Suprunenko Yevhen F, Cornell Stephen J, Gilligan Christopher A
Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK.
Institute of Infection, Veterinary, and Ecological Sciences, University of Liverpool, Liverpool L69 7ZB, UK.
R Soc Open Sci. 2025 Jan 8;12(1):240763. doi: 10.1098/rsos.240763. eCollection 2025 Jan.
The influence of landscape structure on epidemic invasion of agricultural crops is often underestimated in the construction and analysis of epidemiological models. Computer simulations of individual-based models (IBMs) are widely used to characterize disease spread under different management scenarios but can be slow in exploring large numbers of different landscape configurations. Here, we address the problem of finding an analytical measure of the impact of the spatial structure of a crop landscape on the invasion and spread of plant pathogens. We explore the potential of using an analytical approximation for the rate, , at which susceptible crop fields become infected at the start of an epidemic to predict the effect that the spatial structure of a host landscape will have on an epidemic. We demonstrate the validity of this approach using two models: (i) a general IBM of the invasion and spread of a pathogen through an abstract host landscape; and (ii) an IBM of a real-life example for a virus disease spreading through a cassava landscape. Finally, we demonstrate that the analytical approach based on an estimate of the rate, , can be used to identify spatial structures that effect deceleration of an invading pathogen.
在流行病学模型的构建和分析中，景观结构对农作物疫病入侵的影响常常被低估。基于个体的模型（IBM）的计算机模拟被广泛用于描述不同管理情景下的疾病传播，但在探索大量不同的景观配置时可能会很缓慢。在这里，我们解决了寻找一种分析方法来衡量作物景观的空间结构对植物病原体入侵和传播影响的问题。我们探讨了使用一种解析近似来计算在疫病开始时易感作物田被感染的速率(\lambda)，以预测寄主景观的空间结构对疫病将产生的影响。我们使用两个模型证明了这种方法的有效性：（i）一个病原体通过抽象寄主景观入侵和传播的通用IBM；（ii）一个病毒病在木薯景观中传播的实际例子的IBM。最后，我们证明基于速率(\lambda)估计的分析方法可用于识别影响入侵病原体减速的空间结构。
