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Estimating animal movement contacts between holdings of different production types
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.
Linköping University, Department of Physics, Chemistry and Biology, Theoretical Biology . Linköping University, The Institute of Technology.
2010 (English)In: Preventive Veterinary Medicine, ISSN 0167-5877, E-ISSN 1873-1716, Vol. 95, no 1-2, 23-31 p.Article in journal (Refereed) Published
Abstract [en]

Animal movement poses a great risk for disease transmission between holdings. Heterogeneous contact patterns are known to influence the dynamics of disease transmission and should be included in modeling. Using pig movement data from Sweden as an example, we present a method for quantification of between holding contact probabilities based on different production types. The data contained seven production types: Sow pool center, Sow pool satellite, Farrow-to-finish, Nucleus herd, Piglet producer, Multiplying herd and Fattening herd. The method also estimates how much different production types will determine the contact pattern of holdings that have more than one type. The method is based on Bayesian analysis and uses data from central databases of animal movement. Holdings with different production types are estimated to vary in the frequency of contacts as well as in what type of holding they have contact with, and the direction of the contacts. Movements from Multiplying herds to Sow pool centers, Nucleus herds to other Nucleus herds, Sow pool centers to Sow pool satellites, Sow pool satellites to Sow pool centers and Nucleus herds to Multiplying herds were estimated to be most common relative to the abundance of the production types. We show with a simulation study that these contact patterns may also be expected to result in substantial differences in disease transmission via animal movements, depending on the index holding. Simulating transmission for a 1 year period showed that the median number of infected holdings was 1 (i.e. only the index holding infected) if the infection started at a Fattening herd and 2161 if the infection started on a Nucleus herd. We conclude that it is valuable to include production types in models of disease transmission and the method presented in this paper may be used for such models when appropriate data is available. We also argue that keeping records of production types is of great value since it may be helpful in risk assessments.

Place, publisher, year, edition, pages
Elsevier , 2010. Vol. 95, no 1-2, 23-31 p.
Keyword [en]
Markov Chain Monte Carlo; Animal databases; Animal movements; Production types
National Category
Natural Sciences
URN: urn:nbn:se:liu:diva-54834DOI: 10.1016/j.prevetmed.2010.03.002ISI: 000278281600004PubMedID: 20356640OAI: diva2:310639
Available from: 2010-04-15 Created: 2010-04-15 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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