Please use this identifier to cite or link to this item:
Appears in Collections:Aquaculture Journal Articles
Peer Review Status: Refereed
Title: Estimating epidemiological parameters for bovine tuberculosis in British cattle using a Bayesian partial-likelihood approach
Authors: O'Hare, Anthony
Orton, Richard
Bessell, Paul R
Kao, Rowland R
Contact Email:
Issue Date: 9-Apr-2014
Publisher: Royal Society
Citation: O'Hare A, Orton R, Bessell PR & Kao RR (2014) Estimating epidemiological parameters for bovine tuberculosis in British cattle using a Bayesian partial-likelihood approach, Proceedings of the Royal Society B: Biological Sciences, 281 (1783), Art. No.: 20140248.
Abstract: 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.
Type: Journal Article
DOI Link:
Rights: Copyright 2014 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License, which permits unrestricted use, provided the original author and source are credited.
Affiliation: Complex Systems
University of Glasgow
University of Glasgow
University of Glasgow

Files in This Item:
File Description SizeFormat 
OHare_RSB_2015.pdf987.38 kBAdobe PDFView/Open

This item is protected by original copyright

Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

If you believe that any material held in STORRE infringes copyright, please contact providing details and we will remove the Work from public display in STORRE and investigate your claim.