Multilevel Regression and Poststratification Interface: An Application to Track Community-level COVID-19 Viral Transmission
Abstract
We present a novel Bayesian workflow for multilevel regression and poststratification (MRP), introducing extensions to time-varying data and granular geography and publicly available open-source computation tools, facilitating broad research adoption and reproducibility. 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 community-level viral incidence, based on viral RNA testing of asymptomatic patients who present for elective procedures within a hospital system. The approach collects routine testing data on SARS-CoV-2 exposure among outpatients and performs statistical adjustments of sample representation using MRP, a procedure that adjusts for nonrepresentativeness of the sample and yields stable small group estimates. We illustrate the MRP interface with an application to track community-level COVID-19 viral transmission in the state of Michigan.
Keywords
Cite
@article{arxiv.2405.05909,
title = {Multilevel Regression and Poststratification Interface: An Application to Track Community-level COVID-19 Viral Transmission},
author = {Yajuan Si and Toan Tran and Jonah Gabry and Mitzi Morris and Andrew Gelman},
journal= {arXiv preprint arXiv:2405.05909},
year = {2026}
}