1,602 research outputs found

    Early warning signals in plant disease outbreaks

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    Infectious disease outbreaks in plants threaten ecosystems, agricultural crops and food trade. Currently, several fungal diseases are affecting forests worldwide, posing a major risk to tree species, habitats and consequently ecosystem decay. Prediction and control of disease spread are difficult, mainly due to the complexity of the interaction between individual components involved. In this work, we introduce a lattice-based epidemic model coupled with a stochastic process that mimics, in a very simplified way, the interaction between the hosts and pathogen. We studied the disease spread by measuring the propagation velocity of the pathogen on the susceptible hosts. Our quantitative results indicate the occurrence of a critical transition between two stable phases: local confinement and an extended epiphytotic outbreak that depends on the density of the susceptible individuals. Quantitative predictions of epiphytotics are performed using the framework early-warning indicators for impending regime shifts, widely applied on dynamical systems. These signals forecast successfully the outcome of the critical shift between the two stable phases before the system enters the epiphytotic regime. Our study demonstrates that early-warning indicators could be useful for the prediction of forest disease epidemics through mathematical and computational models suited to more specific pathogen–host-environmental interactions. Our results may also be useful to identify a suitable planting density to slow down disease spread and in the future, design highly resilient forests

    The Importance of Being Hybrid for Spatial Epidemic Models: A Multi-Scale Approach

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    International audienceThis work addresses the spread of a disease within an urban system, defined as a network of interconnected cities. The first step consists of comparing two different approaches: a macroscopic one, based on a system of coupled Ordinary Differential Equations (ODE) Susceptible-Infected-Recovered (SIR) systems exploiting populations on nodes and flows on edges (so-called metapopulational model), and a hybrid one, coupling ODE SIR systems on nodes and agents traveling on edges. Under homogeneous conditions (mean field approximation), this comparison leads to similar results on the outputs on which we focus (the maximum intensity of the epidemic, its duration and the time of the epidemic peak). However, when it comes to setting up epidemic control strategies, results rapidly diverge between the two approaches, and it appears that the full macroscopic model is not completely adapted to these questions. In this paper, we focus on some control strategies, which are quarantine, avoidance and risk culture, to explore the differences, advantages and disadvantages of the two models and discuss the importance of being hybrid when modeling and simulating epidemic spread at the level of a whole urban system

    Xylella fastidiosa in Olive in Apulia: Where We Stand

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    A dramatic outbreak of Xylella fastidiosa decimating olive was discovered in 2013 in Apulia, Southern Italy. This pathogen is a quarantine bacterium in the European Union (EU) and created unprecedented turmoil for the local economy and posed critical challenges for its management. With the new emerging threat to susceptible crops in the EU, efforts were devoted to gain basic knowledge on the pathogen biology, host, and environmental interactions (e.g., bacterial strain(s) and pathogenicity, hosts, vector(s), and fundamental drivers of its epidemics) in order to find means to control or mitigate the impacts of the infections. Field surveys, greenhouse tests, and laboratory analyses proved that a single bacterial introduction occurred in the area, with a single genotype, belonging to the subspecies pauca, associated with the epidemic. Infections caused by isolates of this genotype turned to be extremely aggressive on the local olive cultivars, causing a new disease termed olive quick decline syndrome. Due to the initial extension of the foci and the rapid spread of the infections, eradication measures (i.e., pathogen elimination from the area) were soon replaced by containment measures including intense border surveys of the contaminated area, removal of infected trees, and mandatory vector control. However, implementation of containment measures encountered serious difficulties, including public reluctance to accept control measures, poor stakeholder cooperation, misinformation from some media outlets, and lack of robust responses by some governmental authorities. This scenario delayed and limited containment efforts and allowed the bacterium to continue its rapid dissemination over more areas in the region, as shown by the continuous expansion of the official borders of the infected area. At the research level, the European Commission and regional authorities are now supporting several programs aimed to find effective methods to mitigate and contain the impact of X. fastidiosa on olives, the predominant host affected in this epidemic. Preliminary evidence of the presence of resistance in some olive cultivars represents a promising approach currently under investigation for long-term management strategies. The present review describes the current status of the epidemic and major research achievements since 2013

    Landscape Epidemiology and Control of Pathogens with Cryptic and Long-Distance Dispersal: Sudden Oak Death in Northern Californian Forests

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    Exotic pathogens and pests threaten ecosystem service, biodiversity, and crop security globally. If an invasive agent can disperse asymptomatically over long distances, multiple spatial and temporal scales interplay, making identification of effective strategies to regulate, monitor, and control disease extremely difficult. The management of outbreaks is also challenged by limited data on the actual area infested and the dynamics of spatial spread, due to financial, technological, or social constraints. We examine principles of landscape epidemiology important in designing policy to prevent or slow invasion by such organisms, and use Phytophthora ramorum, the cause of sudden oak death, to illustrate how shortfalls in their understanding can render management applications inappropriate. This pathogen has invaded forests in coastal California, USA, and an isolated but fast-growing epidemic focus in northern California (Humboldt County) has the potential for extensive spread. The risk of spread is enhanced by the pathogen's generalist nature and survival. Additionally, the extent of cryptic infection is unknown due to limited surveying resources and access to private land. Here, we use an epidemiological model for transmission in heterogeneous landscapes and Bayesian Markov-chain-Monte-Carlo inference to estimate dispersal and life-cycle parameters of P. ramorum and forecast the distribution of infection and speed of the epidemic front in Humboldt County. We assess the viability of management options for containing the pathogen's northern spread and local impacts. Implementing a stand-alone host-free “barrier” had limited efficacy due to long-distance dispersal, but combining curative with preventive treatments ahead of the front reduced local damage and contained spread. While the large size of this focus makes effective control expensive, early synchronous treatment in newly-identified disease foci should be more cost-effective. We show how the successful management of forest ecosystems depends on estimating the spatial scales of invasion and treatment of pathogens and pests with cryptic long-distance dispersal

    Integrating the landscape epidemiology and genetics of RNA viruses: rabies in domestic dogs as a model

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    Landscape epidemiology and landscape genetics combine advances in molecular techniques, spatial analyses and epidemiological models to generate a more real-world understanding of infectious disease dynamics and provide powerful new tools for the study of RNA viruses. Using dog rabies as a model we have identified how key questions regarding viral spread and persistence can be addressed using a combination of these techniques. In contrast to wildlife rabies, investigations into the landscape epidemiology of domestic dog rabies requires more detailed assessment of the role of humans in disease spread, including the incorporation of anthropogenic landscape features, human movements and socio-cultural factors into spatial models. In particular, identifying and quantifying the influence of anthropogenic features on pathogen spread and measuring the permeability of dispersal barriers are important considerations for planning control strategies, and may differ according to cultural, social and geographical variation across countries or continents. Challenges for dog rabies research include the development of metapopulation models and transmission networks using genetic information to uncover potential source/sink dynamics and identify the main routes of viral dissemination. Information generated from a landscape genetics approach will facilitate spatially strategic control programmes that accommodate for heterogeneities in the landscape and therefore utilise resources in the most cost-effective way. This can include the efficient placement of vaccine barriers, surveillance points and adaptive management for large-scale control programmes

    Offense and defense in landscape-level invasion control

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    Biological invasions are multi-stage processes comprising chance demographic events, species interactions, and dispersal. Despite this complexity, simple models can increase understanding of the invasion process. We model the spread of aquatic invasive species through a network of lakes to evaluate the effectiveness of two intervention strategies. The first, which we call offense, contains the invader at sources; the second, which we call defense, protects uninvaded destinations. Deterministic models reveal the effects of these intervention strategies on spread rates. Practical applications involve finite collections of uninvaded lakes, however, and we therefore also present a stochastic model to describe how these strategies affect expected times to important invasion milestones. When the goal is to reduce overall spread rates, both approaches agree that offense is better early in invasions, but that defense is better after 1/2 the lakes are invaded. When the goal is to protect areas of high conservation value, however, defensive site protection always provides lower per site introduction rates. Although we focus on lakes, our results are quite general, and could be applied to any discrete habitat patches including, for example, fragmented terrestrial habitats

    Pest Risk Assessment for Dutch elm disease

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    Dutch elm disease (DED) is a fungal disease that causes high mortality of elms. DED and its vector beetles are widely present in most of the countries in the Northern Hemisphere, but they are not known to be present in Finland. DED is a major risk to plant health in Finland. DED and its vectors are moderately likely to enter Finland by natural spread aided by hitchhiking, because they are present in areas close to Finland. Entry via other pathways is much less likely, mainly due to the low volume of trade of untreated wood and plants for planting. DED and its vectors could likely establish in the southern parts of the country, since they currently occur in similar climatic conditions in other countries. DED could cause massive environmental damage as natural elm groves are critically endangered habitats in Finland. The economic consequences to the owners of mature elms could also be significant. Eradication or containment of DED could be possible if strict measures were taken as the patchy distribution of elms would limit the spread of the disease. The most important source of uncertainty in this assessment is the lack of information regarding the amount of elm in fuel wood, wood waste and wood chips imported to Finland

    An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices

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    The integration of empirical data in computational frameworks to model the spread of infectious diseases poses challenges that are becoming pressing with the increasing availability of high-resolution information on human mobility and contacts. This deluge of data has the potential to revolutionize the computational efforts aimed at simulating scenarios and designing containment strategies. However, the integration of detailed data sources yields models that are less transparent and general. Hence, given a specific disease model, it is crucial to assess which representations of the raw data strike the best balance between simplicity and detail. We consider high-resolution data on the face-to-face interactions of individuals in a hospital ward, obtained by using wearable proximity sensors. We simulate the spread of a disease in this community by using an SEIR model on top of different mathematical representations of the contact patterns. We show that a contact matrix that only contains average contact durations fails to reproduce the size of the epidemic obtained with the high-resolution contact data and also to identify the most at-risk classes. We introduce a contact matrix of probability distributions that takes into account the heterogeneity of contact durations between (and within) classes of individuals, and we show that this representation yields a good approximation of the epidemic spreading properties obtained by using the high-resolution data. Our results mark a step towards the definition of synopses of high-resolution dynamic contact networks, providing a compact representation of contact patterns that can correctly inform computational models designed to discover risk groups and evaluate containment policies. We show that this novel kind of representation can preserve in simulation quantitative features of the epidemics that are crucial for their study and management

    Characterising Livestock System Zoonoses Hotspots

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    A systematic review of the published literature was undertaken, to explore the ability of different types of model to help identify the relative importance of different drivers leading to the development of zoonoses hotspots. We estimated that out of 373 papers we included in our review, 108 papers touched upon the objective of 'Assessment of interventions and intervention policies', 75 addressed the objective of 'Analysis of economic aspects of disease outbreaks and interventions', 67 the objective of 'Prediction of future outbreaks', but only 37 broadly addressed the objective of 'Sensitivity analysis to identify criteria leading to enhanced risk'. Most models of zoonotic diseases are currently capturing outbreaks over relatively short time and largely ignoring socio-economic drivers leading to pathogen emergence, spill-over and spread. In order to study long-term changes we need to understand how socio-economic and climatic changes affect structure of livestock production and how these in turn affect disease emergence and spread. Models capable of describing this processes do not appear to exist, although some progress has been made in linking social and economical aspects of livestock production and in linking economics to disease dynamics. Henceforth we conclude that a new modelling framework is required that expands and formalises the 'one world, one health' strategy, enabling its deployment in the re-thinking of prevention and control strategies. Although modelling can only provide means to identify risks associated with socio-economic changes, it can never be a substitute for data collection. Finally, we note that uncertainty analysis and uncertainty communication form a key element of modelling process and yet are rarely addressed
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