title: Spatiotemporal Variation in Distance Dependent Animal Movement Contacts: One Size Doesn't Fit All.
abstract: The structure of contacts that mediate transmission has a pronounced effect on the outbreak dynamics of infectious disease and simulation models are powerful tools to inform policy decisions. Most simulation models of livestock disease spread rely to some degree on predictions of animal movement between holdings. Typically, movements are more common between nearby farms than between those located far away from each other. Here, we assessed spatiotemporal variation in such distance dependence of animal movement contacts from an epidemiological perspective. We evaluated and compared nine statistical models, applied to Swedish movement data from 2008. The models differed in at what level (if at all), they accounted for regional and/or seasonal heterogeneities in the distance dependence of the contacts. Using a kernel approach to describe how probability of contacts between farms changes with distance, we developed a hierarchical Bayesian framework and estimated parameters by using Markov Chain Monte Carlo techniques. We evaluated models by three different approaches of model selection. First, we used Deviance Information Criterion to evaluate their performance relative to each other. Secondly, we estimated the log predictive posterior distribution, this was also used to evaluate their relative performance. Thirdly, we performed posterior predictive checks by simulating movements with each of the parameterized models and evaluated their ability to recapture relevant summary statistics. Independent of selection criteria, we found that accounting for regional heterogeneity improved model accuracy. We also found that accounting for seasonal heterogeneity was beneficial, in terms of model accuracy, according to two of three methods used for model selection. Our results have important implications for livestock disease spread models where movement is an important risk factor for between farm transmission. We argue that modelers should refrain from using methods to simulate animal movements that assume the same pattern across all regions and seasons without explicitly testing for spatiotemporal variation.
authors.affiliation: Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden.
authors.forename: Peter
authors.initials: P
authors.lastname: Brommesson
authors.affiliation: Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden.
authors.forename: Uno
authors.initials: U
authors.lastname: Wennergren
authors.affiliation: Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden.
authors.forename: Tom
authors.initials: T
authors.lastname: Lindström
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D002417:Cattle; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D009068:Movement; D012621:Seasons; D062211:Spatio-Temporal Analysis