Modeling reductions in SARS-CoV-2 transmission and hospital burden achieved by prioritizing testing using a clinical prediction rule.

Prompt identification of cases is critical for slowing the spread of COVID-19. However, many areas have faced diagnostic testing shortages, requiring difficult decisions to be made regarding who receives a test, without knowing the implications of those decisions on population-level transmission dynamics. Clinical prediction rules (CPRs) are commonly used tools to guide clinical decisions. We used data from electronic health records to develop a parsimonious 5-variable CPR to identify those who are most likely to test positive, and found that its application to prioritize testing increases the proportion of those testing positive in settings of limited testing capacity. To consider the implications of these gains in daily case detection on the population level, we incorporated testing using the CPR into a compartmentalized disease transmission model. We found that prioritized testing led to a delayed and lowered infection peak (i.e. 'flattens the curve'), with the greatest impact at lower values of the effective reproductive number (such as with concurrent social distancing measures), and when higher proportions of infectious persons seek testing. Additionally, prioritized testing resulted in reductions in overall infections as well as hospital and intensive care unit (ICU) burden. In conclusion, we present a novel approach to evidence-based allocation of limited diagnostic capacity, to achieve public health goals for COVID-19.