Estimating wildlife disease dynamics in complex systems using an Approximate Bayesian Computation framework

Margaret Kosmala, Philip Miller, Sam Ferreira, Paul Funston, Dewald Keet, Craig Packer

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


Emerging infectious diseases of wildlife are of increasing concern to managers and conservation policy makers, but are often diffi cult to study and predict due to the complexity of host-disease systems and a paucity of empirical data. We demonstrate the use of an Approximate Bayesian Computation statistical framework to reconstruct the disease dynamics of bovine tuberculosis in Kruger National Park ' s lion population, despite limited empirical data on the disease ' s effects in lions. The modeling results suggest that, while a large proportion of the lion population will become infected with bovine tuberculosis, lions are a spillover host and long disease latency is common. In the absence of future aggravating factors, bovine tuberculosis is projected to cause a lion population decline of ~3% over the next 50 years, with the population stabilizing at this new equilibrium. The Approximate Bayesian Computation framework is a new tool for wildlife managers. It allows emerging infectious diseases to be modeled in complex systems by incorporating disparate knowledge about host demographics, behavior, and heterogeneous disease transmission, while allowing inference of unknown system parameters.

Original languageEnglish (US)
Pages (from-to)295-308
Number of pages14
JournalEcological Applications
Issue number1
StatePublished - Jan 1 2016

Bibliographical note

Publisher Copyright:
© 2016 by the Ecological Society of America.


  • African buffalo, Syncerus caffer
  • Approximate Bayesian Computation, ABC
  • Bovine tuberculosis, bTB
  • Disease modeling
  • Emerging disease
  • Kruger National Park, South Africa
  • Lion, Panthera leo
  • Multi-host system
  • Mycobacterium bovis
  • Wildlife epidemiology


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