A framework for leveraging animal movement to understand spatio-temporal
disease dynamics
Abstract
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.