ArticleCommunications medicine2023
Forecasting hospital-level COVID-19 admissions using real-time mobility data.
Article in Communications medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- An exploratory study of patient hospitalization patterns and behavioral risk factors using mobile phone location data.PLOS digital health · 2026Article
- A systems approach for anticipating post-pandemic forecast challenges for pediatric influenza and hospital burden.Scientific reports · 2025Article
- How Will AI Shape the Future of Pandemic Response? Early Clues From Data Analytics.Risk analysis : an official publication of the Society for Risk Analysis · 2025Review
- A modular approach to forecasting COVID-19 hospital bed occupancy.Communications medicine · 2025Article
- Behavior-driven forecasts of neighborhood-level COVID-19 spread in New York City.PLoS computational biology · 2025Article
- The Crucial Role of Laboratory Medicine in Addressing Future Public Health Infectious Threats: Insights Gained from the COVID-19 Pandemic.Diagnostics (Basel, Switzerland) · 2025Article
- The Italian public health response during the pandemic emergency: from qualitative data to the "performance index" of care provided by Spoleto Hospital.Frontiers in public health · 2025Article
- Forecasting the relative abundance of Aedes vector populations to enhance situational awareness for mosquito control operations.PLoS neglected tropical diseases · 2024Article
- Private commercial companies sharing health-relevant consumer data with health researchers in sub-Saharan Africa: an ethical exploration.Policy studies (Policy Studies Institute) · 2024Article
- Nonlinear bias toward complex contagion in uncertain transmission settings.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Wastewater-based epidemiology predicts COVID-19-induced weekly new hospital admissions in over 150 USA counties.Nature communications · 2023Article
- Intermunicipal travel networks of Mexico during the COVID-19 pandemic.Scientific reports · 2023Article
- Forecasting hospital-level COVID-19 admissions using real-time mobility data.Communications medicine · 2023Article
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Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
backgroundFor each of the COVID-19 pandemic waves, hospitals have had to plan for deploying surge capacity and resources to manage large but transient increases in COVID-19 admissions. While a lot of effort has gone into predicting regional trends in COVID-19 cases and hospitalizations, there are far fewer successful tools for creating accurate hospital-level forecasts.
methodsLarge-scale, anonymized mobile phone data has been shown to correlate with regional case counts during the first two waves of the pandemic (spring 2020, and fall/winter 2021). Building off this success, we developed a multi-step, recursive forecasting model to predict individual hospital admissions; this model incorporates the following data: (i) hospital-level COVID-19 admissions, (ii) statewide test positivity data, and (iii) aggregate measures of large-scale human mobility, contact patterns, and commuting volume.
resultsIncorporating large-scale, aggregate mobility data as exogenous variables in prediction models allows us to make hospital-specific COVID-19 admission forecasts 21 days ahead. We show this through highly accurate predictions of hospital admissions for five hospitals in Massachusetts during the first year of the COVID-19 pandemic.
conclusionsThe high predictive capability of the model was achieved by combining anonymized, aggregated mobile device data about users' contact patterns, commuting volume, and mobility range with COVID hospitalizations and test-positivity data. Mobility-informed forecasting models can increase the lead-time of accurate predictions for individual hospitals, giving managers valuable time to strategize how best to allocate resources to manage forthcoming surges.
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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.