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Updated: Oct 4, 2025
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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
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Real-time pandemic surveillance using hospital admissions and mobility data
Spencer J Fox1, Michael Lachmann2, Mauricio Tec3
1Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78712; fox@austin.utexas.edu.
Summary
Hospital admissions and mobility data can accurately forecast severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and healthcare needs. This model reliably projects COVID-19 burden in US cities, improving public health strategies.
Area of Science:
- Epidemiology
- Public Health
- Data Science
Background:
- Forecasting COVID-19 burden is challenging due to data limitations like biased case reporting and lagging death counts.
- Hospital data is influenced by varying access, admission criteria, and demographics, complicating accurate predictions.
- Existing methods struggle to provide reliable real-time estimates of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and healthcare demand.
Purpose of the Study:
- To demonstrate that hospital admissions combined with mobility data can reliably predict SARS-CoV-2 transmission rates and healthcare demand.
- To validate a forecasting model used to guide mitigation policies in Austin, Texas.
- To assess changes in the relationship between mobility and transmission over time due to public health interventions.
Main Methods:
- Utilized a forecasting model incorporating hospital admissions and mobility data.
- Estimated local reproduction number (R0), case detection rates, and infection prevalence.
- Analyzed the impact of mobility on transmission and compared forecast accuracy with reported data.
Main Results:
- The local reproduction number varied significantly, from an initial average of 5.8 to a low of 0.65 post-surge.
- Case detection rates improved from 17.2% to 70% by January 2021.
- Mobility-associated transmission decreased by 62% by February 2021 compared to March 2020.
- Forecasts demonstrated high accuracy, with 1, 2, and 3-week ahead predictions containing 93.6%, 89.9%, and 87.7% of reported data, respectively.
Conclusions:
- Hospital admissions and mobility data provide a reliable method for forecasting SARS-CoV-2 transmission and healthcare demand.
- The developed model can effectively guide public health policies and project COVID-19 healthcare needs in urban areas.
- Public adherence to precautionary behaviors significantly altered the transmission dynamics related to mobility.
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