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

Wildlife diseases pose a significant threat to public health, livestock, and biodiversity conservation. In this context, spatial epidemiology offers a robust framework for elucidating disease dynamics and informing policy-making and disease management. The workflow in spatial epidemiology involves three main steps: (1) descriptive analysis of spatial dynamics; (2) exploration of the observed dynamics; and (3) prediction of pathogen distribution and spread. Descriptive analysis, such as disease mapping or clustering analysis, focuses on identifying spatial patterns, enabling the formulation of hypotheses regarding potential risk factors driving disease dynamics. Subsequently, risk factor analysis associates the presence of the pathogen with ecological or anthropogenic factors to explain its spatiotemporal dynamics. Furthermore, the circulation and spread of endemic and emerging pathogens can be further understood at a finer resolution. System-based Susceptible-Infected-Recovered (SIR) models, such as diffusion and lattice-based, allow for the parametrization of pathogen spread at the population level, while individual or group-based SIR models, such as metapopulation or network approaches, consider the impact of host behavior and social structure in disease dynamics. Molecular epidemiology, through the identification of genetic variants of pathogens and the mapping of their phylogenetic relationships, aids in understanding outbreak origins and the epidemiological linkages among hosts. Finally, for emerging pathogens, the knowledge about disease dynamics should be implemented in predictive modeling for anticipating disease spread. In this regard, ecological niche models and species distribution models project potential pathogen distributions through their association with ecological or anthropogenic factors, whereas simulations parameterize the processes of pathogen spread to predict its expansion over time. System-based simulations focus on population-level dynamics, while agent-based simulations incorporate individual-level dynamics, offering detailed insights into disease spread and control measures. Consequently, the integration of descriptive analyses, exploratory procedures, and predictive models provides a robust framework for addressing the challenges posed by wildlife diseases and developing management and control measures. The interdisciplinary approach in spatial epidemiology is crucial for mitigating the impact of wildlife diseases on public health and biodiversity, emphasizing the need for collaboration among ecologists, epidemiologists, statisticians, and policy-makers.