Using auxiliary information to improve wildlife disease surveillance when infected animals are not detected: a Bayesian approach.
PLoS One
; 9(3): e89843, 2014.
Article
em En
| MEDLINE
| ID: mdl-24676479
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Vigilância da População
/
Doenças dos Animais
/
Animais Selvagens
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
/
Screening_studies
Limite:
Animals
País/Região como assunto:
America do norte
Idioma:
En
Revista:
PLoS One
Assunto da revista:
CIENCIA
/
MEDICINA
Ano de publicação:
2014
Tipo de documento:
Article
País de afiliação:
Estados Unidos