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Estimating wildlife disease dynamics in complex systems using an Approximate Bayesian Computation framework
Author(s) -
Kosmala Margaret,
Miller Philip,
Ferreira Sam,
Funston Paul,
Keet Dewald,
Packer Craig
Publication year - 2016
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1890/14-1808
Subject(s) - approximate bayesian computation , wildlife , wildlife disease , population , infectious disease (medical specialty) , computer science , disease , inference , geography , biology , ecology , artificial intelligence , medicine , environmental health , pathology
Emerging infectious diseases of wildlife are of increasing concern to managers and conservation policy makers, but are often difficult 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.