Abstract
Bovine tuberculosis (bTB), caused by infection with Mycobacterium bovis is endemic in cattle in many countries worldwide including Ireland. The incidence rate in Ireland varies by herd and location and it is hoped that statistical disease-mapping models accounting for both spatio-temporal correlation and covariates might contribute towards explaining this variation. The final goal of this work is to produce a user-friendly, near real-time application that incorporates predictions from these models along with other metrics to serve as an early warning system to aid veterinarians in the field. Implementing different spatio-temporal random-effects models (e.g., negative binomial Besag-York-Mollié), we explored the association between covariates and the number of bTB cattle at an areal level by dividing Ireland into equally sized hexagons and determining the best fitting model. Data from the national bTB eradication programme was utilised. Models were fitted in a Bayesian framework and estimates were obtained using the integrated nested Laplace approximation (INLA) approach. We found that spatial models that accounted for spatial dependency offered a statistically significantly better fit in comparison to non-spatial versions where independence between hexagons was assumed. As an epidemiological tool, we have developed and improved on previous iterations, an interactive online dashboard to explore results. By developing a user-friendly, interactive dashboard, it allows the results from complex models to be presented in an accessible manner for veterinarians. The outstanding challenge is to fully automate the dashboard so that near real-time data is being used so that it is most beneficial for the bTB eradication programme.