Data-Driven Epidemiology and Climate Risk in Livestock and Domestic Animals

The intersection of epidemiology, climate change, and animal production has become a critical area of research as global climate change continues to reshape health landscapes and agricultural systems. Climate shifts now profoundly influence domestic animal health, productivity, and welfare—especially in vulnerable regions. Significant research shows that climate change affects livestock through multiple channels, including expanding the geographic range and virulence of infectious agents (bacteria, fungi, protozoa, and vectors), increasing environmental stressors that compromise immune regulation, and worsening the prevalence of zoonotic diseases. These impacts are further complicated by rising antimicrobial resistance and altered disease dynamics, which have led to heightened antibiotic use and the emergence of selective resistance patterns. Despite evolving monitoring and analytical technologies, there remains a pressing need for integrated approaches to understand and mitigate the adverse effects of climate change on animal populations.

Recent advances in big data analytics, environmental monitoring systems, and computational modeling have significantly improved the capacity to collect, store, and analyze vast amounts of climate and epidemiological data on local, regional, and global scales. These technological trajectories allow for the identification of nuanced patterns, predictive modeling of disease outbreaks, and data-driven decision-making to optimize animal health and production systems. However, substantial gaps remain in linking fine-scale microlevel data (e.g., farm or regional records, detailed observational studies) to macrolevel environmental trends, as well as in leveraging advanced analytical tools such as machine learning and joint distribution modeling in this context. There is increasing recognition of the need for robust statistical techniques and interdisciplinary methodologies to forecast infectious disease risks and guide adaptive strategies in animal agriculture.

This Research Topic aims to illuminate and advance understanding of climate-associated epidemiological risks in domestic animals and livestock production by fostering the integration of big data, innovative analytics, and multiscale environmental monitoring. The primary objectives are to identify key mechanisms by which climate factors drive infectious disease dynamics, examine how novel data streams can refine risk prediction, and develop actionable insights for improving animal health, welfare, and system resilience to climate change. Critical questions include: Which climatic variables most strongly influence infectious disease patterns in animal populations? How can machine learning and advanced statistics improve prediction and management strategies? What are the knowledge gaps in microlevel versus macrolevel data integration, and what methodologies are most promising for future research?

To gather further insights in the scope of data-driven epidemiology and climate risk in animal agriculture, we welcome articles addressing, but not limited to, the following themes:

- Mechanistic links between climatic variables and infectious disease emergence or spread in livestock and domestic animals
- Advances in big data analytics, machine learning, and statistical modeling applied to animal health and production
- The role of environmental monitoring systems and local data streams in disease risk forecasting
- Microlevel observational and farm-based studies illuminating climate-disease interactions
- Case studies on antibiotic resistance development influenced by climate and epidemiological change
- Strategies for integrating multiscale data (farm, regional, global) to inform adaptive management in animal agriculture