Abstract
Slaughterhouse surveillance plays a key role in disease detection, especially for zoonotic diseases such as Bovine Tuberculosis (bTB). Premises are under slaughterhouse surveillance if a sufficient number of their animals reach slaughterhouses in order to detect an outbreak. Since the probability of detecting an outbreak is dependent on the number of animals sent to slaughterhouses, total number of animals in the farm, and the prevalence of disease, premises that send animals directly to abattoirs might not be under surveillance, while premises that do not send animals directly to abattoirs might be (through indirect contacts). Therefore, social network analysis is essential to estimate the coverage of one of the main strategies for the surveillance of bTB and could help identify the premises and/or regions that are within the reach of this strategy and those that are not. This study aimed to evaluate the coverage of the Bovine Tuberculosis slaughterhouse surveillance in a state of Brazil. We used a two-year animal movement database, hypergeometric distribution, social network analysis, and stochastic simulations to estimate the probability that at least one infected animal of a premises would reach a slaughterhouse. We considered that premises are under slaughterhouse surveillance if this probability is at least 80%. Results showed that 45% of premises and 86.5% of cattle were covered by the slaughterhouse surveillance in the state. These results can be used to identify regions and premises that are not within the reach of this strategy, prompting stakeholders to develop other surveillance components to fill the gap. Results could also be integrated with the probability of detecting bTB lesions in slaughterhouses to estimate the sensitivity of a slaughterhouse surveillance system. This approach can also be adapted to estimate the coverage of slaughterhouse surveillance for other diseases.