Network models for spread and control of soil-borne epidemics

迫切需要可靠的控制策略来控制由土传植物病原体引起的流行病。尤其是对于根部疾病，缺乏基本的方法。敏感的植物或根在空间上被隔离在一个异质的、动态变化的土壤环境中，病原体通过这个环境传播。土壤的不透明性和异质性使其难以投放防治剂。目前，还没有一个连贯的理论框架可以处理这样一个复杂的、不同种类的系统。因此，从业者和科学家仍在经验性地应用生物和化学防治策略。这项提议旨在通过开发和测试土壤传播流行病的理论来改变这一点。该项目的主要目的是将非平衡统计物理学的发展与流行病学理论和实验联系起来，以便：1.利用复杂网络中的非平衡相变理论，在微观和宏观尺度上模拟和分析土传疾病通过固有的非均质系统的传播；2.分析对这种无序网络的控制策略的效率。在以前的工作中，我们已经证明了环境条件的微小变化可以诱导土传病原体从非侵入性传播到侵入性传播，并且这种行为与从网络渗流理论预测的阈值是一致的。然而，实验是在人工系统中进行的，生态系统突然变化的概念仍然是违反直觉的，也是生物学家争论的主题。因此，在现实情景下对模型预测进行实验验证是很重要的。此外，如我们所建议的那样，实验和建模之间的密切互动将导致适当的模型参数化，并测试在现实的不同条件下预测的稳健性。尽管有这些毋庸置疑的好处，但对理论预测进行实验测试的情况很少。我们认为，网络模型为土壤传播的流行病提供了一条前进的道路，因为可以制定与入侵和持久性有关的可检验假设。在土传流行病中，易感地点可以被确定为根或植物，在不同的空间安排中，类似于网络。地点之间的联系可以是弱的或强的(取决于扩散(繁殖)方式)，永久性的或暂时的(取决于土壤物理条件、寄主生长、恢复和敏感性的变化)，地点在空间上排列成行(作物成行)、规则格子(作物或繁殖托盘)或非格子(如根的空间分布)。流行病在复杂网络上的传播一直是深入研究的主题，但这些模型往往忽略了流行病特有的内在异质性。然而，这种异质性可以明显地影响网络的行为。在这项提案中，我们将通过将网络模型的理论扩展到不同的系统来解决这个问题，利用并建立我们在非平衡统计物理方面的专业知识。我们在土壤物理和土传流行病方面的实验和理论专长将使我们能够找到操纵网络拓扑和网络参数(传输和恢复)的方法，并收集关于重复流行的数据，从而能够测试入侵和灭绝的模型预测。通过将我们在非平衡统计物理方面的专业知识与流行病学理论和实验相结合，我们将制定和测试适当的模型，并使用这些模型来确定那些可以显著改变流行病(使流行病入侵并持续)的条件，从而确定在这样一个复杂的环境中最有可能成功的控制策略。

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.