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Estimation of distance related probability of animal movements between holdings and implications for disease spread modeling
Linköping University, Department of Physics, Chemistry and Biology, Theoretical Biology . Linköping University, The Institute of Technology.ORCID iD: 0000-0001-7856-2925
School of Mathematics and Statistics, University of New South Wales, Sydney 2052, Australia.
Department of Disease Control and Epidemiology, SVA, National Veterinary Institute, 751 89 Uppsala, Sweden.
Research Centre of Systems Biology, Ecological Modelling, University of Skövde, 541 28 Skövde, Sweden.
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2009 (English)In: Preventive Veterinary Medicine, ISSN 0167-5877, E-ISSN 1873-1716, Vol. 91, no 2-4, 85-94 p.Article in journal (Refereed) Published
Abstract [en]

Between holding contacts are more common over short distances and this may have implications for the dynamics of disease spread through these contacts. A reliable estimation of how contacts depend on distance is therefore important when modeling livestock diseases. In this study, we have developed a method for analyzing distant dependent contacts and applied it to animal movement data from Sweden. The data were analyzed with two competing models. The first model assumes that contacts arise from a purely distance dependent process. The second is a mixture model and assumes that, in addition, some contacts arise independent of distance. Parameters were estimated with a Bayesian Markov Chain Monte Carlo (MCMC) approach and the model probabilities were compared. We also investigated possible between model differences in predicted contact structures, using a collection of network measures. We found that the mixture model was a much better model for the data analyzed. Also, the network measures showed that the models differed considerably in predictions of contact structures, which is expected to be important for disease spread dynamics. We conclude that a model with contacts being both dependent on, and independent of, distance was preferred for modeling the example animal movement contact data.

Place, publisher, year, edition, pages
Elsevier , 2009. Vol. 91, no 2-4, 85-94 p.
Keyword [en]
Markov Chain Monte Carlo, Mixture models, Model selection, Animal movements, Disease transmission, Network analysis
National Category
Natural Sciences
URN: urn:nbn:se:liu:diva-20742DOI: 10.1016/j.prevetmed.2009.05.022OAI: diva2:235963
Available from: 2009-09-18 Created: 2009-09-18 Last updated: 2016-08-31
In thesis
1. Spatial Spread of Organisms: Modeling ecological and epidemiological processes
Open this publication in new window or tab >>Spatial Spread of Organisms: Modeling ecological and epidemiological processes
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis focuses on the spread of organisms in both ecological and epidemiological contexts. In most of the studies presented, displacement is modeled with a spatial kernel function, which is characterized by scale and shape. These are measured by the net squared displacement (or kernel variance) and kurtosis, respectively. If organisms disperse by the assumptions of a random walk or correlated random walk, a Gaussian shaped kernel is expected. Empirical studies often report deviations from this, and commonly leptokurtic distributions are found, often as a result of heterogeneity in the dispersal process.

In the studies presented in two of the included papers, the importance of the kernel shape is tested, by using a family of kernels where the shape and scale can be separated effectively. Both studies utilize spectral density approaches for modeling the spatial environment. It is concluded that the shape is not important when studying the population distribution in a habitat/matrix context. The shape is however important when looking at the invasion of organisms in a patchy environment, when the arrangement of patches deviates from randomly distributed. The introduced method for generating patch distribution is also compared to empirical distributions of patches (farms and old trees). Here it is concluded that the assumptions used for modeling of the spatial environment are consistent with the observed patterns. These assumptions include fractal properties such that the same aggregational patterns are found at different scales.

In a series of papers, movements of animals are considered as vectors for between-herd disease spread. The studies are based on data found in databases held by the Swedish Board of Agricultural (SJV), consisting of reported movements, as well as farm location and characteristics. The first study focuses on the distance related probability of contacts between herds. In the following papers, the analysis is expanded to include production type and herd size. Movement data of pigs (and cattle in Paper I) are analyzed with Bayesian models, implemented with Markov Chain Monte Carlo (MCMC). This is a flexible approach that allows for parameter estimations of complex models, and at the same time includes parameter uncertainty.

In Paper IV, the effects of the included factors are investigated. It is shown that all three factors (herd size, production type structure and distance related probability of contacts) are expected to influence disease spread dynamics, however the production type structure is found to be the most important factor. This emphasizes the value of keeping such information in central databases. The models presented can be used as support for risk analysis and disease tracing. However, data reliability is always a problem, and implementation may be improved with better quality data.

The thesis also shows that utilizing spatial kernels for description of the spatial spread of organisms is an appropriate approach. However, these kernels must be flexible and flawed assumptions about the shape may lead to erroneous conclusions. Hence, the joint distribution of kernel shape and scale should be estimated. The flexibility of Bayesian analysis, implemented with MCMC techniques, is a good approach for this, and further allows for implementation of more complex models where other factors may be included.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2010. 54 p.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1311
Spatial kernel, Spatially explicit modeling, Disease transmission, Animal movements
National Category
Natural Sciences
urn:nbn:se:liu:diva-54839 (URN)978-91-7393-401-5 (ISBN)
Public defence
2010-05-07, Planck, Hus F, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Available from: 2010-04-15 Created: 2010-04-15 Last updated: 2016-08-31Bibliographically approved

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