Coupling models of cattle and farms with models of badgers for predicting the dynamics of bovine tuberculosis (TB)

Original Paper

Abstract

Bovine Tuberculosis (TB) is a major problem for the agricultural industry in several countries. TB can be contracted and spread by species other than cattle and this can cause a problem for disease control. In the UK and Ireland, badgers are a recognised reservoir of infection and there has been substantial discussion about potential control strategies. We present a coupling of individual based models of bovine TB in badgers and cattle, which aims to capture the key details of the natural history of the disease and of both species at approximately county scale. The model is spatially explicit it follows a very large number of cattle and badgers on a different grid size for each species and includes also winter housing. We show that the model can replicate the reported dynamics of both cattle and badger populations as well as the increasing prevalence of the disease in cattle. Parameter space used as input in simulations was swept out using Latin hypercube sampling and sensitivity analysis to model outputs was conducted using mixed effect models. By exploring a large and computationally intensive parameter space we show that of the available control strategies it is the frequency of TB testing and whether or not winter housing is practised that have the most significant effects on the number of infected cattle, with the effect of winter housing becoming stronger as farm size increases. Whether badgers were culled or not explained about 5 %, while the accuracy of the test employed to detect infected cattle explained less than 3 % of the variance in the number of infected cattle.

Keywords

Agent based models Simulation modelling Spatial epidemiology Control strategies Disease eradication Confronting models with data 

Notes

Acknowledgments

We thank Ian Boyd for comments on an earlier draft, and William Wint for providing us data on cattle movement. We also thank Harry Constantinides for invaluable help during model coding in C#. Comments from two anonymous reviewers considerably improved an earlier manuscript draft.

Supplementary material

477_2014_1016_MOESM1_ESM.xls (22 kb)
Supplementary material 1 (XLS 22 kb)
477_2014_1016_MOESM2_ESM.docx (57 kb)
Supplementary material 2 (DOCX 58 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.School of Biological and Chemical SciencesQueen Mary University of LondonLondonUK

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