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Estimation of the diagnostic performance of serological tests often relies on another test assumed as a reference or on samples of known infection status, yet both are seldom available for emerging pathogens in wildlife. Longitudinal disease serological data can be analysed through multi‐event capture–mark–recapture (MECMR) models accounting for the uncertainty in state assignment, allowing us to estimate epidemiological parameters such as incidence and mortality. We hypothesized that by estimating the uncertainty in state assignment, MECMR models estimate the diagnostic performance of serological tests for rabbit haemorrhagic disease virus (RHDV) and myxoma virus (MYXV). We evaluated this hypothesis on longitudinal serological data of three tests of RHDV and one test of MYXV in two populations of the European rabbit (Oryctolagus cuniculus algirus). First, we selected the optimal cut‐off threshold for each test using finite mixture models, a reference method not relying on reference tests or samples. Second, we used MECMR models to compare the diagnostic sensitivity (Se) and specificity (Sp) of the three tests for RHDV. Third, we compared the estimates of diagnostic performance by MECMR and finite mixture models across a range of cut‐off values. The MECMR models showed that the RHDV test employing GI.2 antigens (Se: 100%) outperformed two tests employing GI.1 antigens (Se: 21.7% ± 8.6% and 8.7% ± 5.9%). At their selected cut‐offs (2.0 for RHDV GI.2 and 2.4 for MYXV), the estimates of Se and Sp were concordant between the MECMR and finite mixture models. Over the duration of the study (May 2018 to September 2020), the monthly survival of European rabbits seropositive for MYXV was significantly higher than that of seronegative rabbits (82.7% ± 4.9% versus 61.5% ± 12.7%) at the non‐fenced site. We conclude that MECMR models can reliably estimate the diagnostic performance of serological tests for RHDV and MYXV in European rabbits. This conclusion could extend to other diagnostic tests and host‒pathogen systems. Longitudinal disease surveillance data analysed through MECMR models allow the validation of diagnostic tests for emerging pathogens in novel host species while simultaneously estimating epidemiological parameters.