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Lookup NU author(s): Dr Mark ShirleyORCiD, Professor Stephen Rushton, Dr Andrew South, Dr Peter Lurz
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We describe an individual-based spatially-explicit model designed to investigate the dynamics of badger populations and TB epidemiology in a real landscape. We develop a methodology for evaluating the sensitivity of the model to its input parameters through the use of power analysis, partial correlation coefficients and binary logistic regression. This novel approach to sensitivity analysis provides a formal statement of confidence in our findings based on statistical power, and a solution for analysing sparse data sets of disease prevalence. The sensitivity analysis revealed that the simulated badger population size after 20 years was most dependent on five parameters affecting female recruitment (probability of breeding, mortality of adult females in the first half of the year, mortality of juvenile females in the second half of the year and mortality of female cubs in the both halves of the year). The simulated prevalence of TB was most affected by the population size, the rate at which infectious badgers transmit the disease to other members of their social group, and the rate at which the disease is spread outside of the social group. The spatial and temporal predictions of the model were tested against badger demography and TB prevalence data derived from the field. When validated in space, the model generated population sizes and disease incidence that were consistent with the observed field population. We conclude that modelling TB dynamics must include spatial and temporal heterogeneity in life history parameters, social behaviour and the landscape. Based on parameter sensitivity and data availability, we suggest priorities for future empirical research on badgers and bovine tuberculosis. © 2003 Published by Elsevier Science B.V.
Author(s): Shirley MDF, Rushton SP, Smith G, South AB, Lurz PWW
Publication type: Article
Publication status: Published
Journal: Ecological Modelling
Year: 2003
Volume: 167
Issue: 1-2
Pages: 139-157
ISSN (print): 0304-3800
ISSN (electronic): 1872-7026
Publisher: Elsevier BV
URL: http://dx.doi.org/10.1016/S0304-3800(03)00167-4
DOI: 10.1016/S0304-3800(03)00167-4
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