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Throughout the COVID-19 pandemic, government policy and healthcareimplementation responses have been guided by reported positivity rates andcounts of positive cases in the community. The selection bias of these datacalls into question their validity as measures of the actual viral incidence inthe community and as predictors of clinical burden. In the absence of anysuccessful public or academic campaign for comprehensive or random testing, wehave developed a proxy method for synthetic random sampling, based on viral RNAtesting of patients who present for elective procedures within a hospitalsystem. We present here an approach under multilevel regression andpoststratification (MRP) to collecting and analyzing data on viral exposureamong patients in a hospital system and performing statistical adjustment thathas been made publicly available to estimate true viral incidence and trends inthe community. We apply our MRP method to track viral behavior in a mixedurban-suburban-rural setting in Indiana. This method can be easily implementedin a wide variety of hospital settings. Finally, we provide evidence that thismodel predicts the clinical burden of SARS-CoV-2 earlier and more accuratelythan currently accepted metrics.

Routine Hospital-based SARS-CoV-2 Testing Outperforms State-based Data in Predicting Clinical Burden