ArticlePopulation health metrics2026
Multilevel regression and poststratification interface: an application to track community-level COVID-19 viral transmission.
Article in Population health metrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposePublic health surveillance systems require high-quality data to represent the population. In the absence of comprehensive or random testing throughout the COVID-19 pandemic, we have developed a proxy method for synthetic random sampling to estimate the actual community-level viral incidence, based on viral testing of patients who are asymptomatic and present for elective procedures within a hospital system.
methodsThe approach collects routine testing data on SARS-CoV-2 exposure among outpatients and performs statistical adjustments of sample representation using multilevel regression and poststratification (MRP), a procedure that adjusts for nonrepresentativeness of the sample and yields stable small group estimates. We extend MRP to accommodate time-varying data and granular geography.
resultsWe have developed an open-source, user-friendly MRP interface for public implementation of the Bayesian analysis workflow. We illustrate the MRP interface with an application to track community-level COVID-19 viral transmission in Michigan. We present the estimated infection rate over time for the targeted population and across demographic and geographic subpopulations.
conclusionThe interface provides timely, substantive insights into population health trends and serves as a valuable surveillance tool for future epidemic preparedness. Beyond monitoring COVID-19, the MRP interface can analyze a wide range of health and social science data, making it broadly applicable to diverse research areas with reproducibility and scientific rigor.
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