The Role of Artificial Intelligence in Early Detection of Animal Diseases: A Case Study of Livestock Epidemics in Developing Countries

Abstract

Livestock diseases pose a persistent threat to food security, rural livelihoods, and economic stability in developing countries, where traditional disease surveillance systems often fail to provide timely and accurate outbreak detection. This study investigates the role of artificial intelligence in enhancing early detection of livestock diseases through an experimental mixed-methods approach. Quantitative analyses were conducted using multi-source livestock health, environmental, and epidemiological data to evaluate the performance of machine learning models in outbreak prediction and disease classification, while qualitative insights were used to contextualize AI adoption within resource-constrained veterinary systems. The results reveal that AI-based surveillance significantly improves detection accuracy and reduces outbreak detection lead time compared to conventional monitoring methods. Across multiple regions and livestock species, AI models consistently identified early disease signals prior to laboratory confirmation, enabling more effective and timely interventions. Visual analytics further demonstrated strong associations between livestock density, environmental stressors, and AI predictive performance. The study also highlights the scalability and robustness of AI systems across diverse epidemiological settings, emphasizing their potential to support decision-making in low-resource environments. Overall, the findings confirm that artificial intelligence can substantially strengthen livestock disease surveillance, mitigate epidemic impacts, and contribute to more resilient agricultural systems in developing countries.