Accepted for/Published in: JMIRx Med
Date Submitted: Aug 27, 2020
Date Accepted: Dec 24, 2020
Date Submitted to PubMed: Aug 4, 2023
Evaluating population density as a parameter for optimizing COVID-19 testing: Statistical Analysis
ABSTRACT
Background:
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission risk generally increases with proximity of those shedding the virus to those susceptible to infection. Thus, this risk is a function of both number of people and the area which they occupy. However, the latter continues to evade coronavirus disease 2019 (COVID-19) testing policy.
Objective:
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=0.02) between tests per capita and number of cases. In terms of population density, new cases were higher in areas with 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.
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