Modeling regional disease spread over time using a dynamic spatio-temporal model - With an application to porcine epidemic diarrhea virus data in Iowa, US.

Abstract

Regional surveillance is important for detecting the incursion of new pathogens and informing disease monitoring and control programs. Modeling disease distribution over time can provide insight into the development of more efficient regional surveillance approaches. Herein we propose a Bayesian spatio-temporal model to describe the distribution of porcine epidemic diarrhea virus (PEDV) in Iowa USA. Model parameters are estimated through a Bayesian spatio-temporal model approach which can account for missing values. For illustration, we apply the proposed model to PEDV test results from the Iowa State University Veterinary Diagnostic Laboratory (ISU-VDL). A simulation study carried out to evaluate the model showed that the proposed model captured the pattern of PEDV distribution and its spatio-temporal dependence.