  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.
