e-ISSN 1828-1427 |
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Rivista trimestrale di Sanità
Pubblica Veterinaria edita dall'Istituto Zooprofilattico Sperimentale
dell'Abruzzo e del Molise G.
Caporale'
A quarterly journal devoted to veterinary public health, veterinary science and medicine published by the Istituto Zooprofilattico Sperimentale dell’Abruzzo e del Molise ‘G. Caporale’ in Teramo, Italy |
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ISSUES ONLINE
2007
- Volume 43 (3)
July-September |
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Matthew
L. Farnsworth, Jennifer A. Hoeting, N. Thompson Hobbs, Mary M. Conner,
Kenneth P. Burnham, Lisa L. Wolfe, Elizabeth S. Williams, David M.
Theobald & Michael W. Miller |
The
role of geographic information systems in wildlife landscape epidemiology:
models of chronic wasting disease in Colorado mule deer |
581-593 |
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Summary
The
authors present findings from two landscape epidemiology studies of
chronic wasting disease (CWD) in northern Colorado mule deer (Odocoileus
hemionus). First, the effects of human land use on disease prevalence
were explored by formulating a set of models estimating CWD prevalence
in relation to differences in human land use, sex and geographic location.
Prevalence was higher in developed areas and among male deer suggesting
that anthropogenic influences (changes in land use), differences in
exposure risk between sexes and landscape-scaled heterogeneity are
associated with CWD prevalence. The second study focused on identifying
scales of mule deer movement and mixing that had the greatest influence
on the spatial pattern of CWD in north-central Colorado. The authors
hypothesised that three scales of mixing individual, winter
subpopulation and summer subpopulation might control spatial
variation in disease prevalence. A fully Bayesian hierarchical model
was developed to compare the strength of evidence for each mixing
scale. Strong evidence was found indicating that the finest mixing
scale corresponded best to the observed spatial distribution of CWD
prevalence. This analysis demonstrates how information on the scales
of spatial processes that generate observed patterns can be used to
gain insight into the epidemiology of wildlife diseases when process
data are sparse or unavailable.
Keywords
Bayesian
hierarchical model, Chronic wasting disease, Disease ecology, Geographic
information system, Land-use change, Model selection, Spatial epidemiology,
Spatial model, Spatial scale.
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Full article
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