The ongoing explosion of fine-resolution movement data in animal systems provides a unique opportunity to empirically quantify spatial, temporal, and individual variation in transmission risk and improve our ability to forecast disease outbreaks. However, we lack a generalizable framework that can leverage movement data to quantify transmission risk and how it affects pathogen invasion and persistence on heterogeneous landscapes. We developed a flexible framework “Movement-driven modeling of spatio-temporal infection risk” (MoveSTIR) that leverages diverse data on animal movement to derive metrics of direct and indirect contact by decomposing transmission into constituent processes of contact formation and duration and pathogen deposition and acquisition. We use MoveSTIR to demonstrate that ignoring fine-scale animal movements on actual landscapes can mis-characterize transmission risk and epidemiological dynamics. MoveSTIR unifies previous work on epidemiological contact networks and can address applied and theoretical questions at the nexus of movement and disease ecology.

Environment drives the host movements that shape pathogen transmission through three mediating processes: host density, host mobility, and contact. These processes combine with pathogen life-history to give rise to an “epidemiological landscape” that determines spatial patterns of pathogen transmission. Yet despite its central role in transmission, strategies for predicting the epidemiological landscape from real-world data remain limited. Here, we develop the epidemiological landscape as an interface between movement ecology and spatial epidemiology. We propose a movement-pathogen pace-of-life heuristic for prioritizing the landscape’s central processes by positing that spatial dynamics for fast pace-of-life pathogens are best-approximated by the spatial ecology of host contacts; spatial dynamics for slower pace-of-life pathogens are best approximated by host densities; and spatial dynamics for pathogens with environmental reservoirs reflect a convolution of those densities with the spatial configuration of environmental reservoir sites. We then identify mechanisms that underpin the epidemiological landscape and match each mechanism to emerging tools from movement ecology. Finally, we outline workflows for describing the epidemiological landscape and using it to predict subsequent patterns of pathogen transmission. Our framework links transmission to environmental context, providing a scaffold for mechanistically understanding how environmental context can generate and shift existing patterns in spatial epidemiology.