Download 46. Bayesian methods applied to FMD serological results

Appendix 46
Potential application of Bayesian probability diagnostic assignment (BPDA) method
to predict FMDV infection from serologic results
Wesley O. Johnson1*, Mark C. Thurmond2, and Andrés M. Perez2
1
Department of Statistics, University of California, Davis, CA
2
FMD Modeling and Surveillance Laboratory, Department of Medicine and Epidemiology, School
of Veterinary Medicine, University of California, Davis, CA
Introduction: In order to minimize the destruction of livestock and associated consequences
of FMD, vaccination is now considered to be an acceptable strategy for the control and
eradication of FMD. Some drawbacks exist, however, to use of vaccination. These problems
include 1) the need for serologic testing following vaccination to identify infected animals and
2) the lack of accurate means to discriminate between infected-vaccinated animals and
uninfected-vaccinated animals. The latter problem relates both to the failure of some infected
animals to respond to vaccination, and thus not show any detectable antibodies to structural
proteins (false negative), and to the presence in some uninfected-vaccinated animals of
antibodies to non-structural proteins (false positive). Thus, there is likely some small, but as
yet not well known, probability of a false negative result and of a false positive result. A
general recommendation is that the issue of false positive and false negative responses would
(somehow) be resolved on a herd basis, but we are not aware of any methodology proposed
to accomplish this.
Materials and Methods: We have developed a statistical methodology that estimates the
probability that an animal is infected with a specified agent given the specific antibody
concentration (ELISA s/p value). The approach also permits estimation of the prevalence (and
95% prediction interval of the prevalence) of infection in a herd, based on serologic values for
a representative sample of animals in the herd. The method is referred to as probability
diagnostic assignment (PDA), and has been extended to a fully Bayesian format (BPDA). We
have developed the method for Neospora caninum infection in cattle. The method utilizes two
distributions of serologic values, one for animals that are infected and one for animals that are
not infected. No cutoff values are used, thus there is no need for estimates of sensitivity or
specificity, and the full scale of information inherent in the values of the assay is used.
Consequently, information in the serologic values is not limited to two dichotomous
representations of ‘positive’ or ‘negative’; rather, the full range of serologic values is utilized.
Several parameters are estimated, including the probability that a given animal is infected and
the probability that the herd is infected (estimated prevalence of infection in the herd).
Results: In the absence of specific ELISA data for FMD-vaccinated, unvaccinated, infected,
and uninfected animals, we have not yet had the opportunity to assess whether the Bayesian
PDA might have application in predicting infection status of vaccinated animals.
Discussion: We will provide an illustration of the potential application of the BPDA to predict
the probability of FMDV infection in an animal and in a herd, using serologic values and other
covariate information.
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