46 research outputs found

    Evidence-based controls for epidemics using spatio-temporal stochastic models in a Bayesian framework.

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    The control of highly infectious diseases of agricultural and plantation crops and livestock represents a key challenge in epidemiological and ecological modelling, with implemented control strategies often being controversial. Mathematical models, including the spatio-temporal stochastic models considered here, are playing an increasing role in the design of control as agencies seek to strengthen the evidence on which selected strategies are based. Here, we investigate a general approach to informing the choice of control strategies using spatio-temporal models within the Bayesian framework. We illustrate the approach for the case of strategies based on pre-emptive removal of individual hosts. For an exemplar model, using simulated data and historic data on an epidemic of Asiatic citrus canker in Florida, we assess a range of measures for prioritizing individuals for removal that take account of observations of an emerging epidemic. These measures are based on the potential infection hazard a host poses to susceptible individuals (hazard), the likelihood of infection of a host (risk) and a measure that combines both the hazard and risk (threat). We find that the threat measure typically leads to the most effective control strategies particularly for clustered epidemics when resources are scarce. The extension of the methods to a range of other settings is discussed. A key feature of the approach is the use of functional-model representations of the epidemic model to couple epidemic trajectories under different control strategies. This induces strong positive correlations between the epidemic outcomes under the respective controls, serving to reduce both the variance of the difference in outcomes and, consequently, the need for extensive simulation.Hola Adrakey was supported during the course of this research by a James Watt Postgraduate Research Scholarship from Heriot–Watt University

    Risk-based management of invading plant disease

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    - Effective control of plant disease remains a key challenge. Eradication attempts often involve removal of host plants within a certain radius of detection, targeting asymptomatic infection. Here we develop and test potentially more effective, epidemiologically motivated, control strategies, using a mathematical model previously fitted to the spread of citrus canker in Florida. - We test risk-based control, which preferentially removes hosts expected to cause a high number of infections in the remaining host population. Removals then depend on past patterns of pathogen spread and host removal, which might be nontransparent to affected stakeholders. This motivates a variable radius strategy, which approximates risk-based control via removal radii that vary by location, but which are fixed in advance of any epidemic. - Risk-based control outperforms variable radius control, which in turn outperforms constant radius removal. This result is robust to changes in disease spread parameters and initial patterns of susceptible host plants. However, efficiency degrades if epidemiological parameters are incorrectly characterised. - Risk-based control including additional epidemiology can be used to improve disease management, but it requires good prior knowledge for optimal performance. This focuses attention on gaining maximal information from past epidemics, on understanding model transferability between locations and on adaptive management strategies that change over time.Part of this work was funded by the USDA-APHIS Farm Bill; C.A.G. acknowledges support from USDA-APHIS

    Sequencing of diverse mandarin, pummelo and orange genomes reveals complex history of admixture during citrus domestication

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    Cultivated citrus are selections from, or hybrids of, wild progenitor species whose identities and contributions to citrus domestication remain controversial. Here we sequence and compare citrus genomes-a high-quality reference haploid clementine genome and mandarin, pummelo, sweet-orange and sour-orange genomes-and show that cultivated types derive from two progenitor species. Although cultivated pummelos represent selections from one progenitor species, Citrus maxima, cultivated mandarins are introgressions of C. maxima into the ancestral mandarin species Citrus reticulata. The most widely cultivated citrus, sweet orange, is the offspring of previously admixed individuals, but sour orange is an F1 hybrid of pure C. maxima and C. reticulata parents, thus implying that wild mandarins were part of the early breeding germplasm. A Chinese wild 'mandarin' diverges substantially from C. reticulata, thus suggesting the possibility of other unrecognized wild citrus species. Understanding citrus phylogeny through genome analysis clarifies taxonomic relationships and facilitates sequence-directed genetic improvement. (Résumé d'auteur
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