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Using auxiliary information to improve wildlife disease surveillance when infected animals are not detected: a Bayesian approach

PLoS ONE

By:
, , ,
DOI: 10.1371/journal.pone.0089843

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Abstract

There are numerous situations in which it is important to determine whether a particular disease of interest is present in a free-ranging wildlife population. However adequate disease surveillance can be labor-intensive and expensive and thus there is substantial motivation to conduct it as efficiently as possible. Surveillance is often based on the assumption of a simple random sample, but this can almost always be improved upon if there is auxiliary information available about disease risk factors. We present a Bayesian approach to disease surveillance when auxiliary risk information is available which will usually allow for substantial improvements over simple random sampling. Others have employed risk weights in surveillance, but this can result in overly optimistic statements regarding freedom from disease due to not accounting for the uncertainty in the auxiliary information; our approach remedies this. We compare our Bayesian approach to a published example of risk weights applied to chronic wasting disease in deer in Colorado, and we also present calculations to examine when uncertainty in the auxiliary information has a serious impact on the risk weights approach. Our approach allows “apples-to-apples” comparisons of surveillance efficiencies between units where heterogeneous samples were collected

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Using auxiliary information to improve wildlife disease surveillance when infected animals are not detected: a Bayesian approach
Series title:
PLoS ONE
DOI:
10.1371/journal.pone.0089843
Volume
9
Issue:
3
Year Published:
2014
Language:
English
Publisher:
PLoS
Contributing office(s):
National Wildlife Health Center
Description:
9 p.
Larger Work Type:
Article
Larger Work Subtype:
Journal Article