Network models for spread and control of soil-borne epidemics

土传流行病传播和控制的网络模型

• 批准号: BB/E017312/1
• 负责人: Christopher Gilligan
• 金额: $ 70.29万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FE017312%2F1
• 关键词: Network; models; spread; control; soil

There is an urgent need for reliable control strategies for epidemics caused by soil-borne plant pathogens. In particular for root-diseases, a fundamental approach is lacking. Susceptible plants or roots are spatially separated in a heterogeneous, dynamically changing soil environment through which pathogens spread. The opacity and heterogeneity of soil makes it difficult to deliver control agents. Currently, there is no coherent theoretical framework available that can deal with such a complicated and heterogeneous system. Hence practitioners and scientist are still applying biological and chemical control strategies empirically. This proposal is set out to change this, by developing and testing a theory for soil-borne epidemics. The main aims of this project are to link developments from non-equilibrium statistical physics with epidemiological theory and experimentation in order: 1. to model and analyse the spread of soil-borne diseases through inherently heterogeneous systems at microscopic and macroscopic scales, using theory of non-equilibrium phase transition in complex networks; 2. to analyse the efficiency of control strategies on such disordered networks. In previous work we have shown that a small change in environmental conditions can induce a switch from non-invasive to invasive spread for soil-borne pathogens, and that this behaviour is consistent with thresholds predicted from percolation theory for networks. Experimentation, however, was conducted in artificial systems, and the concept of sudden changes to ecosystems remains counterintuitive and subject of debate amongst biologists. Experimental verification of model predictions under realistic scenarios is therefore important. Moreover, a close interaction between experimentation and modelling such as we propose will lead to appropriate model parameterisation and to testing of the robustness of predictions under realistic heterogeneous conditions. Despite these undisputed benefits, experimental testing of theoretical predictions is rare. We propose that network models offer a way forward for soil-borne epidemics in that testable hypotheses related to invasion and persistence can be formulated. Susceptible sites in soil-borne epidemics can be identified as roots or plants, in various spatial arrangements, analogous to networks. The connections between sites may be weak or strong (depending on mode of dispersal (propagation)), permanent or temporal (depending on soil physical conditions, host growth, recovery, and changes in susceptibility), with sites spatially arranged either in lines (crops grown in rows), regular lattice (crops or propagation trays), or off-lattice (e.g. spatial distribution of roots). The spread of epidemics on complex networks has been the topic of intensive investigation, yet the inherent heterogeneity typical for epidemics is often omitted in these models. Such heterogeneity, however, can appreciably affect the behaviour of networks. In this proposal we will tackle this by extending the theory for network models to heterogeneous systems making use and building upon our expertise in non-equilibrium statistical physics. Our experimental and theoretical expertise in soil physics and soil-borne epidemics will enable us to identify ways to manipulate the network topology and the network parameters (transmission and recovery), and to collect data on replicated epidemics, which allows for testing of model prediction on invasion and extinction. By linking our expertises in non-equilibrium statistical physics with epidemiological theory and experimentation we will formulate and test appropriate models, and use these to identify those conditions that can significantly change epidemics (make epidemics invade and persist), and will hence identify control strategies that are most likely to be successful in such a complex environment.

迫切需要对由土壤传播植物病原体引起的流行病进行可靠的控制策略。特别是对于根部脉络，缺乏一种基本的方法。易感植物或根部在异质，动态变化的土壤环境中通过该空间分离，病原体传播。土壤的不透明度和异质性使得很难传递控制剂。当前，尚无连贯的理论框架可以处理如此复杂且异质的系统。因此，从业者和科学家仍在经验上采用生物学和化学控制策略。该提议设定通过开发和测试土壤传播流行病理论来改变这一点。该项目的主要目的是将从非平衡统计物理学与流行病学理论和实验的发展联系起来：1。使用非平衡性过渡的理论在复杂网络中的理论来对土壤传播疾病通过微观和巨大尺度上的固有异质系统进行建模和分析。 2。分析此类无序网络的控制策略的效率。在先前的工作中，我们已经表明，环境条件的较小变化可以引起从非侵入性的土壤传播病原体的转变，并且这种行为与网络渗透理论预测的阈值一致。但是，在人工系统中进行了实验，生态系统突然变化的概念仍然是违反直觉的，并且在生物学家中的辩论主题。因此，对现实情况下模型预测的实验验证很重要。此外，实验和建模（例如我们提出的）之间的紧密相互作用将导致适当的模型参数化和测试在现实的异质条件下预测的鲁棒性。尽管这些无可争议的好处，但对理论预测的实验测试很少。我们建议网络模型为土壤传播流行病提供了前进的道路，即可以制定与入侵和持久性有关的可检验的假设。土壤传播流行病中的易感位点可以鉴定为类似于网络的各种空间排列的根或植物。站点之间的连接可能是弱或较强的（取决于分散模式（繁殖）），永久性或暂时性（取决于土壤的物理条件，宿主的生长，易感性的恢复和变化），站点在空间上排列在线路上（成行的农作物），常规的晶格（作物或传播托盘），或外部分配。流行病在复杂网络上的传播一直是深入研究的主题，但是这些模型中经常省略了流行病的固有异质性。但是，这种异质性可以显着影响网络的行为。在此提案中，我们将通过将网络模型的理论扩展到在非平衡统计物理学方面的专业知识上的异质系统来解决这一问题。我们在土壤物理和土壤传播流行病方面的实验和理论专业知识将使我们能够确定操纵网络拓扑和网络参数（传播和恢复）的方法，并收集有关复制的流行病的数据，从而可以测试模型预测入侵和灭绝和灭绝。通过将非平衡统计物理学的专业化与流行病学理论和实验联系起来，我们将制定和测试适当的模型，并使用这些模型来识别那些可以显着改变流行病的条件（使流行病的入侵和持续存在），因此将在如此复杂的环境中确定最有可能成功的控制策略。

项目成果

Contact process in disordered and periodic binary two-dimensional lattices.

无序和周期性二元二维晶格中的接触过程。

• DOI: 10.1103/physreve.78.041117
• 发表时间: 2008
• 期刊: Physical review. E, Statistical, nonlinear, and soft matter physics
• 作者: Fallert SV

Simulating the contact process in heterogeneous environments.

模拟异构环境中的接触过程。

• DOI: 10.1103/physreve.77.051125
• 发表时间: 2008
• 期刊: Physical review. E, Statistical, nonlinear, and soft matter physics
• 作者: Fallert SV

A new model for the pathozone of the take-all pathogen, Gaeumannomyces graminis var. tritici

• DOI: 10.1111/aab.12060
• 发表时间: 2013-11-01
• 期刊: ANNALS OF APPLIED BIOLOGY
• 影响因子: 2.6
• 作者: Gosme, M.;Lebreton, L.;Bailey, D. J.
• 通讯作者: Bailey, D. J.

Analytical study of hysteresis in the T = 0 random field Ising model

T = 0 随机场 Ising 模型中磁滞的分析研究

• DOI: 10.1063/1.3569520
• 发表时间: 2011
• 作者: Handford T

Modelling fungal colonies and communities: challenges and opportunities.

• DOI: 10.5598/imafungus.2010.01.02.07
• 发表时间: 2010-12
• 期刊: IMA fungus
• 影响因子: 5.4
• 作者: Falconer RE;Bown JL;McAdam E;Perez-Reche P;Sampson AT;van den Bulcke J;White NA
• 通讯作者: White NA