The aim of the present study was to calculate the sensitivity (Se) and specificity (Sp) of the single cervical tuberculin test (SCT), rapid lateral flow test (RLFT), and real-time polymerase chain reaction (RT-PCR) for the diagnosis of Mycobacterium bovis (M. bovis) infection in Egyptian dairy cattle herds within a Bayesian framework. The true M. bovis infection within-herd prevalence was assessed as a secondary objective. Data on the test results of SCT, RLFT, and RT-PCR for the detection of M. bovis were available from 245 cows in eleven herds in six major governorates in Egypt. A Bayesian latent class model was built for the estimation of the characteristics of the three tests. Our findings showed that Se of SCT (0.93 (95% Posterior credible interval (PCI): 0.89-0.93)) was higher than that of RT-PCR (0.83 (95% PCI: 0.28-0.93)) but was similar to the Se of RLFT (0.93 (95% PCI: 0.31-0.99)). On the contrary, SCT showed the lowest Sp estimate (0.60 (95% PCI: 0.59-0.65)), whereas Sp estimates of RT-PCR (0.99 (95% PCI: 0.95-1.00)) and RLFT (0.99 (95% PCI: 0.95-1.00)) were comparable. The true prevalence of M. bovis ranged between 0.07 and 0.71. In conclusion, overall, RT-PCR and RLFT registered superior performance to SCT, making them good candidates for routine use in the Egyptian bovine tuberculosis control program.
Keywords: Bayesian modelling; Mycobacterium bovis; dairy cows; rapid lateral flow; real-time PCR; test accuracy.