✕
OpenMind Journal of Multidisciplinary Innovation & Development
Home / Archive / Vol. 2 (2026): Issue 4 (Jul–Aug) / Articles
Research Article

Veterinary Epidemiology and Disease Modelling

Fadimatu Dauda Muhammad

Department of Veterinary Medicine, Usman Dan Fodio University Sokoto, Sokoto 840104, Sokoto, Nigeria

Kaltum Ismail Adam

Yobe State University, Yobe State, Nigeria

Received: 20 June 2026 | Accepted: 15 July 2026 | Published: 20 July 2026

DOI: https://doi.org/10.5281/zenodo.21485031 | Pages: 1-30 | Volume: 2 | Issue: 4

Abstract

Veterinary epidemiology has undergone significant methodological evolution, transitioning from traditional descriptive approaches to sophisticated quantitative frameworks that integrate spatio-temporal analysis, mechanistic modelling, and advanced computational techniques. This review synthesizes current methodological approaches in veterinary epidemiology and disease modelling, with particular emphasis on the integration of spatio-temporal analysis, transmission dynamics, and surveillance strategies. The emergence of novel pathogens and the increasing complexity of livestock production systems necessitate the adoption of multidisciplinary modelling approaches that can capture the intricate dynamics of disease transmission at various spatial and temporal scales. This article examines the application of Bayesian geostatistical methods, hidden Markov models, and network-based approaches in understanding disease patterns and informing control strategies. Furthermore, the review addresses the challenges of imperfect disease detection and the integration of artificial intelligence in veterinary epidemiological research. The findings highlight the critical importance of combining multiple modelling frameworks to enhance disease surveillance, risk assessment, and the evaluation of intervention strategies in animal populations.

Keywords

Veterinary epidemiology; Disease modelling; Spatio-temporal analysis; Transmission dynamics; Surveillance; Bayesian methods; Network analysis

References

Arjkumpa, O., Suwannaboon, M., Boonrod, M., Punyawan, I., Liangchaisiri, S., Laobannue, P., ... Punyapornwithaya, V. (2022). The first lumpy skin disease outbreak in Thailand (2021): Epidemiological features and spatio-temporal analysis. Frontiers in Veterinary Science, 8, Article 799065. https://doi.org/10.3389/fvets.2021.799065

Biggeri, A., Dreassi, E., Catelan, D., Rinaldi, L., Lagazio, C., & Cringoli, G. (2006). Disease mapping in veterinary epidemiology: A Bayesian geostatistical approach. Statistical Methods in Medical Research, 15(4), 337–352.

Combelles, L., Corbiere, F., Calavas, D., Bronner, A., Hénaux, V., & Vergne, T. (2019). Impact of imperfect disease detection on the identification of risk factors in veterinary epidemiology. Frontiers in Veterinary Science, 6, Article 66. https://doi.org/10.3389/fvets.2019.00066

Ezanno, P., Picault, S., Beaunée, G., Bailly, X., Muñoz, F., Duboz, R., ... Guégan, J. F. (2021). Research perspectives on animal health in the era of artificial intelligence. Veterinary Research, 52(1), Article 40. https://doi.org/10.1186/s13567-021-00902-4

Galvis, J. A., Corzo, C. A., Prada, J. M., & Machado, G. (2022). Modelling the transmission and vaccination strategy for porcine reproductive and respiratory syndrome virus. Transboundary and Emerging Diseases, 69(2), 485–500.

Gervasi, V., Marcon, A., Bellini, S., & Guberti, V. (2019). Evaluation of the efficiency of active and passive surveillance in the detection of African swine fever in wild boar. Veterinary Sciences, 7(1), Article 5. https://doi.org/10.3390/vetsci7010005

Kim, D., Michelot, T., Mertes, K., Stabach, J. A., & Fieberg, J. (2026). Detecting disease progression from animal movement using hidden Markov models. Journal of Applied Ecology, 63(3), Article e70323.

Kinsley, A. C., Rossi, G., Silk, M. J., & VanderWaal, K. (2020). Multilayer and multiplex networks: An introduction to their use in veterinary epidemiology. Frontiers in Veterinary Science, 7, Article 596. https://doi.org/10.3389/fvets.2020.00596

Kirkeby, C., Brookes, V. J., Ward, M. P., Dürr, S., & Halasa, T. (2021). A practical introduction to mechanistic modeling of disease transmission in veterinary science. Frontiers in Veterinary Science, 7, Article 546651. https://doi.org/10.3389/fvets.2020.546651

Kleczkowski, A., Hoyle, A., & McMenemy, P. (2019). One model to rule them all? Modelling approaches across One Health for human, animal and plant epidemics. Philosophical Transactions of the Royal Society B: Biological Sciences, 374(1775), Article 20180255. https://doi.org/10.1098/rstb.2018.0255

Lanzas, C., & Chen, S. (2015). Complex system modelling for veterinary epidemiology. Preventive Veterinary Medicine, 118(2–3), 207–214.

Moustakas, A. (2017). Spatio-temporal data mining in ecological and veterinary epidemiology. Stochastic Environmental Research and Risk Assessment, 31(4), 829–834.

Rashid, M., Rashid, M. I., Akbar, H., Ahmad, L., Hassan, M. A., Ashraf, K., ... Gharbi, M. (2019). A systematic review on modelling approaches for economic losses studies caused by parasites and their associated diseases in cattle. Parasitology, 146(2), 129–141.

Rees, E. M., Minter, A., Edmunds, W. J., Lau, C. L., Kucharski, A. J., & Lowe, R. (2021). Transmission modelling of environmentally persistent zoonotic diseases: A systematic review. The Lancet Planetary Health, 5(7), e466–e478.

Thrusfield, M. (2018). Veterinary epidemiology (4th ed.). John Wiley & Sons.