A big-data spatial, temporal and network analysis of bovine tuberculosis between wildlife (badgers) and cattle

Original Paper

Abstract

Bovine tuberculosis (TB) poses a serious threat for agricultural industry in several countries, it involves potential interactions between wildlife and cattle and creates societal problems in terms of human-wildlife conflict. This study addresses connectedness network analysis, the spatial, and temporal dynamics of TB between cattle in farms and the European badger (Meles meles) using a large dataset generated by a calibrated agent based model. Results showed that infected network connectedness was lower in badgers than in cattle. The contribution of an infected individual to the mean distance of disease spread over time was considerably lower for badger than cattle; badgers mainly spread the disease locally while cattle infected both locally and across longer distances. The majority of badger-induced infections occurred when individual badgers leave their home sett, and this was positively correlated with badger population growth rates. Point pattern analysis indicated aggregation in the spatial pattern of TB prevalence in badger setts across all scales. The spatial distribution of farms that were not TB free was aggregated at different scales than the spatial distribution of infected badgers and became random at larger scales. The spatial cross correlation between infected badger setts and infected farms revealed that generally infected setts and farms do not coexist except at few scales. Temporal autocorrelation detected a two year infection cycle for badgers, while there was both within the year and longer cycles for infected cattle. Temporal cross correlation indicated that infection cycles in badgers and cattle are negatively correlated. The implications of these results for understanding the dynamics of the disease are discussed.

Keywords

Network connectedness Point pattern analysis Individual based models Veterinary epidemiology Spatial analysis Temporal correlation 

Notes

Acknowledgments

Comments of three anonymous reviewers have been exceptionally thorough and helpful. This paper is part of the special issue in Spatio-temporal Data Mining in Ecological and Veterinary Epidemiology. AM has been the guest editor for the special issue and declares that this submission was handled by regular member of the editorial board.

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© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

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

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