A Novel Bayesian Spatial-Temporal Approach to Quantify SARS-CoV-2 Testing Disparities for Small Area Estimation.

Abstract

<b>Objectives.</b> To propose a novel Bayesian spatial-temporal approach to identify and quantify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing disparities for small area estimation. <b>Methods.</b> In step 1, we used a Bayesian inseparable space-time model framework to estimate the testing positivity rate (TPR) at geographically granular areas of the census block groups (CBGs). In step 2, we adopted a rank-based approach to compare the estimated TPR and the testing rate to identify areas with testing deficiency and quantify the number of needed tests. We used weekly SARS-CoV-2 infection and testing surveillance data from Cameron County, Texas, between March 2020 and February 2022 to demonstrate the usefulness of our proposed approach. <b>Results.</b> We identified the CBGs that had experienced substantial testing deficiency, quantified the number of tests that should have been conducted in these areas, and evaluated the short- and long-term testing disparities. <b>Conclusions.</b> Our proposed analytical framework offers policymakers and public health practitioners a tool for understanding SARS-CoV-2 testing disparities in geographically small communities. It could also aid COVID-19 response planning and inform intervention programs to improve goal setting and strategy implementation in SARS-CoV-2 testing uptake. (<i>Am J Public Health</i>. 2023;113(1):40-48. https://doi.org/10.2105/AJPH.2022.307127).