• Contact Us
    • Send Feedback
    • Login
    View Item 
    •   Home
    • Journal and Journal Articles
    • Journal and Journal Articles
    • View Item
    •   Home
    • Journal and Journal Articles
    • Journal and Journal Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    Whole Repository
    CollectionsIssue DateRegionCountryHubAffiliationAuthorsTitlesSubject
    This Sub-collection
    Issue DateRegionCountryHubAffiliationAuthorsTitlesSubject

    My Account

    Login

    Welcome to the International Institute of Tropical Agriculture Research Repository

    What would you like to view today?

    Trends in theoretical plant epidemiology

    Thumbnail
    Date
    2006
    Author
    Scherm, H.
    Ngugi, H.
    Ojiambo, P.
    Type
    Journal Article
    Metadata
    Show full item record
    Abstract/Description
    We review trends and advances in three specific areas of theoretical plant epidemiology: models of temporal and spatial dynamics of disease, the synergism of epidemiology and population genetics, and progress in statistical epidemiology. Recent analytical modelling of disease dynamics has focused on SIR (susceptible–infected–removed) models modified to include spatial structure, stochasticity, and multiple management-related parameters. Such models are now applied routinely to derive threshold criteria for pathogen invasion or persistence based on pathogen demographics (e.g., Allee effect or fitness of fungicide-resistant strains) and/or host spatial structure (e.g., host density or patch size and arrangement). Traditionally focused on the field level, the scale of analytical models has broadened to range from individual plants to landscapes and continents; however, epidemiological models for interactions at the cellular level, e.g., during the process of virus infection, are still rare. There is considerable interest in the concept of scaling, i.e., to what degree and how data and models from one scale can be transferred to another (smaller or larger) scale. Despite assertions to the contrary, the linkages between epidemiology and population genetics are alive and well as exemplified by recent efforts to integrate epidemiological parameters into population genetics models (and vice versa) and by numerous integrated studies with an applied focus (e.g., to quantify sources and types of primary and secondary inoculum). Statistical plant epidemiology continues to rely heavily on the medical and ecological fields for inspiration and conceptual advances, as illustrated by the recent surge in papers utilizing ROC (receiver operating characteristic), Bayesian, or survival analysis. Among these, Bayesian analysis should prove especially fruitful given the reliance on uncertain and subjective information for practical disease management. However, apart from merely adopting statistical tools from other disciplines, plant epidemiologists should be more proactive in exploring potential applications of their concepts and procedures in rapidly expanding disciplines such as statistical genetics or bioinformatics. Although providing the scientific basis for disease management will always be the raison d'être for plant epidemiology, a broader perspective will help the discipline to remain relevant as more resources are being devoted to genomic and ecosystem-level science. Keywords
    https://doi.org/10.1007/s10658-005-3682-6
    Multi standard citation
    Permanent link to this item
    https://hdl.handle.net/20.500.12478/5431
    Digital Object Identifier (DOI)
    https://doi.org/10.1007/s10658-005-3682-6
    IITA Subjects
    Research Method; Policies And Institutions; Capacity Development; Knowledge Management
    Agrovoc Terms
    Analysis; Mathematical Models; Population Genetics; Structures; Epidemiology
    Regions
    Acp; Africa; North America; West Africa
    Countries
    United States; Nigeria
    Collections
    • Journal and Journal Articles4463
    copyright © 2019  IITASpace. All rights reserved.
    IITA | Open Access Repository