Timing of Pathogen Adaptation to a Multicomponent Treatment

The sustainable use of multicomponent treatments such as combination therapies, combination vaccines/chemicals, and plants carrying multigenic resistance requires an understanding of how their population-wide deployment affects the speed of the pathogen adaptation. Here, we develop a stochastic model describing the emergence of a mutant pathogen and its dynamics in a heterogeneous host population split into various types by the management strategy. Based on a multi-type Markov birth and death process, the model can be used to provide a basic understanding of how the life-cycle parameters of the pathogen population, and the controllable parameters of a management strategy affect the speed at which a pathogen adapts to a multicomponent treatment. Our results reveal the importance of coupling stochastic mutation and migration processes, and illustrate how their stochasticity can alter our view of the principles of managing pathogen adaptive dynamics at the population level. In particular, we identify the growth and migration rates that allow pathogens to adapt to a multicomponent treatment even if it is deployed on only small proportions of the host. In contrast to the accepted view, our model suggests that treatment durability should not systematically be identified with mutation cost. We show also that associating a multicomponent treatment with defeated monocomponent treatments can be more durable than associating it with intermediate treatments including only some of the components. We conclude that the explicit modelling of stochastic processes underlying evolutionary dynamics could help to elucidate the principles of the sustainable use of multicomponent treatments in population-wide management strategies intended to impede the evolution of harmful populations.Comment: 3 figure

Estimating the delay between host infection and disease (incubation period) and assessing its significance to the epidemiology of plant diseases.

Knowledge of the incubation period of infectious diseases (time between host infection and expression of disease symptoms) is crucial to our epidemiological understanding and the design of appropriate prevention and control policies. Plant diseases cause substantial damage to agricultural and arboricultural systems, but there is still very little information about how the incubation period varies within host populations. In this paper, we focus on the incubation period of soilborne plant pathogens, which are difficult to detect as they spread and infect the hosts underground and above-ground symptoms occur considerably later. We conducted experiments on Rhizoctonia solani in sugar beet, as an example patho-system, and used modelling approaches to estimate the incubation period distribution and demonstrate the impact of differing estimations on our epidemiological understanding of plant diseases. We present measurements of the incubation period obtained in field conditions, fit alternative probability models to the data, and show that the incubation period distribution changes with host age. By simulating spatially-explicit epidemiological models with different incubation-period distributions, we study the conditions for a significant time lag between epidemics of cryptic infection and the associated epidemics of symptomatic disease. We examine the sensitivity of this lag to differing distributional assumptions about the incubation period (i.e. exponential versus Gamma). We demonstrate that accurate information about the incubation period distribution of a pathosystem can be critical in assessing the true scale of pathogen invasion behind early disease symptoms in the field; likewise, it can be central to model-based prediction of epidemic risk and evaluation of disease management strategies. Our results highlight that reliance on observation of disease symptoms can cause significant delay in detection of soil-borne pathogen epidemics and mislead practitioners and epidemiologists about the timing, extent, and viability of disease control measures for limiting economic loss.ML thanks the Institut Technique français de la Betterave industrielle (ITB) for funding this project. CAG and JANF were funded by the UK’s Biotechnology and Biological Sciences Research Council (BBSRC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

The effect of heterogeneity on invasion in spatial epidemics: from theory to experimental evidence in a model system

Heterogeneity in host populations is an important factor affecting the ability of a pathogen to invade, yet the quantitative investigation of its effects on epidemic spread is still an open problem. In this paper, we test recent theoretical results, which extend the established “percolation paradigm” to the spread of a pathogen in discrete heterogeneous host populations. In particular, we test the hypothesis that the probability of epidemic invasion decreases when host heterogeneity is increased. We use replicated experimental microcosms, in which the ubiquitous pathogenic fungus Rhizoctonia solani grows through a population of discrete nutrient sites on a lattice, with nutrient sites representing hosts. The degree of host heterogeneity within different populations is adjusted by changing the proportion and the nutrient concentration of nutrient sites. The experimental data are analysed via Bayesian inference methods, estimating pathogen transmission parameters for each individual population. We find a significant, negative correlation between heterogeneity and the probability of pathogen invasion, thereby validating the theory. The value of the correlation is also in remarkably good agreement with the theoretical predictions. We briefly discuss how our results can be exploited in the design and implementation of disease control strategies

Durable resistance to crop pathogens: an epidemiological framework to predict risk under uncertainty.

Increasing the durability of crop resistance to plant pathogens is one of the key goals of virulence management. Despite the recognition of the importance of demographic and environmental stochasticity on the dynamics of an epidemic, their effects on the evolution of the pathogen and durability of resistance has not received attention. We formulated a stochastic epidemiological model, based on the Kramer-Moyal expansion of the Master Equation, to investigate how random fluctuations affect the dynamics of an epidemic and how these effects feed through to the evolution of the pathogen and durability of resistance. We focused on two hypotheses: firstly, a previous deterministic model has suggested that the effect of cropping ratio (the proportion of land area occupied by the resistant crop) on the durability of crop resistance is negligible. Increasing the cropping ratio increases the area of uninfected host, but the resistance is more rapidly broken; these two effects counteract each other. We tested the hypothesis that similar counteracting effects would occur when we take account of demographic stochasticity, but found that the durability does depend on the cropping ratio. Secondly, we tested whether a superimposed external source of stochasticity (for example due to environmental variation or to intermittent fungicide application) interacts with the intrinsic demographic fluctuations and how such interaction affects the durability of resistance. We show that in the pathosystem considered here, in general large stochastic fluctuations in epidemics enhance extinction of the pathogen. This is more likely to occur at large cropping ratios and for particular frequencies of the periodic external perturbation (stochastic resonance). The results suggest possible disease control practises by exploiting the natural sources of stochasticity.GL is funded by the ESPA award “Dynamic Drivers of Disease in Africa Consortium”. The work of FvdB is supported by Rothamsted Research, who receives grant aided assistance from the Biological and Biotechnological Research Council of the United Kingdom. CAG gratefully acknowledges the support of a BBSRC Professorial Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.This is the final version of the article. It first appeared from PLOS via http://dx.doi.org/10.1371/journal.pcbi.100287

Modelling the spread of American foulbrood in honeybees

We investigate the spread of American foulbrood (AFB), a disease caused by the bacterium Paenibacillus larvae, that affects bees and can be extremely damaging to beehives. Our dataset comes from an inspection period carried out during an AFB epidemic of honeybee colonies on the island of Jersey during the summer of 2010. The data include the number of hives of honeybees, location and owner of honeybee apiaries across the island. We use a spatial SIR model with an underlying owner network to simulate the epidemic and characterize the epidemic using a Markov chain Monte Carlo (MCMC) scheme to determine model parameters and infection times (including undetected ‘occult’ infections). Likely methods of infection spread can be inferred from the analysis, with both distance- and owner-based transmissions being found to contribute to the spread of AFB. The results of the MCMC are corroborated by simulating the epidemic using a stochastic SIR model, resulting in aggregate levels of infection that are comparable to the data. We use this stochastic SIR model to simulate the impact of different control strategies on controlling the epidemic. It is found that earlier inspections result in smaller epidemics and a higher likelihood of AFB extinction

Eco-evolutionary dynamics of disease under human-induced selection

More than ten thousand years ago, humans started breeding plants as food supply: they chose those varieties of nutritional interest, grew them, and kept the seeds of the best plants for the next season. These practices were the beginning of agriculture, a long-term evolutionary experiment where humans act as a selective force. Active breeding is not the only way in which humans modify evolutionary trajectories: they also change the environment where species live. For example, global trade creates novel species interactions, and the urbanisation of wild areas alters ecological niches. Another compelling case of human-induced selection – and the topic of interest in this thesis – is the control of pathogens. Pathogens are regarded as a threat for human species survival, either because they are causing diseases in humans or because they constitute a risk to food security. In consequence, humans have developed management practices which intend to reduce or eradicate the population of these pathogens by applying abiotic (e.g. drugs) or biotic (e.g. biocontrol with other species) pressures. These strategies, as they deal with populations of living organisms, involve ecological and evolutionary processes. Thus, to improve pathogen control, we need to apply the current knowledge and techniques of ecology and evolution. This thesis studies how pathogen populations are affected by the alternation of selective pressures to which they are exposed. Mainly, I study the dynamics of pathogen populations when host species are switched along time. The different reproductive rates of the pathogen in each host species can slow down the growth or diminish its population in the long-term. In agriculture, this can be achieved by using crop rotations in a field; in vector-borne diseases, the vector and the host are two different ecological niches for the pathogen, and the administration of drugs to the human host can be disadvantageous for pathogen reproduction in the vector. Using mathematical and computational models, I study host-pathogen interactions in infected crop fields and human populations affected by malaria. I simulate infections under multiple scenarios of selection in alternating host species and observe their progress or regression. The results are used to assess the optimality of human interventions for the control of the disease-causing pathogens. Overall, this thesis confirms that a better knowledge of eco-evolutionary principles in disease management can improve the design of strategies. This is especially true given the need for practices which are both efficient and sustainable across generations

Effects of single and mixed infections with wild type and genetically modified Helicoverpa armigera nucleopolyhedrovirus on movement behaviour of cotton bollworm larvae

Naturally occurring insect viruses can modify the behaviour of infected insects and thereby modulate virus transmission. Modifications of the virus genome could alter these behavioural effects. We studied the distance moved and the position of virus-killed cadavers of fourth instars of Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) infected with a wild-type genotype of H. armigera nucleopolyhedrovirus (HaSNPV) or with one of two recombinant genotypes of this virus on cotton plants. The behavioural effects of virus infection were examined both in larvae infected with a single virus genotype, and in larvae challenged with mixtures of the wild-type and one of the recombinant viruses. An egt-negative virus variant caused more rapid death and lower virus yield in fourth instars, but egt-deletion did not produce consistent behavioural effects over three experiments, two under controlled glasshouse conditions and one in field cages. A recombinant virus containing the AaIT-(Androctonus australis Hector) insect-selective toxin gene, which expresses a neurotoxin derived from a scorpion, caused faster death and cadavers were found lower down the plant than insects infected with unmodified virus. Larvae that died from mixed infections of the AaIT-expressing recombinant and the wild-type virus died at positions significantly lower, compared to infection with the pure wild-type viral strain. The results indicate that transmission of egt-negative variants of HaSNPV are likely to be affected by lower virus yield, but not by behavioural effects of egt gene deletion. By contrast, the AaIT recombinant will produce lower virus yields as well as modified behaviour, which together can contribute to reduced virus transmission under field conditions. In addition, larvae infected with both the wild-type virus and the toxin recombinant behaved as larvae infected with the toxin recombinant only, which might be a positive factor for the risk assessment of such toxin recombinants in the environment

Optimising reactive disease management using spatially explicit models at the landscape scale

Increasing rates of global trade and travel, as well as changing climatic patterns, have led to more frequent outbreaks of plant disease epidemics worldwide. Mathematical modelling is a key tool in predicting where and how these new threats will spread, as well as in assessing how damaging they might be. Models can also be used to inform disease management, providing a rational methodology for comparing the performance of possible control strategies against one another. For emerging epidemics, in which new pathogens or pathogen strains are actively spreading into new regions, the spatial component of spread becomes particularly important, both to make predictions and to optimise disease control. In this chapter we illustrate how the spatial spread of emerging plant diseases can be modelled at the landscape scale via spatially explicit compartmental models. Our particular focus is on the crucial role of the dispersal kernel-which parameterises the probability of pathogen spread from an infected host to susceptible hosts at any given distance-in determining outcomes of epidemics. We add disease management to our model by testing performance of a simple "one off" form of reactive disease control, in which sites within a particular distance of locations detected to contain infection are removed in a single round of disease management. We use this simplified model to show how ostensibly arcane decisions made by the modeller-most notably whether or not the underpinning disease model allows for stochasticity (i.e. randomness)-can greatly impact on disease management recommendations. Our chapter is accompanied by example code in the programming language R available via an online repository, allowing the reader to run the models we present for him/herself