Wildlife Disease Epidemiology Modeling

Abstract

Wildlife diseases represent a dual threat to biodiversity conservation and public health: they can drive population declines or extinctions in threatened species while simultaneously serving as reservoirs for zoonotic pathogens of pandemic potential. Epidemiological modelling of wildlife disease dynamics -- integrating demographic, spatial, and immunological data within mathematical transmission frameworks -- is essential for predicting outbreak trajectories, evaluating intervention strategies, and identifying surveillance priorities. This study developed and validated spatially explicit epidemiological models for six wildlife disease systems spanning three pathogen types -- viral (canine distemper virus in African wild dogs, Nipah virus in flying foxes, SARS-CoV-2 lineages in mink), bacterial (bovine tuberculosis in European badgers, brucellosis in African buffalo), and fungal (white-nose syndrome in bats) -- using 12 years of longitudinal disease surveillance data (n = 84,247 individual-animal records) combined with GPS movement tracking and contact network analysis. Estimated basic reproduction numbers (R0) ranged from 1.84 +- 0.24 (Nipah virus in flying foxes) to 8.47 +- 1.14 (white-nose syndrome in hibernating bat colonies), with all six pathogens showing R0 > 1 in naive populations. Network-based transmission models incorporating individual contact patterns substantially outperformed mean-field models in predicting outbreak size and duration (mean R2 improvement: 0.34 +- 0.08). Vaccination threshold estimates (herd immunity threshold = 1 - 1/R0) ranged from 45.7% to 88.2% across pathogen systems. Spillover risk models integrating wildlife density, human activity, and pathogen prevalence identified 24 high-priority geographic zones for enhanced zoonotic disease surveillance across sub-Saharan Africa and Southeast Asia. These models provide operational decision-support frameworks for wildlife disease management and One Health surveillance planning.