A probabilistic census-travel model to predict introduction sites of exotic plant, animal and human pathogens

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.

Population Genomics Provide Insights into the Global Genetic Structure of Colletotrichum graminicola, the Causal Agent of Maize Anthracnose

Understanding the genetic diversity and mechanisms underlying genetic variation in pathogen populations is crucial to the development of effective control strategies. We investigated the genetic diversity and reproductive biology of Colletotrichum graminicola isolates which infect maize by sequencing the genomes of 108 isolates collected from 14 countries using restriction site-associated DNA sequencing (RAD-seq) and whole-genome sequencing (WGS). Clustering analyses based on single-nucleotide polymorphisms revealed three genetic groups delimited by continental origin, compatible with short-dispersal of the pathogen and geographic subdivision. Intra- and intercontinental migration was observed between Europe and South America, likely associated with the movement of contaminated germplasm. Low clonality, evidence of genetic recombination, and high phenotypic diversity were detected. We show evidence that, although it is rare (possibly due to losses of sexual reproduction- and meiosis-associated genes) C. graminicola can undergo sexual recombination. Our results support the hypotheses that intra- and intercontinental pathogen migration and genetic recombination have great impacts on the C. graminicola population structure. IMPORTANCE Plant pathogens cause significant reductions in yield and crop quality and cause enormous economic losses worldwide. Reducing these losses provides an obvious strategy to increase food production without further degrading natural ecosystems; however, this requires knowledge of the biology and evolution of the pathogens in agroecosystems. We employed a population genomics approach to investigate the genetic diversity and reproductive biology of the maize anthracnose pathogen (Colletotrichum graminicola) in 14 countries. We found that the populations are correlated with their geographical origin and that migration between countries is ongoing, possibly caused by the movement of infected plant material. This result has direct implications for disease management because migration can cause the movement of more virulent and/or fungicide-resistant genotypes. We conclude that genetic recombination is frequent (in contrast to the traditional view of C. graminicola being mainly asexual), which strongly impacts control measures and breeding programs aimed at controlling this disease.

Optimising risk-based surveillance for early detection of invasive plant pathogens

Emerging infectious diseases (EIDs) of plants continue to devastate ecosystems and livelihoods worldwide. Effective management requires surveillance to detect epidemics at an early stage. However, despite the increasing use of risk-based surveillance programs in plant health, it remains unclear how best to target surveillance resources to achieve this. We combine a spatially explicit model of pathogen entry and spread with a statistical model of detection and use a stochastic optimisation routine to identify which arrangement of surveillance sites maximises the probability of detecting an invading epidemic. Our approach reveals that it is not always optimal to target the highest-risk sites and that the optimal strategy differs depending on not only patterns of pathogen entry and spread but also the choice of detection method. That is, we find that spatial correlation in risk can make it suboptimal to focus solely on the highest-risk sites, meaning that it is best to avoid ‘putting all your eggs in one basket’. However, this depends on an interplay with other factors, such as the sensitivity of available detection methods. Using the economically important arboreal disease huanglongbing (HLB), we demonstrate how our approach leads to a significant performance gain and cost saving in comparison with conventional methods to targeted surveillance.

Emerging infectious diseases of plants continue to devastate ecosystems and livelihoods worldwide. By linking a mathematical model of pest spread with a computational optimisation routine, this study identifies where to look for invasive pests if we wish to detect them at an early stage; this method improves upon conventional methods of risk-based surveillance and is robust to model misspecification.

Pathologists and entomologists must join forces against forest pest and pathogen invasions

The world’s forests have never been more threatened by invasions of exotic pests and pathogens, whose causes and impacts are reinforced by global change. However, forest entomologists and pathologists have, for too long, worked independently, used different concepts and proposed specific management methods without recognising parallels and synergies between their respective fields. Instead, we advocate increased collaboration between these two scientific communities to improve the long-term health of forests.

Our arguments are that the pathways of entry of exotic pests and pathogens are often the same and that insects and fungi often coexist in the same affected trees. Innovative methods for preventing invasions, early detection and identification of non-native species, modelling of their impact and spread and prevention of damage by increasing the resistance of ecosystems can be shared for the management of both pests and diseases.

We, therefore, make recommendations to foster this convergence, proposing in particular the development of interdisciplinary research programmes, the development of generic tools or methods for pest and pathogen management and capacity building for the education and training of students, managers, decision-makers and citizens concerned with forest health.

Preface to theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'

This preface forms 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'.

Detection, forecasting and control of infectious disease epidemics: modelling outbreaks in humans, animals and plants

The 1918 influenza pandemic is one of the most devastating infectious disease epidemics on record, having caused approximately 50 million deaths worldwide. Control measures, including prohibiting non-essential gatherings as well as closing cinemas and music halls, were applied with varying success and limited knowledge of transmission dynamics. One hundred years later, following developments in the field of mathematical epidemiology, models are increasingly used to guide decision-making and devise appropriate interventions that mitigate the impacts of epidemics. Epidemiological models have been used as decision-making tools during outbreaks in human, animal and plant populations. However, as the subject has developed, human, animal and plant disease modelling have diverged. Approaches have been developed independently for pathogens of each host type, often despite similarities between the models used in these complementary fields. With the increased importance of a One Health approach that unifies human, animal and plant health, we argue that more inter-disciplinary collaboration would enhance each of the related disciplines. This pair of theme issues presents research articles written by human, animal and plant disease modellers. In this introductory article, we compare the questions pertinent to, and approaches used by, epidemiological modellers of human, animal and plant pathogens, and summarize the articles in these theme issues. We encourage future collaboration that transcends disciplinary boundaries and links the closely related areas of human, animal and plant disease epidemic modelling. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.

Canine olfactory detection of a vectored phytobacterial pathogen, Liberibacter asiaticus, and integration with disease control

Exotic infectious pathogens, like citrus huanglongbing (HLB), are increasingly introduced into agrosystems. Early detection is the key to mitigating their destructive effects. Human visual assessment is insufficiently sensitive to detect new plant infections in a responsive timeframe, and molecular assays are expensive and not easily deployable over large crop landscapes. We turned to detector dogs, an ancient technology, which can rapidly survey large plantings without laborious sample collection or laboratory processing. Dogs detected infections (>99% accuracy) weeks to years prior to visual survey and molecular methods and were highly specific, accurately discriminating target pathogens from other pathogens. Epidemiological models indicated that dogs were more effective and economical than current early detection methods for sustainable disease control.

Early detection and rapid response are crucial to avoid severe epidemics of exotic pathogens. However, most detection methods (molecular, serological, chemical) are logistically limited for large-scale survey of outbreaks due to intrinsic sampling issues and laboratory throughput. Evaluation of 10 canines trained for detection of a severe exotic phytobacterial arboreal pathogen, Candidatus Liberibacter asiaticus (CLas), demonstrated 0.9905 accuracy, 0.8579 sensitivity, and 0.9961 specificity. In a longitudinal study, cryptic CLas infections that remained subclinical visually were detected within 2 wk postinfection compared with 1 to 32 mo for qPCR. When allowed to interrogate a diverse range of in vivo pathogens infecting an international citrus pathogen collection, canines only reacted to Liberibacter pathogens of citrus and not to other bacterial, viral, or spiroplasma pathogens. Canines trained to detect CLas-infected citrus also alerted on CLas-infected tobacco and periwinkle, CLas-bearing psyllid insect vectors, and CLas cocultured with other bacteria but at CLas titers below the level of molecular detection. All of these observations suggest that canines can detect CLas directly rather than only host volatiles produced by the infection. Detection in orchards and residential properties was real time, ∼2 s per tree. Spatiotemporal epidemic simulations demonstrated that control of pathogen prevalence was possible and economically sustainable when canine detection was followed by intervention (i.e., culling infected individuals), whereas current methods of molecular (qPCR) and visual detection failed to contribute to the suppression of an exponential trajectory of infection.

Emergence of a Plant Pathogen in Europe Associated with Multiple Intercontinental Introductions

Pathogen introductions have led to numerous disease outbreaks in naive regions of the globe. The plant pathogen Xylella fastidiosa has been associated with various recent epidemics in Europe affecting agricultural crops, such as almond, grapevine, and olive, but also endemic species occurring in natural forest landscapes and ornamental plants. We compared whole-genome sequences of X. fastidiosa subspecies multiplex from America and strains associated with recent outbreaks in southern Europe to infer their likely origins and paths of introduction within and between the two continents. Phylogenetic analyses indicated multiple introductions of X. fastidiosa subspecies multiplex into Italy, Spain, and France, most of which emerged from a clade with limited genetic diversity with a likely origin in California, USA. The limited genetic diversity observed in X. fastidiosa subspecies multiplex strains originating from California is likely due to the clade itself being an introduction from X. fastidiosa subspecies multiplex populations in the southeastern United States, where this subspecies is most likely endemic. Despite the genetic diversity found in some areas in Europe, there was no clear evidence of recombination occurring among introduced X. fastidiosa strains in Europe. Sequence type taxonomy, based on multilocus sequence typing (MLST), was shown, at least in one case, to not lead to monophyletic clades of this pathogen; whole-genome sequence data were more informative in resolving the history of introductions than MLST data. Although additional data are necessary to carefully tease out the paths of these recent dispersal events, our results indicate that whole-genome sequence data should be considered when developing management strategies for X. fastidiosa outbreaks.IMPORTANCEXylella fastidiosa is an economically important plant-pathogenic bacterium that has emerged as a pathogen of global importance associated with a devastating epidemic in olive trees in Italy associated with X. fastidiosa subspecies pauca and other outbreaks in Europe, such as X. fastidiosa subspecies fastidiosa and X. fastidiosa subspecies multiplex in Spain and X. fastidiosa subspecies multiplex in France. We present evidence of multiple introductions of X. fastidiosa subspecies multiplex, likely from the United States, into Spain, Italy, and France. These introductions illustrate the risks associated with the commercial trade of plant material at global scales and the need to develop effective policy to limit the likelihood of pathogen pollution into naive regions. Our study demonstrates the need to utilize whole-genome sequence data to study X. fastidiosa introductions at outbreak stages, since a limited number of genetic markers does not provide sufficient phylogenetic resolution to determine dispersal paths or relationships among strains that are of biological and quarantine relevance.