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Multilevel regression and poststratification interface: an application to track community-level COVID-19 viral
Yajuan Si1, Toan Tran2, Jonah Gabry3
1Institute for Social Research, University of Michigan, 426 Thompson St., Ann Arbor, 48104, MI, USA. yajuan@umich.edu.
Population Health Metrics
|February 4, 2026
Summary
Public health surveillance can now estimate COVID-19 incidence using a novel method. This approach analyzes asymptomatic patient testing data, creating a synthetic random sample for accurate community viral tracking.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health
Background:
- High-quality data is crucial for effective public health surveillance.
- The COVID-19 pandemic highlighted limitations in traditional testing methods for accurate population representation.
- Accurate estimation of community viral incidence is essential for public health response.
Purpose of the Study:
- To develop a proxy method for synthetic random sampling to estimate community-level viral incidence.
- To utilize asymptomatic patient testing data from elective procedures as a proxy for random sampling.
- To improve the accuracy of public health surveillance data during pandemics.
Main Methods:
- Collected routine SARS-CoV-2 testing data from outpatients undergoing elective procedures.
- Applied multilevel regression and poststratification (MRP) to adjust for sample nonrepresentativeness.
- Extended MRP methodology to incorporate time-varying data and granular geographic information.
Main Results:
- Developed an open-source, user-friendly MRP interface for public implementation.
- Illustrated the MRP interface's application in tracking COVID-19 transmission in Michigan.
- Presented estimated infection rates over time across demographic and geographic subpopulations.
Conclusions:
- The MRP interface offers timely insights into population health trends.
- It serves as a valuable surveillance tool for epidemic preparedness.
- The methodology is broadly applicable to diverse health and social science research, ensuring reproducibility.
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