Epidemiological models for invasion and persistence of pathogens

Building pyramids against the evolutionary emergence of pathogens

Mutations allowing pathogens to escape host immunity promote the spread of infectious diseases in heterogeneous host populations and can lead to major epidemics. Understanding the conditions that slow down this evolution is key for the development of durable control strategies against pathogens. Here, we use theory and experiments to compare the efficacy of three strategies for the deployment of resistance: (i) a mixing strategy where the host population contains two single-resistant genotypes, (ii) a pyramiding strategy where the host carries a double-resistant genotype, (iii) a combining strategy where the host population is a mix of a single-resistant genotype and a double-resistant genotype. First, we use evolutionary epidemiology theory to clarify the interplay between demographic stochasticity and evolutionary dynamics to show that the pyramiding strategy always yields lower probability of evolutionary emergence. Second, we test experimentally these predictions with the introduction of bacteriophages into bacterial populations where we manipulated the diversity and the depth of immunity using a Clustered Regularly Interspaced Short Palindromic Repeats-CRISPR associated (CRISPR-Cas) system. These biological assays confirm that pyramiding multiple defences into the same host genotype and avoiding combination with single-defence genotypes is a robust way to reduce pathogen evolutionary emergence. The experimental validation of these theoretical recommendations has practical implications in various areas, including for the optimal deployment of resistance varieties in agriculture and for the design of durable vaccination strategies.

Alternative host shapes transmission and life-history trait correlations in a multi-host plant pathogen

Most pathogens are generalists capable of infecting multiple host species or strains. Trade-offs in performance among different hosts are expected to limit the evolution of generalism. Despite the commonness of generalism, the variation in infectivity, transmission, and trade-offs in performance among host species have rarely been studied in the wild. To understand the ecological and evolutionary drivers of multi-host pathogen infectivity and transmission potential, I studied disease severity, transmission dynamics, and infectivity variation of downy mildew pathogen Peronospora sparsa on its three host plants Rubus arcticus, R. chamaemorus, and R. saxatilis. In a survey of 20 wild and cultivated sites of the three host species, disease severity varied by host species and by host population size but not among wild and cultivated sites. To understand how alternative host presence and plant diversity affect transmission of the pathogen, I conducted a transmission experiment. In this experiment, alternative host abundance and plant diversity together modified P. sparsa transmission to trap plants. To understand how resistance to P. sparsa varies among host species and genotypes, I conducted an inoculation experiment using 10 P. sparsa strains from different locations and 20 genotypes of the three host species. Significant variation in infectivity was found among host genotypes but not among host species. When trade-offs for infectivity were tested, high infectivity in one host species correlated with high infectivity in another host species. However, when pathogen transmission-related life-history correlations were tested, a positive correlation was found in R. arcticus but not in R. saxatilis. The results suggest that host resistance may shape pathogen life-history evolution with epidemiological consequences in a multi-host pathogen.

Evolutionary Epidemiology Consequences of Trait-Dependent Control of Heterogeneous Parasites

AbstractDisease control can induce both demographic and evolutionary responses in host-parasite systems. Foreseeing the outcome of control therefore requires knowledge of the eco-evolutionary feedback between control and system. Previous work has assumed that control strategies have a homogeneous effect on the parasite population. However, this is not true when control targets those traits that confer to the parasite heterogeneous levels of resistance, which can additionally be related to other key parasite traits through evolutionary trade-offs. In this work, we develop a minimal model coupling epidemiological and evolutionary dynamics to explore possible trait-dependent effects of control strategies. In particular, we consider a parasite expressing continuous levels of a trait-determining resource exploitation and a control treatment that can be either positively or negatively correlated with that trait. We demonstrate the potential of trait-dependent control by considering that the decision maker may want to minimize both the damage caused by the disease and the use of treatment, due to possible environmental or economic costs. We identify efficient strategies showing that the optimal type of treatment depends on the amount applied. Our results pave the way for the study of control strategies based on evolutionary constraints, such as collateral sensitivity and resistance costs, which are receiving increasing attention for both public health and agricultural purposes.

Modelling the spread and mitigation of an emerging vector-borne pathogen: Citrus greening in the U.S

Predictive models, based upon epidemiological principles and fitted to surveillance data, play an increasingly important role in shaping regulatory and operational policies for emerging outbreaks. Data for parameterising these strategically important models are often scarce when rapid actions are required to change the course of an epidemic invading a new region. We introduce and test a flexible epidemiological framework for landscape-scale disease management of an emerging vector-borne pathogen for use with endemic and invading vector populations. We use the framework to analyse and predict the spread of Huanglongbing disease or citrus greening in the U.S. We estimate epidemiological parameters using survey data from one region (Texas) and show how to transfer and test parameters to construct predictive spatio-temporal models for another region (California). The models are used to screen effective coordinated and reactive management strategies for different regions.

Role of vector resistance and grafting infection in Huanglongbing control models

Citrus huanglongbing (HLB) is one of the most devastating diseases affecting citrus almost worldwide due to the lack of a cure. To better understand the impact of insecticide resistance and grafting infection on the spread of HLB disease, a vector-borne compartmental model is formulated to describe the transmission dynamics of HLB between citrus and Asian citrus psyllid (ACP). The basic reproduction number R₀ is computed by using the next generation matrix approach, which is a threshold value of the uniform persistence and disappearance of HLB disease. By applying the sensitivity analysis of R₀, we obtain some parameters with the most significant influence on the transmission dynamics of HLB. Moreover, we also obtain that grafting infection has the least influence on the transmission dynamics of HLB. Additionally, a time-dependent control model of HLB to minimize the cost of implementing control efforts and infected trees and ACPs is formulated. By using Pontryagin's Minimum Principle, we obtain the optimal integrated strategy and prove the uniqueness of optimal control solution. The simulation results illustrate that the strategy involving two time-dependent optimal controls is the most effective to suppress the spread of the disease. However, insecticide spraying is more effective measure compared with infected tree removing.

Predicting the effects of climate change on the cross-scale epidemiological dynamics of a fungal plant pathogen

The potential for climate change to exacerbate the burden of human infectious diseases is increasingly recognized, but its effects on infectious diseases of plants have received less attention. Understanding the impacts of climate on the epidemiological dynamics of plant pathogens is imperative, as these organisms play central roles in natural ecosystems and also pose a serious threat to agricultural production and food security. We use the fungal ‘flax rust’ pathogen (Melampsora lini) and its subalpine wildflower host Lewis flax (Linum lewisii) to investigate how climate change might affect the dynamics of fungal plant pathogen epidemics using a combination of empirical and modeling approaches. Our results suggest that climate change will initially slow transmission at both the within- and between-host scales. However, moderate resurgences in disease spread are predicted as warming progresses, especially if the rate of greenhouse gas emissions continues to increase at its current pace. These findings represent an important step towards building a holistic understanding of climate effects on plant infectious disease that encompasses demographic, epidemiological, and evolutionary processes. A core result is that neglecting processes at any one scale of plant pathogen transmission may bias projections of climate effects, as climate drivers have variable and cascading impacts on processes underlying transmission that occur at different scales.

Spatial effects in parasite-induced marine diseases of immobile hosts

Emerging marine infectious diseases pose a substantial threat to marine ecosystems and the conservation of their biodiversity. Compartmental models of epidemic transmission in marine sessile organisms, available only recently, are based on non-spatial descriptions in which space is homogenized and parasite mobility is not explicitly accounted for. However, in realistic scenarios epidemic transmission is conditioned by the spatial distribution of hosts and the parasites' mobility patterns, calling for an explicit description of space. In this work, we develop a spatially explicit individual-based model to study disease transmission by waterborne parasites in sessile marine populations. We investigate the impact of spatial disease transmission through extensive numerical simulations and theoretical analysis. Specifically, the effects of parasite mobility into the epidemic threshold and the temporal progression of the epidemic are assessed. We show that larger values of pathogen mobility imply more severe epidemics, as the number of infections increases, and shorter timescales to extinction. An analytical expression for the basic reproduction number of the spatial model, R ~ 0 , is derived as a function of the non-spatial counterpart, R ₀, which characterizes a transition between a disease-free and a propagation phase, in which the disease propagates over a large fraction of the system.

Modeling Root-Knot Nematode Regulation by the Biocontrol Fungus Pochonia chlamydosporia

Two models of increasing complexity were constructed to simulate the interactions between the root-knot nematode (RKN) Meloidogyne incognita and the biocontrol fungus Pochonia chlamydosporia var. catenulata in a rhizosphere microcosm. The models described discrete population dynamics at hourly rates over a 6-month period and were validated using real parasitism and nematode or fungus data. A first, general Pochonia-nematode-root model (GPNR) used five functions and 16 biological constants. The variables and constants describing the RKN life cycle included the rates of egg production, hatching, juvenile (J2), and mature female development, including root or nematode self-density-dependent factors. Other constants accounted for egg parasitism, nematode-induced root losses, growth, and mortalities. The relationship between nematodes and fungal propagules showed density dependence and cyclic variations in time, including an attractor on the propagules and J2 phases space. The simulations confirmed a P. chlamydosporia optimal initial density of 5 · 103 propagules · cc soil-1, as usually applied in assays. The constants used in GPNR showed adherence to the nematode biology, with 103 eggs per egg mass, a 10-day average lifespan of J2, with 2 days required to enter roots, and adult lifespan lasting 24 days. The fungus propagule lifespan was 25 days, with an average feeder root lifespan lasting around 52 days. A second, more complex Pochonia-nematode-root detailed model (GPNRd) was then constructed using eight functions and 23 constants. It was built as GPNR did not allow the evaluation of host prevalence. GPNRd allowed simulations of all RKN life stages and included non-parasitic and parasitic fungus population fractions. Both GPNR and GPNRd matched real J2 and fungus density data observed in a RKN biocontrol assay. Depending on the starting conditions, simulations showed stability in time, interpreted as effective host regulation. GPNRd showed a fungus cyclic relationship with the J2 numbers, with prevalence data close to those observed (38.3 vs. 39.4%, respectively). This model also showed a further density-independent nematode regulation mechanism based on the P. chlamydosporia switch from a non-parasitic to a parasitic trophic behavior. This mechanism supported the biocontrol of M. incognita, also sustained by a concomitant increase of the root density.

Modelling cassava production and pest management under biotic and abiotic constraints

We summarise modelling studies of the most economically important cassava diseases and arthropods, highlighting research gaps where modelling can contribute to the better management of these in the areas of surveillance, control, and host-pest dynamics understanding the effects of climate change and future challenges in modelling. For over 30 years, experimental and theoretical studies have sought to better understand the epidemiology of cassava diseases and arthropods that affect production and lead to considerable yield loss, to detect and control them more effectively. In this review, we consider the contribution of modelling studies to that understanding. We summarise studies of the most economically important cassava pests, including cassava mosaic disease, cassava brown streak disease, the cassava mealybug, and the cassava green mite. We focus on conceptual models of system dynamics rather than statistical methods. Through our analysis we identified areas where modelling has contributed and areas where modelling can improve and further contribute. Firstly, we identify research challenges in the modelling developed for the surveillance, detection and control of cassava pests, and propose approaches to overcome these. We then look at the contributions that modelling has accomplished in the understanding of the interaction and dynamics of cassava and its' pests, highlighting success stories and areas where improvement is needed. Thirdly, we look at the possibility that novel modelling applications can achieve to provide insights into the impacts and uncertainties of climate change. Finally, we identify research gaps, challenges, and opportunities where modelling can develop and contribute for the management of cassava pests, highlighting the recent advances in understanding molecular mechanisms of plant defence.

Delays in Epidemic Outbreak Control Cost Disproportionately Large Treatment Footprints to Offset

Epidemic outbreak control often involves a spatially explicit treatment area (quarantine, inoculation, ring cull) that covers the outbreak area and adjacent regions where hosts are thought to be latently infected. Emphasis on space however neglects the influence of treatment timing on outbreak control. We conducted field and in silico experiments with wheat stripe rust (WSR), a long-distance dispersed plant disease, to understand interactions between treatment timing and area interact to suppress an outbreak. Full-factorial field experiments with three different ring culls (outbreak area only to a 25-fold increase in treatment area) at three different disease control timings (1.125, 1.25, and 1.5 latent periods after initial disease expression) indicated that earlier treatment timing had a conspicuously greater suppressive effect than the area treated. Disease spread computer simulations over a broad range of influential epidemic parameter values (R0, outbreak disease prevalence, epidemic duration) suggested that potentially unrealistically large increases in treatment area would be required to compensate for even small delays in treatment timing. Although disease surveillance programs are costly, our results suggest that treatments early in an epidemic disease outbreak require smaller areas to be effective, which may ultimately compensate for the upfront costs of proactive disease surveillance programs.

An epi-evolutionary model for predicting the adaptation of spore-producing pathogens to quantitative resistance in heterogeneous environments

We have modeled the evolutionary epidemiology of spore-producing plant pathogens in heterogeneous environments sown with several cultivars carrying quantitative resistances. The model explicitly tracks the infection-age structure and genetic composition of the pathogen population. Each strain is characterized by pathogenicity traits determining its infection efficiency and a time-varying sporulation curve taking into account lesion aging. We first derived a general expression of the basic reproduction number R 0 for fungal pathogens in heterogeneous environments. We show that the evolutionary attractors of the model coincide with local maxima of R 0 only if the infection efficiency is the same on all host types. We then studied the contribution of three basic resistance characteristics (the pathogenicity trait targeted, resistance effectiveness, and adaptation cost), in interaction with the deployment strategy (proportion of fields sown with a resistant cultivar), to (i) pathogen diversification at equilibrium and (ii) the shaping of transient dynamics from evolutionary and epidemiological perspectives. We show that quantitative resistance affecting only the sporulation curve will always lead to a monomorphic population, whereas dimorphism (i.e., pathogen diversification) can occur if resistance alters infection efficiency, notably with high adaptation costs and proportions of the resistant cultivar. Accordingly, the choice of the quantitative resistance genes operated by plant breeders is a driver of pathogen diversification. From an evolutionary perspective, the time to emergence of the evolutionary attractor best adapted to the resistant cultivar tends to be shorter when resistance affects infection efficiency than when it affects sporulation. Conversely, from an epidemiological perspective, epidemiological control is always greater when the resistance affects infection efficiency. This highlights the difficulty of defining deployment strategies for quantitative resistance simultaneously maximizing epidemiological and evolutionary outcomes.

Modelling interference between vectors of non-persistently transmitted plant viruses to identify effective control strategies

Aphids are the primary vector of plant viruses. Transient aphids, which probe several plants per day, are considered to be the principal vectors of non-persistently transmitted (NPT) viruses. However, resident aphids, which can complete their life cycle on a single host and are affected by agronomic practices, can transmit NPT viruses as well. Moreover, they can interfere both directly and indirectly with transient aphids, eventually shaping plant disease dynamics. By means of an epidemiological model, originally accounting for ecological principles and agronomic practices, we explore the consequences of fertilization and irrigation, pesticide deployment and roguing of infected plants on the spread of viral diseases in crops. Our results indicate that the spread of NPT viruses can be i) both reduced or increased by fertilization and irrigation, depending on whether the interference is direct or indirect; ii) counter-intuitively increased by pesticide application and iii) reduced by roguing infected plants. We show that a better understanding of vectors’ interactions would enhance our understanding of disease transmission, supporting the development of disease management strategies.

A range of both experimental and theoretical studies show that the behaviour and population dynamics of insects depend strongly upon interactions with other insect species. These interactions have the potential to greatly affect the dynamics of insect-vectored plant disease, as transmission of viruses is intimately dependent on the local density of vectors, as well as how they select and move between potential host plants. Surprisingly, the effects of interaction between vector species on epidemics remains little studied and even worse understood, probably because experimentation is costly and difficult. Here, we present a model which permits us to investigate the effect of interaction between a virus, two vector species and the host plant on the spread of viral disease in crops. In this study, our model is used to explore the consequences of common agronomic practices on epidemics. Our study highlights the importance of exploring vectors’ interactions to enhance the understanding of disease transmission, supporting the development of disease management strategies.

Evaluating online and tangible interfaces for engaging stakeholders in forecasting and control of biological invasions

Ecological forecasts will be best suited to inform intervention strategies if they are accessible to a diversity of decision-makers. Researchers are developing intuitive forecasting interfaces to guide stakeholders through the development of intervention strategies and visualization of results. Yet, few studies to date have evaluated how user interface design facilitates the coordinated, cross-boundary management required for controlling biological invasions. We used a participatory approach to develop complementary tangible and online interfaces for collaboratively forecasting biological invasions and devising control strategies. A diverse group of stakeholders evaluated both systems in the real-world context of controlling sudden oak death, an emerging forest disease killing millions of trees in California and Oregon. Our findings suggest that while both interfaces encouraged adaptive experimentation, tangible interfaces are particularly well suited to support collaborative decision-making. Reflecting on the strengths of both systems, we suggest workbench-style interfaces that support simultaneous interactions and dynamic geospatial visualizations.

Models of Plant Resistance Deployment

Owing to their evolutionary potential, plant pathogens are able to rapidly adapt to genetically controlled plant resistance, often resulting in resistance breakdown and major epidemics in agricultural crops. Various deployment strategies have been proposed to improve resistance management. Globally, these rely on careful selection of resistance sources and their combination at various spatiotemporal scales (e.g., via gene pyramiding, crop rotations and mixtures, landscape mosaics). However, testing and optimizing these strategies using controlled experiments at large spatiotemporal scales are logistically challenging. Mathematical models provide an alternative investigative tool, and many have been developed to explore resistance deployment strategies under various contexts. This review analyzes 69 modeling studies in light of specific model structures (e.g., demographic or demogenetic, spatial or not), underlying assumptions (e.g., whether preadapted pathogens are present before resistance deployment), and evaluation criteria (e.g., resistance durability, disease control, cost-effectiveness). It highlights major research findings and discusses challenges for future modeling efforts.

Complexity and critical thresholds in the dynamics of visceral leishmaniasis

We study a general multi-host model of visceral leishmaniasis including both humans and animals, and where host and vector characteristics are captured via host competence along with vector biting preference. Additionally, the model accounts for spatial heterogeneity in human population and heterogeneity in biting behaviour of sandflies. We then use parameters for visceral leishmaniasis in the Indian subcontinent as an example and demonstrate that the model exhibits backward bifurcation, i.e. it has a human infection and a sandfly population threshold, characterized by a bi-stable region. These thresholds shift as a function of host competence, host population size, vector feeding preference, spatial heterogeneity, biting heterogeneity and control efforts. In particular, if control is applied through human treatment a new and lower human infection threshold is created, making elimination difficult to achieve, before eventually the human infection threshold no longer exists, making it impossible to control the disease by only reducing the infection levels below a certain threshold. A better strategy would be to reduce the human infection below a certain threshold potentially by early diagnosis, control animal population levels and keep the vector population under check. Spatial heterogeneity in human populations lowers the overall thresholds as a result of weak migration between patches.

Forecasting and control of emerging infectious forest disease through participatory modelling

Epidemiological models are powerful tools for evaluating scenarios and visualizing patterns of disease spread, especially when comparing intervention strategies. However, the technical skill required to synthesize and operate computational models frequently renders them beyond the command of the stakeholders who are most impacted by the results. Participatory modelling (PM) strives to restructure the power relationship between modellers and the stakeholders who rely on model insights by involving these stakeholders directly in model development and application; yet, a systematic literature review indicates little adoption of these techniques in epidemiology, especially plant epidemiology. We investigate the potential for PM to integrate stakeholder and researcher knowledge, using Phytophthora ramorum and the resulting sudden oak death disease as a case study. Recent introduction of a novel strain (European 1 or EU1) in southwestern Oregon has prompted significant concern and presents an opportunity for coordinated management to minimize regional pathogen impacts. Using a PM framework, we worked with local stakeholders to develop an interactive forecasting tool for evaluating landscape-scale control strategies. We find that model co-development has great potential to empower stakeholders in the design, development and application of epidemiological models for disease control.

This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

Expecting the unexpected: factors influencing the emergence of fungal and oomycete plant pathogens

In recent years, the number of emergent plant pathogens (EPPs) has grown substantially, threatening agroecosystem stability and native biodiversity. Contributing factors include, among others, shifts in biogeography, with EPP spread facilitated by the global unification of monocultures in modern agriculture, high volumes of trade in plants and plant products and an increase in sexual recombination within pathogen populations. The unpredictable nature of EPPs as they move into new territories is a situation that has led to sudden and widespread epidemics. Understanding the underlying causes of pathogen emergence is key to managing the impact of EPPs. Here, we review some factors specifically influencing the emergence of oomycete and fungal EPPs, including new introductions through anthropogenic movement, natural dispersal and weather events, as well as genetic factors linked to shifts in host range.

Dispersal Kernels may be Scalable: Implications from a Plant Pathogen

AIM

Understanding how spatial scale of study affects observed dispersal patterns can provide insights into spatiotemporal population dynamics, particularly in systems with significant long-distance dispersal (LDD). We aimed to investigate the dispersal gradients of two rusts of wheat with spores of similar size, mass, and shape, over multiple spatial scales. We hypothesized that a single dispersal kernel could fit the dispersal from all spatial scales well, and that it would be possible to obtain similar results in spatiotemporal increase of disease when modeling based on differing scales.

LOCATION

Central Oregon and St. Croix Island.

TAXA

Puccinia striiformis f. sp. tritici, Puccinia graminis f. sp. tritici, Triticum aestivum.

METHODS

We compared empirically-derived primary disease gradients of cereal rust across three spatial scales: local (inoculum source and sampling unit = 0.0254 m, spatial extent = 1.52m) field-wide (inoculum source = 1.52 m, sampling unit = 0.305 m, and spatial extent = 91.44 m), and regional (inoculum source and sampling unit = 152 m, spatial extent = 10.7 km). We then examined whether disease spread in spatially explicit simulations depended upon the scale at which data were collected by constructing a compartmental time-step model.

RESULTS

The three data sets could be fit well by a single inverse-power law dispersal kernel. Simulating epidemic spread at different spatial resolutions resulted in similar patterns of spatiotemporal spread. Dispersal kernel data obtained at one spatial scale can be used to represent spatiotemporal disease spread at a larger spatial scale.

MAIN CONCLUSIONS

Organisms spread by aerially dispersed small propagules that exhibit LDD may follow similar dispersal patterns over a several hundred- or thousand-fold expanse of spatial scale. Given that the primary mechanisms driving aerial dispersal remain constant, it may be possible to extrapolate across scales when empirical data are unavailable at a scale of interest.

Coupling epidemiological and tree growth models to control fungal diseases spread in fruit orchards

Agronomic practices can alter plant susceptibility to diseases and represent a promising alternative to the use of pesticides. Yet, they also alter crop quality and quantity so that the evaluation of their efficacy is not straightforward. Here we couple a compartmental epidemiological model for brown rot diffusion in fruit orchards with a fruit-tree growth model explicitly considering the role of agronomic practices over fruit quality. The new modelling framework permits us to evaluate, in terms of quantity and quality of the fruit production, management scenarios characterized by different levels of regulated deficit irrigation and crop load. Our results suggest that a moderate water stress in the final weeks of fruit development and a moderate fruit load provide effective control on the brown rot spreading, and eventually guarantee monetary returns similar to those that would be obtained in the absence of the disease.

The global burden of pathogens and pests on major food crops

Crop pathogens and pests reduce the yield and quality of agricultural production. They cause substantial economic losses and reduce food security at household, national and global levels. Quantitative, standardized information on crop losses is difficult to compile and compare across crops, agroecosystems and regions. Here, we report on an expert-based assessment of crop health, and provide numerical estimates of yield losses on an individual pathogen and pest basis for five major crops globally and in food security hotspots. Our results document losses associated with 137 pathogens and pests associated with wheat, rice, maize, potato and soybean worldwide. Our yield loss (range) estimates at a global level and per hotspot for wheat (21.5% (10.1-28.1%)), rice (30.0% (24.6-40.9%)), maize (22.5% (19.5-41.1%)), potato (17.2% (8.1-21.0%)) and soybean (21.4% (11.0-32.4%)) suggest that the highest losses are associated with food-deficit regions with fast-growing populations, and frequently with emerging or re-emerging pests and diseases. Our assessment highlights differences in impacts among crop pathogens and pests and among food security hotspots. This analysis contributes critical information to prioritize crop health management to improve the sustainability of agroecosystems in delivering services to societies.

Lavender Decline in France Is Associated with Chronic Infection by Lavender-Specific Strains of "Candidatus Phytoplasma solani"

The etiology and main pathways for the spread of lavender decline, an infectious disease affecting French lavender production since the 1960s, have remained unclear, hampering the development of efficient control strategies. An extensive survey of lavender fields led to the conclusion that “Candidatus Phytoplasma solani” was chronically infecting declining lavenders and was associated with large infectious populations of Hyalesthes obsoletus planthoppers living on the crop itself. Lavender appeared to be the main reservoir host for lavender-specific phytoplasma strains, an unusual feature for this phytoplasma, which usually propagates from reservoir weeds to various economically important crops. These results point out the necessity to protect young lavender fields from the initial phytoplasma inoculum coming from surrounding lavender fields or from infected nurseries and to promote agricultural practices that reduce the development of H. obsoletus vector populations.

Lavender decline compromises French lavender production, and preliminary data have suggested the involvement of “Candidatus Phytoplasma solani” in the etiology of the disease. In order to evaluate the epidemiological role of “Ca. Phytoplasma solani,” a 3-year survey was conducted in southeastern France. “Ca. Phytoplasma solani” was detected in 19 to 56% of the declining plants, depending on seasons and cultivars, and its prevalence was correlated with symptom severity. Autumn was more favorable than spring for phytoplasma detection, and “Ca. Phytoplasma solani” incidence was higher in Lavandula angustifolia than in Lavandula intermedia hybrids. Detection of the phytoplasma fluctuated over months, supporting the chronicity of infection. Three “Ca. Phytoplasma solani” secY genotypes, S17, S16, and S14, were the most prevalent in lavender fields and were also detected in nurseries, whereas strains detected in surrounding bindweed and wild carrots were mostly of the S1 and S4 genotypes. This suggests that lavender is the main pathogen reservoir of the epidemic. Adults and nymphs of the planthopper vector Hyalesthes obsoletus were commonly captured in lavender fields and were shown to harbor mainly the prevalent phytoplasma genotypes detected in lavenders. The “Ca. Phytoplasma solani” genotype S17 was transmitted to Catharanthus roseus periwinkle by naturally infected H. obsoletus. Finally, the inventory of the bacterial community of declining lavenders that tested negative for “Ca. Phytoplasma solani” by 16S rRNA deep sequencing ruled out the involvement of other phloem-limited bacterial pathogens.

IMPORTANCE The etiology and main pathways for the spread of lavender decline, an infectious disease affecting French lavender production since the 1960s, have remained unclear, hampering the development of efficient control strategies. An extensive survey of lavender fields led to the conclusion that “Candidatus Phytoplasma solani” was chronically infecting declining lavenders and was associated with large infectious populations of Hyalesthes obsoletus planthoppers living on the crop itself. Lavender appeared to be the main reservoir host for lavender-specific phytoplasma strains, an unusual feature for this phytoplasma, which usually propagates from reservoir weeds to various economically important crops. These results point out the necessity to protect young lavender fields from the initial phytoplasma inoculum coming from surrounding lavender fields or from infected nurseries and to promote agricultural practices that reduce the development of H. obsoletus vector populations.

Differential impact of landscape-scale strategies for crop cultivar deployment on disease dynamics, resistance durability and long-term evolutionary control

A multitude of resistance deployment strategies have been proposed to tackle the evolutionary potential of pathogens to overcome plant resistance. In particular, many landscape-based strategies rely on the deployment of resistant and susceptible cultivars in an agricultural landscape as a mosaic. However, the design of such strategies is not easy as strategies targeting epidemiological or evolutionary outcomes may not be the same. Using a stochastic spatially explicit model, we studied the impact of landscape organization (as defined by the proportion of fields cultivated with a resistant cultivar and their spatial aggregation) and key pathogen life-history traits on three measures of disease control. Our results show that short-term epidemiological dynamics are optimized when landscapes are planted with a high proportion of the resistant cultivar in low aggregation. Importantly, the exact opposite situation is optimal for resistance durability. Finally, well-mixed landscapes (balanced proportions with low aggregation) are optimal for long-term evolutionary equilibrium (defined here as the level of long-term pathogen adaptation). This work offers a perspective on the potential for contrasting effects of landscape organization on different goals of disease management and highlights the role of pathogen life history.

Plant Virus Epidemiology: Applications and Prospects for Mathematical Modeling and Analysis to Improve Understanding and Disease Control

In recent years, mathematical modeling has increasingly been used to complement experimental and observational studies of biological phenomena across different levels of organization. In this article, we consider the contribution of mathematical models developed using a wide range of techniques and uses to the study of plant virus disease epidemics. Our emphasis is on the extent to which models have contributed to answering biological questions and indeed raised questions related to the epidemiology and ecology of plant viruses and the diseases caused. In some cases, models have led to direct applications in disease control, but arguably their impact is better judged through their influence in guiding research direction and improving understanding across the characteristic spatiotemporal scales of plant virus epidemics. We restrict this article to plant virus diseases for reasons of length and to maintain focus even though we recognize that modeling has played a major and perhaps greater part in the epidemiology of other plant pathogen taxa, including vector-borne bacteria and phytoplasmas.

Managing Wildlife Faced with Pathogen Risks Involving Multi-Stable Outcomes

Most models designed to understand how to manage infected wildlife systems with bioeconomic multi-stability take the initial conditions as given, thereby treating pathogen invasion as unanticipated. We examine how ex ante management is an opportunity to influence the ex post conditions, which in turn affect the ex post optimal outcome. To capture these ex ante management choices, we extend the Poisson "collapse" model of Reed and Heras (Bull Math Biol 54:185-207, 1992) to allow for endogenous initial conditions and ex post multi-stability. We account for two uncertain processes: the introduction and establishment of the pathogen. Introduction is conditional on anthropogenic investments in prevention, and both random processes are conditional on how we manage the native population to provide natural prevention of invasion and natural insurance against establishment placing the system in an undesirable basin of attraction. We find that both multi-stability of the invaded system and these uncertainty processes can create economic non-convexities that yield multiple candidate solutions to the ex ante optimization problem. Additionally, we illustrate how the nature of natural protection against introduction and establishment risks can play an important role in the allocation of anthropogenic investments.

Control fast or control smart: When should invading pathogens be controlled?

The intuitive response to an invading pathogen is to start disease management as rapidly as possible, since this would be expected to minimise the future impacts of disease. However, since more spread data become available as an outbreak unfolds, processes underpinning pathogen transmission can almost always be characterised more precisely later in epidemics. This allows the future progression of any outbreak to be forecast more accurately, and so enables control interventions to be targeted more precisely. There is also the chance that the outbreak might die out without any intervention whatsoever, making prophylactic control unnecessary. Optimal decision-making involves continuously balancing these potential benefits of waiting against the possible costs of further spread. We introduce a generic, extensible data-driven algorithm based on parameter estimation and outbreak simulation for making decisions in real-time concerning when and how to control an invading pathogen. The Control Smart Algorithm (CSA) resolves the trade-off between the competing advantages of controlling as soon as possible and controlling later when more information has become available. We show-using a generic mathematical model representing the transmission of a pathogen of agricultural animals or plants through a population of farms or fields-how the CSA allows the timing and level of deployment of vaccination or chemical control to be optimised. In particular, the algorithm outperforms simpler strategies such as intervening when the outbreak size reaches a pre-specified threshold, or controlling when the outbreak has persisted for a threshold length of time. This remains the case even if the simpler methods are fully optimised in advance. Our work highlights the potential benefits of giving careful consideration to the question of when to start disease management during emerging outbreaks, and provides a concrete framework to allow policy-makers to make this decision.

Experimental evidence of a pathogen invasion threshold

Host density thresholds to pathogen invasion separate regions of parameter space corresponding to endemic and disease-free states. The host density threshold is a central concept in theoretical epidemiology and a common target of human and wildlife disease control programmes, but there is mixed evidence supporting the existence of thresholds, especially in wildlife populations or for pathogens with complex transmission modes (e.g. environmental transmission). Here, we demonstrate the existence of a host density threshold for an environmentally transmitted pathogen by combining an epidemiological model with a microcosm experiment. Experimental epidemics consisted of replicate populations of naive crustacean zooplankton (Daphnia dentifera) hosts across a range of host densities (20-640 hosts l-1) that were exposed to an environmentally transmitted fungal pathogen (Metschnikowia bicuspidata). Epidemiological model simulations, parametrized independently of the experiment, qualitatively predicted experimental pathogen invasion thresholds. Variability in parameter estimates did not strongly influence outcomes, though systematic changes to key parameters have the potential to shift pathogen invasion thresholds. In summary, we provide one of the first clear experimental demonstrations of pathogen invasion thresholds in a replicated experimental system, and provide evidence that such thresholds may be predictable using independently constructed epidemiological models.

A Framework for Optimizing Phytosanitary Thresholds in Seed Systems

Seedborne pathogens and pests limit production in many agricultural systems. Quarantine programs help prevent the introduction of exotic pathogens into a country, but few regulations directly apply to reducing the reintroduction and spread of endemic pathogens. Use of phytosanitary thresholds helps limit the movement of pathogen inoculum through seed, but the costs associated with rejected seed lots can be prohibitive for voluntary implementation of phytosanitary thresholds. In this paper, we outline a framework to optimize thresholds for seedborne pathogens, balancing the cost of rejected seed lots and benefit of reduced inoculum levels. The method requires relatively small amounts of data, and the accuracy and robustness of the analysis improves over time as data accumulate from seed testing. We demonstrate the method first and illustrate it with a case study of seedborne oospores of Peronospora effusa, the causal agent of spinach downy mildew. A seed lot threshold of 0.23 oospores per seed could reduce the overall number of oospores entering the production system by 90% while removing 8% of seed lots destined for distribution. Alternative mitigation strategies may result in lower economic losses to seed producers, but have uncertain efficacy. We discuss future challenges and prospects for implementing this approach.

What a Difference a Stochastic Process Makes: Epidemiological-Based Real Options Models of Optimal Treatment of Disease

The real options approach has been used within environmental economics to investigate the impact of uncertainty on the optimal timing of control measures to minimise the impacts of invasive species, including pests and diseases. Previous studies typically model the growth in infected area using geometric Brownian motion (GBM). The advantage of this simple approach is that it allows for closed form solutions. However, such a process does not capture the mechanisms underlying the spread of infection. In particular the GBM assumption does not respect the natural upper boundary of the system, which is determined by the maximum size of the host species, nor the deceleration in the rate of infection as this boundary is approached. We show how the stochastic process describing the growth in infected area can be derived from the characteristics of the spread of infection. If the model used does not appropriately capture uncertainty in infection dynamics, then the excessive delay before treatment implies that the full value of the option to treat is not realised. Indeed, when uncertainty is high or the disease is fast spreading, ignoring the mechanisms of infection spread can lead to control never being deployed. Thus the results presented here have important implications for the way in which the real options approach is applied to determine optimal timing of disease control given uncertainty in future disease progression.

Ecology of Fungal Plant Pathogens

Fungal plant pathogens are ubiquitous and highly diverse. Key to their success is high host density, which notably is the case in agroecosystems. Several hypotheses related to the effects of plant pathogens on plant diversity (the Janzen-Connell hypothesis, the dilution effect hypothesis) and the phenomenon of higher biomass in plant mixtures (i.e., overyielding) can all be explained by the quantitative interplay between host and pathogen density. In many agroecosystems, fungal plant pathogens cause great losses, since in monocultures diseased plants cannot be replaced by healthy plants. On the other hand, in natural ecosystems fungal plant pathogens shape the succession of vegetation and enhance the biodiversity of forests and grasslands. When pathogens are introduced into areas outside their natural range, they may behave differently, causing severe damage. Once introduced, changes may occur such as hybridization with other closely related pathogens or host shifts, host jumps, or horizontal gene transfer. Such changes can be hazardous for both agricultural and natural ecosystems.

Mathematical models are a powerful method to understand and control the spread of Huanglongbing

Huanglongbing (HLB), or citrus greening, is a global citrus disease occurring in almost all citrus growing regions. It causes substantial economic burdens to individual growers, citrus industries and governments. Successful management strategies to reduce disease burden are desperately needed but with so many possible interventions and combinations thereof it is difficult to know which are worthwhile or cost-effective. We review how mathematical models have yielded useful insights into controlling disease spread for other vector-borne plant diseases, and the small number of mathematical models of HLB. We adapt a malaria model to HLB, by including temperature-dependent psyllid traits, "flushing" of trees, and economic costs, to show how models can be used to highlight the parameters that require more data collection or that should be targeted for intervention. We analyze the most common intervention strategy, insecticide spraying, to determine the most cost-effective spraying strategy. We find that fecundity and feeding rate of the vector require more experimental data collection, for wider temperatures ranges. Also, the best strategy for insecticide intervention is to spray for more days rather than pay extra for a more efficient spray. We conclude that mathematical models are able to provide useful recommendations for managing HLB spread.

A curious case of resistance to a new encounter pathogen: myrtle rust in Australia

Resistance genes (R genes) in plants mediate a highly specific response to microbial pathogens, often culminating in localized cell death. Such resistance is generally pathogen race specific and believed to be the result of evolutionary selection pressure. Where a host and pathogen do not share an evolutionary history, specific resistance is expected to be absent or rare. Puccinia psidii, the causal agent of myrtle rust, was recently introduced to Australia, a continent rich in myrtaceous taxa. Responses within species to this new pathogen range from full susceptibility to resistance. Using the myrtle rust case study, we examine models to account for the presence of resistance to new encounter pathogens, such as the retention of ancient R genes through prolonged 'trench warfare', pairing of resistance gene products and the guarding of host integrity.

Cassava brown streak disease and the sustainability of a clean seed system

One method of reducing disease in crops is the dissemination of disease-free planting material from a multiplication site to growers. This study assesses the validity and sustainability of this method for cassava brown streak disease, a threat to cassava crops across East Africa. Using mathematical modelling, the effects of different environmental and control conditions on pathogen spread were determined in a single-field multiplication site. High disease pressure, through large vector populations and disease in the surrounding area, combined with poor roguing practice, resulted in unsuccessful disease suppression. However, fields may produce sufficiently clean material for replanting if these factors can be overcome. Assessing the sustainability of a low-pressure system over multiple harvests, well-managed fields were found to maintain low disease levels, although producing sufficient cuttings may prove challenging. Replanting fields from the previous harvest does not lead to degeneration of planting material, only cutting numbers, and the importation of new clean material is not necessarily required. It is recommended that multiplication sites are only established in areas of low disease pressure and vector population density, and the importance of training in field management is emphasized. Cultivars displaying strong foliar symptoms are to be encouraged, as these allow for effective roguing, resulting in negative selection against the disease and reducing its spread. Finally, efforts to increase plant multiplication rates, the number of cuttings that can be obtained from each plant, have a significant impact on the sustainability of sites, as this represents the primary limiting factor to success.

History, Epidemic Evolution, and Model Burn-In for a Network of Annual Invasion: Soybean Rust

Ecological history may be an important driver of epidemics and disease emergence. We evaluated the role of history and two related concepts, the evolution of epidemics and the burn-in period required for fitting a model to epidemic observations, for the U.S. soybean rust epidemic (caused by Phakopsora pachyrhizi). This disease allows evaluation of replicate epidemics because the pathogen reinvades the United States each year. We used a new maximum likelihood estimation approach for fitting the network model based on observed U.S. epidemics. We evaluated the model burn-in period by comparing model fit based on each combination of other years of observation. When the miss error rates were weighted by 0.9 and false alarm error rates by 0.1, the mean error rate did decline, for most years, as more years were used to construct models. Models based on observations in years closer in time to the season being estimated gave lower miss error rates for later epidemic years. The weighted mean error rate was lower in backcasting than in forecasting, reflecting how the epidemic had evolved. Ongoing epidemic evolution, and potential model failure, can occur because of changes in climate, host resistance and spatial patterns, or pathogen evolution.

The effect of time delay in plant--pathogen interactions with host demography

Botanical epidemic models are very important tools to study invasion, persistence and control of diseases. It is well known that limitations arise from considering constant infection rates. We replace this hypothesis in the framework of delay differential equations by proposing a delayed epidemic model for plant--pathogen interactions with host demography. Sufficient conditions for the global stability of the pathogen-free equilibrium and the permanence of the system are among the results obtained through qualitative analysis. We also show that the delay can cause stability switches of the coexistence equilibrium. In the undelayed case, we prove that the onset of oscillations may occur through Hopf bifurcation.

Optimising and communicating options for the control of invasive plant disease when there is epidemiological uncertainty

Although local eradication is routinely attempted following introduction of disease into a new region, failure is commonplace. Epidemiological principles governing the design of successful control are not well-understood. We analyse factors underlying the effectiveness of reactive eradication of localised outbreaks of invading plant disease, using citrus canker in Florida as a case study, although our results are largely generic, and apply to other plant pathogens (as we show via our second case study, citrus greening). We demonstrate how to optimise control via removal of hosts surrounding detected infection (i.e. localised culling) using a spatially-explicit, stochastic epidemiological model. We show how to define optimal culling strategies that take account of stochasticity in disease spread, and how the effectiveness of disease control depends on epidemiological parameters determining pathogen infectivity, symptom emergence and spread, the initial level of infection, and the logistics and implementation of detection and control. We also consider how optimal culling strategies are conditioned on the levels of risk acceptance/aversion of decision makers, and show how to extend the analyses to account for potential larger-scale impacts of a small-scale outbreak. Control of local outbreaks by culling can be very effective, particularly when started quickly, but the optimum strategy and its performance are strongly dependent on epidemiological parameters (particularly those controlling dispersal and the extent of any cryptic infection, i.e. infectious hosts prior to symptoms), the logistics of detection and control, and the level of local and global risk that is deemed to be acceptable. A version of the model we developed to illustrate our methodology and results to an audience of stakeholders, including policy makers, regulators and growers, is available online as an interactive, user-friendly interface at http://www.webidemics.com/. This version of our model allows the complex epidemiological principles that underlie our results to be communicated to a non-specialist audience.

Prior population immunity reduces the expected impact of CTL-inducing vaccines for pandemic influenza control

Vaccines that trigger an influenza-specific cytotoxic T cell (CTL) response may aid pandemic control by limiting the transmission of novel influenza A viruses (IAV). We consider interventions with hypothetical CTL-inducing vaccines in a range of epidemiologically plausible pandemic scenarios. We estimate the achievable reduction in the attack rate, and, by adopting a model linking epidemic progression to the emergence of IAV variants, the opportunity for antigenic drift. We demonstrate that CTL-inducing vaccines have limited utility for modifying population-level outcomes if influenza-specific T cells found widely in adults already suppress transmission and prove difficult to enhance. Administration of CTL-inducing vaccines that are efficacious in "influenza-experienced" and "influenza-naive" hosts can likely slow transmission sufficiently to mitigate a moderate IAV pandemic. However if neutralising cross-reactive antibody to an emerging IAV are common in influenza-experienced hosts, as for the swine-variant H3N2v, boosting CTL immunity may be ineffective at reducing population spread, indicating that CTL-inducing vaccines are best used against novel subtypes such as H7N9. Unless vaccines cannot readily suppress transmission from infected hosts with naive T cell pools, targeting influenza-naive hosts is preferable. Such strategies are of enhanced benefit if naive hosts are typically intensively mixing children and when a subset of experienced hosts have pre-existing neutralising cross-reactive antibody. We show that CTL-inducing vaccination campaigns may have greater power to suppress antigenic drift than previously suggested, and targeting adults may be the optimal strategy to achieve this when the vaccination campaign does not have the power to curtail the attack rate. Our results highlight the need to design interventions based on pre-existing cellular immunity and knowledge of the host determinants of vaccine efficacy, and provide a framework for assessing the performance requirements of high-impact CTL-inducing vaccines.

Modeling climate impact on an emerging disease, the Phytophthora alni-induced alder decline

Alder decline caused by Phytophthora alni is one of the most important emerging diseases in natural ecosystems in Europe, where it has threatened riparian ecosystems for the past 20 years. Environmental factors, such as mean site temperature and soil characteristics, play an important role in the occurrence of the disease. The objective of the present work was to model and forecast the effect of environment on the severity of alder Phytophthora outbreaks, and to determine whether recent climate change might explain the disease emergence. Two alder sites networks in NE and SW France were surveyed to assess the crown health of trees; the oomycete soil inoculum was also monitored in the NE network. The main factors explaining the temporal annual variation in alder crown decline or crown recovery were the mean previous winter and previous summer temperatures. Both low winter temperatures and high summer temperatures were unfavorable to the disease. Cold winters promoted tree recovery because of poor survival of the pathogen, while hot summer temperature limited the incidence of tree decline. An SIS model explaining the dynamics of the P. alni-induced alder decline was developed using the data of the NE site network and validated using the SW site network. This model was then used to simulate the frequency of declining alder over time with historical climate data. The last 40 years' weather conditions have been generally favorable to the establishment of the disease, indicating that others factors may be implicated in its emergence. The model, however, showed that the climate of SW France was much more favorable for the disease than that of the Northeast, because it seldom limited the overwintering of the pathogen. Depending on the European area, climate change could either enhance or decrease the severity of the alder decline.

Development and validation of a weather-based model for predicting infection of loquat fruit by Fusicladium eriobotryae

A mechanistic, dynamic model was developed to predict infection of loquat fruit by conidia of Fusicladium eriobotryae, the causal agent of loquat scab. The model simulates scab infection periods and their severity through the sub-processes of spore dispersal, infection, and latency (i.e., the state variables); change from one state to the following one depends on environmental conditions and on processes described by mathematical equations. Equations were developed using published data on F. eriobotryae mycelium growth, conidial germination, infection, and conidial dispersion pattern. The model was then validated by comparing model output with three independent data sets. The model accurately predicts the occurrence and severity of infection periods as well as the progress of loquat scab incidence on fruit (with concordance correlation coefficients >0.95). Model output agreed with expert assessment of the disease severity in seven loquat-growing seasons. Use of the model for scheduling fungicide applications in loquat orchards may help optimise scab management and reduce fungicide applications.

A model for multiseasonal spread of verticillium wilt of lettuce

Verticillium wilt, caused by Verticillium dahliae, is a destructive disease in lettuce, and the pathogen is seedborne. Even though maximum seed infestation rates of <5% have been detected in commercial lettuce seed lots, it is necessary to establish acceptable contamination thresholds to prevent introduction and establishment of the pathogen in lettuce production fields. However, introduction of inoculum into lettuce fields for experimental purposes to determine its long term effects is undesirable. Therefore, we constructed a simulation model to study the spread of Verticillium wilt following pathogen introduction from seed. The model consists of four components: the first for simulating infection of host plants, the second for simulating reproduction of microsclerotia on diseased plants, the third for simulating the survival of microsclerotia, and the fourth for simulating the dispersal of microsclerotia. The simulation results demonstrated that the inoculum density-disease incidence curve parameters and the dispersal gradients affect disease spread in the field. Although a steep dispersal gradient facilitated the establishment of the disease in a new field with a low inoculum density, a long-tail gradient allowed microsclerotia to be dispersed over greater distances, promoting the disease spread in fields with high inoculum density. The simulation results also revealed the importance of avoiding successive lettuce crops in the same field, reducing survival rate of microsclerotia between crops, and the need for breeding resistance against V. dahliae in lettuce cultivars to lower the number of microsclerotia formed on each diseased plant. The simulation results, however, suggested that, even with a low seed infestation rate, the pathogen would eventually become established if susceptible lettuce cultivars were grown consecutively in the same field for many years. A threshold for seed infestation can be established only when two of the three drivers of the disease-(i) low microsclerotia production per diseased plant, (ii) long-tail dispersal gradient, and (iii) low microsclerotia survival between lettuce crops-are present.

Cost-effective control of plant disease when epidemiological knowledge is incomplete: modelling Bahia bark scaling of citrus

A spatially-explicit, stochastic model is developed for Bahia bark scaling, a threat to citrus production in north-eastern Brazil, and is used to assess epidemiological principles underlying the cost-effectiveness of disease control strategies. The model is fitted via Markov chain Monte Carlo with data augmentation to snapshots of disease spread derived from a previously-reported multi-year experiment. Goodness-of-fit tests strongly supported the fit of the model, even though the detailed etiology of the disease is unknown and was not explicitly included in the model. Key epidemiological parameters including the infection rate, incubation period and scale of dispersal are estimated from the spread data. This allows us to scale-up the experimental results to predict the effect of the level of initial inoculum on disease progression in a typically-sized citrus grove. The efficacies of two cultural control measures are assessed: altering the spacing of host plants, and roguing symptomatic trees. Reducing planting density can slow disease spread significantly if the distance between hosts is sufficiently large. However, low density groves have fewer plants per hectare. The optimum density of productive plants is therefore recovered at an intermediate host spacing. Roguing, even when detection of symptomatic plants is imperfect, can lead to very effective control. However, scouting for disease symptoms incurs a cost. We use the model to balance the cost of scouting against the number of plants lost to disease, and show how to determine a roguing schedule that optimises profit. The trade-offs underlying the two optima we identify-the optimal host spacing and the optimal roguing schedule-are applicable to many pathosystems. Our work demonstrates how a carefully parameterised mathematical model can be used to find these optima. It also illustrates how mathematical models can be used in even this most challenging of situations in which the underlying epidemiology is ill-understood.

Comparative population genomics of the Borrelia burgdorferi species complex reveals high degree of genetic isolation among species and underscores benefits and constraints to studying intra-specific epidemiological processes

Lyme borreliosis, one of the most frequently contracted zoonotic diseases in the Northern Hemisphere, is caused by bacteria belonging to different genetic groups within the Borrelia burgdorferi species complex, which are transmitted by ticks among various wildlife reservoirs, such as small mammals and birds. These features make the Borrelia burgdorferi species complex an attractive biological model that can be used to study the diversification and the epidemiology of endemic bacterial pathogens. We investigated the potential of population genomic approaches to study these processes. Sixty-three strains belonging to three species within the Borrelia burgdorferi complex were isolated from questing ticks in Alsace (France), a region where Lyme disease is highly endemic. We first aimed to characterize the degree of genetic isolation among the species sampled. Phylogenetic and coalescent-based analyses revealed clear delineations: there was a ∼50 fold difference between intra-specific and inter-specific recombination rates. We then investigated whether the population genomic data contained information of epidemiological relevance. In phylogenies inferred using most of the genome, conspecific strains did not cluster in clades. These results raise questions about the relevance of different strategies when investigating pathogen epidemiology. For instance, here, both classical analytic approaches and phylodynamic simulations suggested that population sizes and migration rates were higher in B. garinii populations, which are normally associated with birds, than in B. burgdorferi s.s. populations. The phylogenetic analyses of the infection-related ospC gene and its flanking region provided additional support for this finding. Traces of recombination among the B. burgdorferi s.s. lineages and lineages associated with small mammals were found, suggesting that they shared the same hosts. Altogether, these results provide baseline evidence that can be used to formulate hypotheses regarding the host range of B. burgdorferi lineages based on population genomic data.

Programmed Allee effect in bacteria causes a tradeoff between population spread and survival

Dispersal is necessary for spread into new habitats, but it has also been shown to inhibit spread. Theoretical studies have suggested that the presence of a strong Allee effect may account for these counterintuitive observations. Experimental demonstration of this notion is lacking due to the difficulty in quantitative analysis of such phenomena in a natural setting. We engineered Escherichia coli to exhibit a strong Allee effect and examined how the Allee effect would affect the spread of the engineered bacteria. We showed that the Allee effect led to a biphasic dependence of bacterial spread on the dispersal rate: spread is promoted for intermediate dispersal rates but inhibited at low or high dispersal rates. The shape of this dependence is contingent upon the initial density of the source population. Moreover, the Allee effect led to a tradeoff between effectiveness of population spread and survival: increasing the number of target patches during dispersal allows more effective spread, but it simultaneously increases the risk of failing to invade or of going extinct. We also observed that total population growth is transiently maximized at an intermediate number of target patches. Finally, we demonstrate that fluctuations in cell growth may contribute to the paradoxical relationship between dispersal and spread. Our results provide direct experimental evidence that the Allee effect can explain the apparently paradoxical effects of dispersal on spread and have implications for guiding the spread of cooperative organisms.

Impact of viral drift on vaccination dynamics and patterns of seasonal influenza

BACKGROUND

Much research has been devoted to the determination of optimal vaccination strategies for seasonal influenza epidemics. However, less attention has been paid to whether this optimization can be achieved within the context of viral drift.

METHODS

We developed a mathematical model that links different intra-seasonal dynamics of vaccination and infection to investigate the effect of viral drift on optimal vaccination for minimizing the total number of infections. The model was computationally implemented using a seasonal force of infection, with estimated parameters from the published literature.

RESULTS

Simulation results show that the pattern of large seasonal epidemics is strongly correlated with the duration of specific cross-protection immunity induced by natural infection. Considering a random vaccination, our simulations suggest that the effect of vaccination on epidemic patterns is largely influenced by the duration of protection induced by strain-specific vaccination. We found that the protection efficacy (i.e., reduction of susceptibility to infection) of vaccine is a parameter that could influence these patterns, particularly when the duration of vaccine-induced cross-protection is lengthened.

CONCLUSIONS

Given the uncertainty in the timing and nature of antigenically drifted variants, the findings highlight the difficulty in determining optimal vaccination dynamics for seasonal epidemics. Our study suggests that the short- and long-term impacts of vaccination on seasonal epidemics should be evaluated within the context of population-pathogen landscape for influenza evolution.

The consequence of tree pests and diseases for ecosystem services

Trees and forests provide a wide variety of ecosystem services in addition to timber, food, and other provisioning services. New approaches to pest and disease management are needed that take into account these multiple services and the different stakeholders they benefit, as well as the likelihood of greater threats in the future resulting from globalization and climate change. These considerations will affect priorities for both basic and applied research and how trade and phytosanitary regulations are formulated.

Mortality and community changes drive sudden oak death impacts on litterfall and soil nitrogen cycling

Few studies have quantified pathogen impacts to ecosystem processes, despite the fact that pathogens cause or contribute to regional-scale tree mortality. We measured litterfall mass, litterfall chemistry, and soil nitrogen (N) cycling associated with multiple hosts along a gradient of mortality caused by Phytophthora ramorum, the cause of sudden oak death. In redwood forests, the epidemiological and ecological characteristics of the major overstory species determine disease patterns and the magnitude and nature of ecosystem change. Bay laurel (Umbellularia californica) has high litterfall N (0.992%), greater soil extractable NO3 -N, and transmits infection without suffering mortality. Tanoak (Notholithocarpus densiflorus) has moderate litterfall N (0.723%) and transmits infection while suffering extensive mortality that leads to higher extractable soil NO3 -N. Redwood (Sequoia sempervirens) has relatively low litterfall N (0.519%), does not suffer mortality or transmit the pathogen, but dominates forest biomass. The strongest impact of pathogen-caused mortality was the potential shift in species composition, which will alter litterfall chemistry, patterns and dynamics of litterfall mass, and increase soil NO3 -N availability. Patterns of P. ramorum spread and consequent mortality are closely associated with bay laurel abundances, suggesting this species will drive both disease emergence and subsequent ecosystem function.