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持久抵御作物病原体:预测不确定性下风险的流行病学框架。

Durable resistance to crop pathogens: an epidemiological framework to predict risk under uncertainty.

机构信息

Department of Veterinary Medicine, Disease Dynamics Unit, University of Cambridge, Cambridge, United Kingdom.

出版信息

PLoS Comput Biol. 2013;9(1):e1002870. doi: 10.1371/journal.pcbi.1002870. Epub 2013 Jan 17.

Abstract

Increasing the durability of crop resistance to plant pathogens is one of the key goals of virulence management. Despite the recognition of the importance of demographic and environmental stochasticity on the dynamics of an epidemic, their effects on the evolution of the pathogen and durability of resistance has not received attention. We formulated a stochastic epidemiological model, based on the Kramer-Moyal expansion of the Master Equation, to investigate how random fluctuations affect the dynamics of an epidemic and how these effects feed through to the evolution of the pathogen and durability of resistance. We focused on two hypotheses: firstly, a previous deterministic model has suggested that the effect of cropping ratio (the proportion of land area occupied by the resistant crop) on the durability of crop resistance is negligible. Increasing the cropping ratio increases the area of uninfected host, but the resistance is more rapidly broken; these two effects counteract each other. We tested the hypothesis that similar counteracting effects would occur when we take account of demographic stochasticity, but found that the durability does depend on the cropping ratio. Secondly, we tested whether a superimposed external source of stochasticity (for example due to environmental variation or to intermittent fungicide application) interacts with the intrinsic demographic fluctuations and how such interaction affects the durability of resistance. We show that in the pathosystem considered here, in general large stochastic fluctuations in epidemics enhance extinction of the pathogen. This is more likely to occur at large cropping ratios and for particular frequencies of the periodic external perturbation (stochastic resonance). The results suggest possible disease control practises by exploiting the natural sources of stochasticity.

摘要

提高作物对植物病原体的抗性持久性是毒力管理的关键目标之一。尽管人们认识到人口统计学和环境随机性对流行动态的重要性,但它们对病原体进化和抗性持久性的影响尚未得到关注。我们基于主方程的 Kramer-Moyal 展开式,构建了一个随机流行病学模型,以研究随机波动如何影响流行的动态,以及这些影响如何反馈到病原体的进化和抗性的持久性。我们关注两个假设:首先,以前的确定性模型表明,种植比例(受抗性作物所占土地面积的比例)对作物抗性持久性的影响可以忽略不计。增加种植比例会增加未感染宿主的面积,但抗性会更快地被打破;这两种效应相互抵消。我们测试了在考虑到人口统计学随机性时会出现类似的抵消效应的假设,但发现抗性确实取决于种植比例。其次,我们测试了外部随机源(例如由于环境变化或间歇性杀菌剂施用)与内在人口统计学波动的叠加如何相互作用,以及这种相互作用如何影响抗性的持久性。我们表明,在所考虑的病理系统中,一般来说,流行病中的大随机波动会增强病原体的灭绝。这更可能发生在大的种植比例和周期性外部干扰的特定频率(随机共振)下。结果表明,通过利用自然随机源,可以采取可能的疾病控制措施。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a09c/3547817/f2a1935f9644/pcbi.1002870.g001.jpg

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