Veterinary Epidemiology and Disease Modelling

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