Bayesian Spatio-temporal Regression Models for Group Testing Data

Oregon Convention Center 

Group testing is a procedure that tests groups of biospecimens instead of individual ones. If a pool tests positive, subsequent tests are usually conducted on the individuals who contributed to the pool to determine their disease status; if a pool tests negative, all are considered disease-free. Under relatively low disease prevalence, group testing reduces required diagnostic tests and the associated costs. Spatio-temporal dependencies can arise in testing data collected across multiple locations and time points. However, existing group testing models are not appropriate for spatio-temporal data. In this study, we propose two Bayesian spatio-temporal regression models for discrete-time areal group testing data. We apply the proposed models to COVID-19 testing data from 4,516 South Carolina residents (2020-2022) and 19,152 Central New York residents (2020). Our models are suitable for various group testing protocols and can estimate the sensitivity and specificity of diagnostic tests. Moreover, the models also produce forecast maps for future infection prevalence. This study showcases the effectiveness of group testing in forecasting infectious diseases across different locations.

group testing

Bayesian spatio-temporal model

infectious disease forecasting

conditional autoregressive model

vector autoregressive model

COVID-19 

Section on Statistics in Epidemiology