A model for leveraging animal movement to understand spatio-temporal disease dynamics

Authors

Mark Q. Wilber, University of TennesseeFollow
Anni Yang, USDA APHIS Wildlife ServicesFollow
Raoul Boughton, Archbold Biological Station, Buck Island Ranch, Lake Placid, FloridaFollow
Kezia R. Manlove, Utah State UniversityFollow
Ryan S. Miller, USDA APHISFollow
Kim M. Pepin, USDA APHIS Wildlife ServicesFollow
George Wittemyer, Colorado State UniversityFollow

ORCID IDs

Mark Q. Wilber https://orcid.org/0000-0002-8274-8025

Kim M. Pepin https://orcid.org/0000-0002-9931-8312

Document Type

Article

Date of this Version

2022

Citation

Ecology Letters. 2022;25:1290–1304.

DOI: 10.1111/ele.13986

Comments

U.S. government work

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 model that can leverage movement data to quantify transmission risk and how it affects pathogen invasion and persistence on heterogeneous landscapes. We developed a flexible model ‘Movement-driven modelling 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.