From patterns to predictions: a framework for the spatial epidemiology of wildlife diseases

Wildlife diseases pose a significant threat to biodiversity conservation, public health, and livestock. Spatial epidemiology provides a robust framework to understand disease dynamics and guide policy and management. However, the rapid proliferation of spatial methods has largely occurred in isolation across disciplines, leaving the field without a unified framework that connects these approaches and clarifies their strengths, limitations, and applications. The spatial epidemiology workflow includes three main steps: 1) descriptive analysis of spatial patterns; 2) exploration of observed patterns through explicative analyses; and 3) prediction of pathogen distribution and spread. Descriptive analysis, such as disease mapping and clustering analysis, identifies spatial patterns and informs hypotheses about potential risk factors. Subsequently, explicative analyses can link pathogen presence to ecological or anthropogenic drivers and explain spatiotemporal dynamics. Drivers of pathogen transmission can be further understood through mechanistic models, ranging from population‐level Susceptible‐Infected‐Removed (SIR) frameworks to individual‐ or group‐based SIR approaches such as metapopulation and network models that can incorporate host behavior and social structure. Molecular epidemiology, through genetic variant identification and phylogenetic mapping, can identify outbreak origins and host linkages. Lastly, predictions of pathogen distribution and spread are enabled by ecological niche models and species distribution models, which project potential pathogen distributions based on ecological, geographical, and/or anthropogenic factors, and by mechanistic simulations that parameterize pathogen spread processes to predict expansion over space and time. System‐based simulations focus on population‐level dynamics, while agent‐based simulations incorporate individual‐level dynamics, offering detailed insights into pathogen spread and control measures. Consequently, the integration of descriptive analyses, explicative procedures, and predictive models provides a robust framework for addressing the challenges posed by wildlife diseases and developing management and control measures.