Campus Units

Veterinary Diagnostic and Production Animal Medicine, Statistics

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

6-20-2020

Journal or Book Title

Preventive Veterinary Medicine

First Page

105053

DOI

10.1016/j.prevetmed.2020.105053

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.

Comments

This is a manuscript of an article published as Ji, J., C. Wang, M. Rotolo, and J. Zimmerman. "Modeling Regional Disease Spread Over Time Using a Dynamic Spatio-temporal Model–With an Application to Porcine Epidemic Diarrhea Virus data in Iowa, US." Preventive Veterinary Medicine (2020): 105053. DOI: 10.1016/j.prevetmed.2020.105053. Posted with permission.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

Elsevier B.V.

Language

en

File Format

application/pdf

Available for download on Sunday, June 20, 2021

Published Version

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