Modelling plant disease spread and containment: Simulation and approximate Bayesian Computation for Xylella fastidiosa in Puglia, Italy.

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
PLoS Computational Biology Pub Date : 2025-10-03 eCollection Date: 2025-10-01 DOI:10.1371/journal.pcbi.1013539
Daniel Chapman, Flavia Occhibove, James M Bullock, Pieter S A Beck, Juan A Navas-Cortes, Steven M White
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引用次数: 0

Abstract

Mathematical and computational models play a crucial role in understanding the epidemiology of economically important plant disease outbreaks, and in evaluating the effectiveness of surveillance and disease management measures. A case in point is Xylella fastidiosa, one of the world's most deadly plant pathogens. Since its European discovery in olives in Puglia, Italy in 2013, there remain key knowledge gaps that undermine landscape-scale containment efforts of the outbreak, most notably concerning the year of introduction, the rate of spread, dispersal mechanisms and control efficacy. To address this, we developed a spatially explicit simulation model for the outbreak spreading among olive groves coupled to a simulation of the real surveillance and containment measures. We used Approximate Bayesian Computation to fit the model to surveillance and remote-sensing infection data, comparing the fits for three alternative dispersal mechanisms (isotropic, wind and road). The model accurately explained the rate and spatiotemporal pattern of the outbreak and found weak support for the wind dispersal model over the isotropic model. It suggests that the bacterium may have been introduced as early as 2003 (95% CI [2000, 2009]), earlier than previous estimates and congruent with anecdotal evidence. The isotropic model estimates the pathogen is spreading at 5.7 km y-1 (95% CI [5.4-5.9]) under containment measures, down from 7.2 km y-1 (95% CI [6.9-7.5]) without containment measures. Our estimate of an approximately 10-year lag between introduction and detection highlights the need for stronger biosecurity and surveillance for earlier detection of emerging plant pathogens. The outputs from simulations without any disease management also suggest that while containment measures have caused some slowing of X. fastidiosa spread, stronger measures will be required to contain the outbreak fully.

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植物疾病传播和控制模型:意大利普利亚苛养木杆菌的模拟和近似贝叶斯计算。
数学和计算模型在了解经济上重要的植物病害暴发的流行病学以及评估监测和疾病管理措施的有效性方面发挥着至关重要的作用。一个典型的例子是苛养木杆菌,世界上最致命的植物病原体之一。自2013年在意大利普利亚的橄榄中发现该病毒以来,仍然存在重大的知识空白,这破坏了在景观范围内遏制疫情的努力,特别是在引入年份、传播速度、传播机制和控制效果方面。为了解决这一问题,我们开发了一个空间明确的模拟模型,用于模拟橄榄树林中爆发的蔓延,并对真实的监测和遏制措施进行了模拟。我们使用近似贝叶斯计算将模型拟合到监测和遥感感染数据中,并比较了三种不同传播机制(各向同性、风和道路)的拟合。该模型准确地解释了爆发的速度和时空格局,并发现风扩散模型比各向同性模型支持较弱。这表明该细菌可能早在2003年就已被引入(95%置信区间[2000,2009]),比以前的估计要早,并与轶事证据一致。各向同性模型估计,在控制措施下,病原体的传播速度为每小时5.7公里(95% CI[5.4-5.9]),低于未采取控制措施时的每小时7.2公里(95% CI[6.9-7.5])。我们估计从引入到发现之间大约有10年的滞后,这突出了加强生物安全和监测以早期发现新出现的植物病原体的必要性。在没有任何疾病管理的情况下进行的模拟结果还表明,虽然控制措施使苛养x虫的传播有所减缓,但仍需要采取更强有力的措施来完全控制疫情。
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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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