Estimating disease transmission in a closed population under repeated testing
本文提出一个统计框架，用于监测和控制COVID-19传播，处理了区间删失的患病率数据并纳入传播动态和行为变化，应用于俄亥俄州立大学2020年秋季的学生检测数据。
Abstract The article presents a novel statistical framework for COVID-19 transmission monitoring and control, which was developed and deployed at The Ohio State University main campus in Columbus during the Autumn term of 2020. Our approach effectively handles prevalence data with interval censoring and explicitly incorporates changes in transmission dynamics and human behaviour. To illustrate the methodology’s usefulness, we apply it to both synthetic and actual student SARS-CoV-2 testing data collected at the OSU Columbus campus in late 2020.