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

doi:10.1890/14-1808.1/suppinfo

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

Ecology, Evolution and Behavior

Research output: Contribution to journal › Article › peer-review

15 Scopus citations

Abstract

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 language	English (US)
Pages (from-to)	295-308
Number of pages	14
Journal	Ecological Applications
Volume	26
Issue number	1
DOIs	https://doi.org/10.1890/14-1808.1/suppinfo
State	Published - Jan 1 2016

Bibliographical note

Funding Information:
The risk assessment workshop was facilitated by the Conservation Breeding Specialists Group; we thank workshop participants for sharing their expertise. We thank Cathleen Nguyen for assistance with early models, Damien Caillaud for the introduction to ABC-SMC, and Elizabeth Borer, David Tilman, and George Heimpel for comments on earlier versions of the manuscript. We very much appreciate the comments of Florian Hartig, Franck Jabot, and an anonymous reviewer, which greatly improved this version. M. Kosmala was supported by an NSF Graduate Research Fellowship.

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

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1890/14-1808.1/suppinfo

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abstract = "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.",

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author = "Margaret Kosmala and Philip Miller and Sam Ferreira and Paul Funston and Dewald Keet and Craig Packer",

note = "Funding Information: The risk assessment workshop was facilitated by the Conservation Breeding Specialists Group; we thank workshop participants for sharing their expertise. We thank Cathleen Nguyen for assistance with early models, Damien Caillaud for the introduction to ABC-SMC, and Elizabeth Borer, David Tilman, and George Heimpel for comments on earlier versions of the manuscript. We very much appreciate the comments of Florian Hartig, Franck Jabot, and an anonymous reviewer, which greatly improved this version. M. Kosmala was supported by an NSF Graduate Research Fellowship. Publisher Copyright: {\textcopyright} 2016 by the Ecological Society of America.",

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AU - Packer, Craig

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N2 - 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.

AB - 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.

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

Abstract

Bibliographical note

Keywords

UN SDGs

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