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.

Assessing intrastate shipments from interstate data and expert opinion

“…Thirdly, we implemented three different functional forms for the spatial kernel that model how the probability of destination decays with distance. In countries where complete data is available, shipments typically occur at shorter distances [ 16 , 41 , 42 ], and it is therefore important when modelling outbreaks of infectious diseases to have accurate estimations of the kernel’s behaviour at distances where the majority of shipments occurs. Yet, it is equally important to accurately predict shipments at long distances since these can spread pathogens to previously uninfected areas and spark new local outbreaks [ 3 , 43 ].…”

“…Furthermore, these kernels have closed form solutions to a reparametrization that can be used instead of parameters a and b which hold no readily interpretable information. Previous studies have reparametrized K 1 by moment statistics [16,17], but here we use a different approach for two main reasons. First, these moment statistics provide little intuitive understanding of the behaviour of the kernels and are hence only marginally more informative than a and b. Secondly, K 2 and K 3 include shapes that lack finite moments and it is therefore not possible to define finite quantities for all possible shapes.…”

“…Survey data have previously been used to identify interstate shipping patterns using Interstate Certificates of Veterinary Inspection (ICVI) issued by animal health authorities when animals are shipped across state boundaries [ 15 ]. However, shipping patterns in countries with complete data typically show high frequency of short-distance shipments [ 16 ], and a similar pattern in the USA would result in a large number of intrastate shipments. As such, there is a need for methods to extrapolate from existing data to predict complete shipment patterns.…”

Performance enhancement of software defined cellular LTE and internet-of-things networks

“…For example, [139] concentrates on the trend in Australia, while [135] and [140] study its pattern in France and East Africa respectively. The works in [135,141,142] investigate the spatiotemporal variations in livestock movement and the spread of infectious diseases over a relatively long duration.…”

“…In [141], the authors analysed nine cattle movement models by comparing the distance between farms using a hierarchical Bayesian framework. The models were evaluated using different established criteria to measure their accuracy.…”