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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