A method of probability diagnostic assignment that applies bayes theorem for use in serologic diagnostics, using an example of Neospora caninum infection in cattle.


Probability functions identified for infected and uninfected cattle were Weibull and inverse gamma functions, respectively. Herd prevalence was estimated, and probabilities of N caninum infection were determined for cows with various ELISA values.

To develop a method of probability diagnostic assignment (PDA) that uses continuous serologic measures and infection prevalence to estimate the probability of an animal being infected, using Neospora caninum as an example.

196 N caninum-infected beef and dairy cattle and 553 cattle not infected with N caninum; 50 dairy cows that aborted and 50 herdmates that did not abort.

Probability density functions corresponding to distributions of N caninum kinetic ELISA results from infected and uninfected cattle were estimated by maximum likelihood methods. Maximum likelihood methods also were used to estimate N caninum infection prevalence in a herd that had an excessive number of abortions. Density functions and the prevalence estimate were incorporated into Bayes formula to calculate the conditional probability that a cow with a particular ELISA value was infected with N caninum.

Use of PDA offers an advantage to clinicians and diagnosticians over traditional seronegative or seropositive classifications used as a proxy for infection status by providing an assessment of the actual probability of infection. The PDA permits use of all diagnostic information inherent in an assay, thereby eliminating a need for estimates of sensitivity and specificity. The PDA also would have general utility in interpreting results of any diagnostic assay measured on a continuous or discrete scale.

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