.Healthcare (Basel, Switzerland) · 2025Article
- The crucial role of explainable artificial intelligence (XAI) in improving health care management.Health care management science · 2025Article
- Systematic Comparison of Different Compartmental Models for Predicting COVID-19 Progression.Epidemiologia (Basel, Switzerland) · 2025Article
- A Review of the Use of Data Analytics to Address Preeclampsia in Ecuador Between 2020 and 2024.Diagnostics (Basel, Switzerland) · 2025Review
- Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges.Nature communications · 2025Article
- Article
- Evaluating infectious disease forecasts with allocation scoring rules.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2024Article
- Changing US Support for Public Health Data Use Through Pandemic and Political Turmoil.The Milbank quarterly · 2024Article
- Mitigating the COVID-19 Pandemic through Data-Driven Resource Sharing.Naval research logistics · 2024Article
- Predicting the spread of SARS-CoV-2 variants: An artificial intelligence enabled early detection.PNAS nexus · 2024Article
- Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways.Health care management science · 2023Article
- Predictors of all-cause mortality in hospitalized COVID-19 patients taking corticosteroids: a multicenter retrospective cross-sectional study.Annals of medicine and surgery (2012) · 2023Article
- Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature.Applied clinical informatics · 2023Review
- Gender and sex bias in COVID-19 epidemiological data through the lens of causality.Information processing & management · 2023Article
- Modeling social, economic, and health perspectives for optimal pandemic policy decision-making.Socio-economic planning sciences · 2023Article
Corrections and comments
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Authors and funding
21 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.