Spatio-temporal models of bovine tuberculosis in the Irish cattle population, 2012-2019

Abstract

Bovine tuberculosis (bTB) is an important zoonotic disease which has serious and sometimes fatal effects on both human and non-human animals. In many countries it is endemic in the cattle population and has a considerable economic impact through losses in productivity and impacts on trade. 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. Methods: Ireland was divided into equally sized hexagons for computational efficiency (n = 997). Different spatio-temporal random-effects models (e.g. negative binomial Besag-York-Mollié) were explored, using comprehensive data from the national bTB eradication programme to examine the association between covariates and the number of bTB cattle. Leveraging a Bayesian framework, model parameter estimates were obtained using the integrated nested Laplace approximation (INLA) approach. Exceedance probabilities were calculated to identify spatial clusters of cases. Results: Models accounting for spatial correlation significantly improved model fit in comparison to non-spatial versions where independence between regions was assumed. In our final model at hexagon level, the number of cattle (IR = 1.142, CrI: 1.108 – 1.177 per 1000), the capture of badgers (IR = 5.951, CrI: 4.482 – 7.912), percentage of forest cover (IR = 1.031, CrI: 1.020 – 1.042) and number of farm fragments (IR = 1.012, CrI: 1.009 – 1.015 per 10 fragments) were all associated with an increased incidence of bTB. Habitat suitability for badgers, percentage of dairy herds and the number of cattle movements into the herd were not. As an epidemiological tool and to suggest future work, an interactive online dashboard was developed to monitor disease progression and disseminate results to the general public. Conclusion: Accounting for spatial correlation is an important consideration in disease mapping applications and is often ignored in statistical models examining bTB risk factors. Over time, the same regions in Ireland generally show highest incidences of bTB and allocation of more resources to these areas may be needed to combat the disease. This study highlights national bTB incidence rates. Shifting from national level analysis to smaller geographical regions may help identify localised high-risk areas.