Premium
A model for leveraging animal movement to understand spatio‐temporal disease dynamics
Author(s) -
Wilber Mark Q.,
Yang Anni,
Boughton Raoul,
Manlove Kezia R.,
Miller Ryan S.,
Pepin Kim M.,
Wittemyer George
Publication year - 2022
Publication title -
ecology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.852
H-Index - 265
eISSN - 1461-0248
pISSN - 1461-023X
DOI - 10.1111/ele.13986
Subject(s) - leverage (statistics) , transmission (telecommunications) , temporal scales , nexus (standard) , computer science , ecology , movement (music) , disease transmission , spatial epidemiology , landscape epidemiology , data science , biology , artificial intelligence , landscape ecology , telecommunications , habitat , epidemiology , medicine , philosophy , virology , embedded system , aesthetics
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.