Investigating the spatial dynamics of bovine tuberculosis in badger
populations
Journal
Ecological
Modelling
Volume
167, Issues 1-2 , 1 September 2003, Pages 139-157 -
Elsevier B.V.
Authors
Mark D. F. Shirley,
Steve P. Rushton,
Graham C. Smith, Andrew B. South and Peter W. W. Lurz
Abstract
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.
Keywords
Spatially-explicit; Sensitivity analysis; Power
analysis; Mantel tests; Latin hypercube sampling; Logistic regression;
Meles meles; TB
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