Throughout the COVID-19 pandemic, government policy and healthcare implementation responses have been guided by reported positivity rates and counts of positive cases in the community. The selection bias of these data calls into question their validity as measures of the actual viral incidence in the community and as predictors of clinical burden. In the absence of any successful public or academic campaign for comprehensive or random testing, we have developed a proxy method for synthetic random sampling, based on viral RNA testing of patients who present for elective procedures within a hospital system. We present here an approach under multilevel regression and poststratification (MRP) to collecting and analyzing data on viral exposure among patients in a hospital system and performing statistical adjustment that has been made publicly available to estimate true viral incidence and trends in the community. We apply our MRP method to track viral behavior in a mixed urban-suburban-rural setting in Indiana. This method can be easily implemented in a wide variety of hospital settings. Finally, we provide evidence that this model predicts the clinical burden of SARS-CoV-2 earlier and more accurately than currently accepted metrics.

翻译：在整个COVID-19大流行期间,政府政策和保健执行对策一直以所报告的积极率和社区积极病例数为指导。这些数据的选择偏差使人们质疑这些数据作为社区实际病毒发病率衡量标准以及临床负担预测器的有效性。在没有任何成功的公共或学术全面或随机测试运动的情况下,我们开发了合成随机抽样替代方法,其依据是对在医院系统内选择手术的病人进行病毒性RNA检测。我们在此提出一种多级回归和后分级(MRP)的方法,以收集和分析医院系统中患者病毒接触的数据,并进行统计调整,公开提供这些数据,以估计社区真实病毒发病率和趋势。我们采用我们的MRP方法来追踪印第安纳省城市-郊区混合环境中的病毒行为。这种方法很容易在各种医院环境中实施。最后,我们提供证据,该模型预测SARS-COV-2的临床负担比目前接受的指数更早、更准确。