2024/05/09 by Yajuan Si, Trần Khánh Toàn, Si, Yajuan +7
Mathematics · #Applications (stat.AP) #COVID-19 epidemiological studies #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.2405.05909
openalex publication_date 2024/05/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.