Bovine besnoitiosis: Assessment of the diagnostic accuracy of three different tests using a Bayesian latent class model approach and clinical characterization of the disease.

Bovine besnoitiosis, a disease caused by the tissue cyst-forming apicomplexan Besnoitia besnoiti, is re-emerging in Europe, leading to significant impairment of health and production, as well as economic losses. The early detection of the disease is of the utmost importance for the implementation of effective control measures, yet this is a challenge due to the lack of specific early clinical signs. The objectives of our study were 1) to estimate the diagnostic accuracy of three tests to detect B. besnoiti in naturally exposed cattle (histopathology-skin (HIS-SK); PCR-skin (PCR-SK); and parallel PCR of nasal and scleroconjunctival swabs (PCR-NS-SC)) using a Bayesian latent class model (BLCM) and 2) to describe the clinical presentation of besnoitiosis in the studied animals. The study involved 54 adult Limousin cattle. Biosecurity measures were assessed and scored as medium. At clinical examination, a sire was diagnosed with a form of besnoitiosis between the end of the acute phase and the beginning of the chronic phase. Furthermore, 29 animals displaying a subclinical infection, characterized by the presence of scleroconjunctival cysts, were identified. The PCR-SK and PCR-NS-SC were able to detect B. besnoitia. The diagnostic performance of PCR-SK, PCR-NS-SC and HIS-SK was evaluated. The BLCM indicated that HIS-SK had the highest specificity (99.1 %, 95 % posterior probability interval PI: 96-100 %), while PCR-SK and PCR-NS-SC demonstrated higher sensitivities (91.0 %, 95 % PI: 68-100 %, and 85.0 %, 95 % PI: 67-100 %, respectively). The study concludes that the use of a parallel PCR-NS-SC could represent a viable alternative for the early detection of B. besnoiti, providing a less invasive method to monitor and control bovine besnoitiosis at the herd level.