Estimating epidemiological parameters for bovine tuberculosis in British cattle using a Bayesian partial-likelihood approach.
Fitting models with Bayesian likelihood-based parameter inference is becoming increasingly important in infectious disease epidemiology. Detailed datasets present the opportunity to identify subsets of these data that capture important characteristics of the underlying epidemiology. One such dataset describes the epidemic of bovine tuberculosis (bTB) in British cattle, which is also an important exemplar of a disease with a wildlife reservoir (the Eurasian badger). Here, we evaluate a set of nested dynamic models of bTB transmission, including individual- and herd-level transmission heterogeneity and assuming minimal prior knowledge of the transmission and diagnostic test parameters. We performed a likelihood-based bootstrapping operation on the model to infer parameters based only on the recorded numbers of cattle testing positive for bTB at the start of each herd outbreak considering high- and low-risk areas separately. Models without herd heterogeneity are preferred in both areas though there is some evidence for super-spreading cattle. Similar to previous studies, we found low test sensitivities and high within-herd basic reproduction numbers (R0), suggesting that there may be many unobserved infections in cattle, even though the current testing regime is sufficient to control within-herd epidemics in most cases. Compared with other, more data-heavy approaches, the summary data used in our approach are easily collected, making our approach attractive for other systems.
Boyd Orr Centre for Population and Ecosystem Health, Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences University of Glasgow, , Glasgow G61 1QH, UK, The Roslin Institute, The University of Edinburgh, , Easter Bush, Edinburgh, EH25 9RG, UK.
A
A
O'Hare
R J
RJ
Orton
P R
PR
Bessell
R R
RR
Kao
D000818:Animals; D050936:Basic Reproduction Number; D001499:Bayes Theorem; D002417:Cattle; D004196:Disease Outbreaks / Q000662:veterinary*; D016013:Likelihood Functions; D008962:Models, Theoretical; D014380:Tuberculosis, Bovine / Q000453:epidemiology* / Q000635:transmission*; D006113:United Kingdom / Q000453:epidemiology
bootstrap; ergodic; nonlinear dynamics; partial likelihood