Network models for spread and control of soil-borne epidemics

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.

The spread of epidemics in complex networks exemplified by biological populations including animals, human and plants is a very important area of research from both fundamental and practical viewpoints. Real epidemics spread in networks which are inherently disordered with network parameters including recovery and transmission rates varying throughout populations. Such disorders can influence crucially the behaviour within a network and understanding the role of disorder in the dynamical properties of epidemics within realistic disordered biological networks is of major significance for the control of 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 systems. We will develop and experimentally test this theory for contrasting epidemiological systems, namely (a) root systems, and (b) plants on lattices. In each system we will consider the spread of selected pathogens with different mode of dispersal. Our approach is motivated by recent work in the PI's group where we validated the existence of theoretical thresholds for invasion empirically, the expertise of the CI in theoretical analysis of complex networks, which now makes it possible to develop and test network-based models for soil-borne epidemics in inherently heterogeneous environments. The findings will have applicability to a broad range of eidemics and will identify criteria for invasion (e.g. plant densities or dispersal distance of pathogens) and will provide guidance for the deployment of control strategies in heterogeneous environments.