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.

翻译：我们提出了一种新颖的多级回归与事后分层贝叶斯工作流，引入了对时变数据与精细地理单元的扩展，并提供了公开可用的开源计算工具，以促进广泛的研究采用与可重复性。在COVID-19大流行期间缺乏全面或随机检测的情况下，我们开发了一种基于医院系统内接受择期手术的无症状患者病毒RNA检测的合成随机抽样代理方法，用于估计社区层面的病毒发病率。该方法收集门诊患者SARS-CoV-2暴露的常规检测数据，并利用MRP进行样本代表性的统计调整——该程序可校正样本的非代表性，并产生稳定的小组估计值。我们通过一项在密歇根州追踪社区层面COVID-19病毒传播的应用案例，展示了该MRP接口的功能。