Seeking a second opinion: uncertainty in disease ecology

• Autor: McClintock, Brett T ; Nichols, James D ; Bailey, Larissa L ; MacKenzie, Darryl I ; Kendall, William.L ; Franklin, Alan B
• Assuntos: Amphibians - microbiology ; Amphibians - physiology ; Animal and plant ecology ; Animal diseases ; Animal Diseases - epidemiology ; Animal Diseases - microbiology ; Animal Diseases - virology ; Animal Migration ; Animal, plant and microbial ecology ; Animals ; Animals, Domestic - microbiology ; Animals, Domestic - physiology ; Animals, Domestic - virology ; Animals, Wild - microbiology ; Animals, Wild - physiology ; Animals, Wild - virology ; Anseriformes - virology ; Avian influenza virus ; Biological and medical sciences ; disease detection ; disease diagnosis ; disease models ; disease prevalence ; Ecology ; Ecology - statistics & numerical data ; Epidemiology ; Fundamental and applied biological sciences. Psychology ; Fungi - pathogenicity ; General aspects ; Host and pathogen dynamics ; Humans ; imperfect detection ; incidence ; Influenza A virus - pathogenicity ; Influenza in Birds - epidemiology ; Influenza in Birds - virology ; mathematical models ; misclassification ; Models, Statistical ; Mycoses - epidemiology ; Mycoses - microbiology ; Mycoses - veterinary ; observation error ; occupancy ; presence-absence ; prevalence ; probabilistic models ; Research methodology ; spatial epidemiology ; spatial variation ; species occurrence ; temporal variation ; Uncertainty ; wildlife diseases
• É parte de: Ecology letters, 2010-06, Vol.13 (6), p.659-674
• Notas: http://hdl.handle.net/10113/41915
  http://dx.doi.org/10.1111/j.1461-0248.2010.01472.x
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• Descrição: Ecology Letters (2010) 13: 659-674 Analytical methods accounting for imperfect detection are often used to facilitate reliable inference in population and community ecology. We contend that similar approaches are needed in disease ecology because these complicated systems are inherently difficult to observe without error. For example, wildlife disease studies often designate individuals, populations, or spatial units to states (e.g., susceptible, infected, post-infected), but the uncertainty associated with these state assignments remains largely ignored or unaccounted for. We demonstrate how recent developments incorporating observation error through repeated sampling extend quite naturally to hierarchical spatial models of disease effects, prevalence, and dynamics in natural systems. A highly pathogenic strain of avian influenza virus in migratory waterfowl and a pathogenic fungus recently implicated in the global loss of amphibian biodiversity are used as motivating examples. Both show that relatively simple modifications to study designs can greatly improve our understanding of complex spatio-temporal disease dynamics by rigorously accounting for uncertainty at each level of the hierarchy.
  
• Editor: Oxford, UK: Oxford, UK : Blackwell Publishing Ltd
  
• Idioma: Inglês