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

机译：使用近似贝叶斯计算框架估算复杂系统中的野生动植物疾病动态

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摘要

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 similar to 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.
机译：管理人员和保护政策制定者日益关注新出现的野生动植物传染病，但由于宿主疾病系统的复杂性和经验数据的匮乏，往往难以对其进行研究和预测。我们展示了使用近似贝叶斯计算统计框架来重建克鲁格国家公园狮子种群中牛结核病的疾病动态，尽管有关该疾病对狮子影响的经验数据有限。模型结果表明，尽管很大一部分狮子种群将被牛结核病感染，但狮子是溢出宿主，长期的疾病潜伏期很普遍。在没有未来加重因素的情况下，预计牛结核病将在未来50年内造成大约3％的狮子种群下降，而种群稳定在这个新的平衡点上。近似贝叶斯计算框架是野生动物管理者的新工具。通过合并有关宿主的人口统计学，行为和异质性疾病传播的不同知识，它允许在复杂系统中对新兴传染病进行建模，同时允许推断未知的系统参数。

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