Evaluating Population Density as a Parameter for Optimizing COVID-19 Testing: Statistical Analysis

BACKGROUND: SARS-CoV-2 transmission risk generally increases with the proximity of those shedding the virus to those susceptible to infection. Thus, this risk is a function of both the number of people and the area they occupy. However, the latter continues to evade the COVID-19 testing policy. OBJECTIVE: The aim of this study is to analyze per capita COVID-19 testing data reported for Alabama to evaluate whether testing realignment along population density, rather than density agnostic per capita, would be more effective. METHODS: Descriptive statistical analyses were performed for population, density, COVID-19 tests administered, and positive cases for all 67 Alabama counties. RESULTS: Tests reported per capita appeared to suggest widespread statewide testing. However, there was little correlation (r=0.28, P=.02) between tests per capita and the number of cases. In terms of population density, new cases were higher in areas with a higher population density, despite relatively lower test rates as a function of density. CONCLUSIONS: Increased testing in areas with lower population density has the potential to induce a false sense of security even as cases continue to rise sharply overall.