Novel coupling of individual-based epidemiological and demographic models predicts realistic dynamics of tuberculosis in alien buffalo
Article first published online: 26 OCT 2011
© 2011 The Authors. Journal of Applied Ecology © 2011 British Ecological Society
Journal of Applied Ecology
Volume 49, Issue 1, pages 268–277, February 2012
How to Cite
Bradshaw, C. J. A., McMahon, C. R., Miller, P. S., Lacy, R. C., Watts, M. J., Verant, M. L., Pollak, J. P., Fordham, D. A., Prowse, T. A. A. and Brook, B. W. (2012), Novel coupling of individual-based epidemiological and demographic models predicts realistic dynamics of tuberculosis in alien buffalo. Journal of Applied Ecology, 49: 268–277. doi: 10.1111/j.1365-2664.2011.02081.x
- Issue published online: 17 JAN 2012
- Article first published online: 26 OCT 2011
- Received 16 June 2011; accepted 29 September 2011 Handling Editor: Julia Jones
- invasive species;
- MetaModel Manager;
- population viability analysis;
1. Increasing sophistication of population viability analysis has broadened our capacity to model population change while accounting for system complexity and uncertainty. However, many emergent properties of population dynamics, such as the coupling of demographic processes with transmission and spread of disease, are still poorly understood.
2. We combined an individual-based demographic (Vortex) and epidemiological (Outbreak) model using a novel command-centre module (MetaModel Manager) to predict the progression of bovine tuberculosis in introduced swamp buffalo Bubalus bubalis in northern Australia and validated the model with data from a large-scale disease-monitoring and culling programme. We also assessed the capacity to detect disease based on incrementing sentinel (randomly sampled individuals) culling rates.
3. We showed that even high monitoring effort (1000 culled sentinels) has a low (<10%) probability of detecting the disease, and current sampling is inadequate.
4. Testing proportional and stepped culling rates revealed that up to 50% of animals must be killed each year to reduce disease prevalence to near-eradication levels.
5. Sensitivity analysis indicated that prevalence depended mainly on population demography (e.g. female age at primiparity) and the additional mortality induced by disease, with only minor contributions from epidemiological characteristics such as probability of transmission and encounter rate. This is a useful finding because the disease parameters are the least well known.
6. Synthesis and applications. Our models suggest that details of population demography should be incorporated into epidemiological models to avoid extensive bias in predictions of disease spread and effectiveness of control. Importantly, we demonstrate that low detection probabilities challenge the effectiveness of existing disease-monitoring protocols in northern Australia. The command-centre module we describe linking demographic and epidemiological models provides managers with the tools necessary to make informed decisions regarding disease management.