Validation of diagnostic tests for detection of avian influenza in vaccinated chickens using Bayesian analysis

Vaccination is an attractive tool for the prevention of outbreaks of highly pathogenic avian influenza in domestic birds. It is known, however, that under certain circumstances vaccination may fail to prevent infection, and that the detection of infection in vaccinated birds can be problematic. Here, we investigate the characteristics of three serological tests (immunofluorescent antibody test (iIFAT), neuraminidase inhibition (NI) assay, and NS1 ELISA) that are able to differentiate infected from vaccinated animals. To this end, data of H7N7 infection experiments are analyzed using Bayesian methods of inference. These Bayesian methods enable validation of the tests in the absence of a gold standard, and allow one to take into account that infected birds do not always develop antibodies after infection. The results show that the N7 iIFAT and the NI assay have sensitivities for detecting antibodies of 0.95 (95% CI: 0.89–0.98) and 0.93 (95% CI: 0.78–0.99), but substantially lower sensitivities for detecting infection: 0.64 (95% CI: 0.52–0.75) and 0.63 (95% CI: 0.49–0.75). The NS1 ELISA has a low sensitivity for both detecting antibodies 0.55 (95% CI: 0.34–0.74) and infection 0.42 (95% CI: 0.28–0.56). The estimated specificities of the N7 iIFAT and the NI assay are 0.92 (95% CI: 0.87–0.95) and 0.91 (95% CI: 0.85–0.95), and 0.82 (95% CI: 0.74–0.87) for the NS1 ELISA. Additionally, our analyses suggest a strong association between the duration of virus excretion of infected birds and the probability to develop antibodies.