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dc.contributor.authorBourhis, Y.
dc.contributor.authorGottwald, T.
dc.contributor.authorLopez-Ruiz, Fran
dc.contributor.authorPatarapuwadol, S.
dc.contributor.authorvan den Bosch, F.
dc.identifier.citationBourhis, Y. and Gottwald, T. and Lopez-Ruiz, F. and Patarapuwadol, S. and van den Bosch, F. 2019. Sampling for disease absence—deriving informed monitoring from epidemic traits. Journal of Theoretical Biology. 461: pp. 8-16.

Monitoring for disease requires subsets of the host population to be sampled and tested for the pathogen. If all the samples return healthy, what are the chances the disease was present but missed? In this paper, we developed a statistical approach to solve this problem considering the fundamental property of infectious diseases: their growing incidence in the host population. The model gives an estimate of the incidence probability density as a function of the sampling effort, and can be reversed to derive adequate monitoring patterns ensuring a given maximum incidence in the population. We then present an approximation of this model, providing a simple rule of thumb for practitioners. The approximation is shown to be accurate for a sample size larger than 20, and we demonstrate its use by applying it to three plant pathogens: citrus canker, bacterial blight and grey mould.

dc.publisherAcademic Press
dc.titleSampling for disease absence—deriving informed monitoring from epidemic traits
dc.typeJournal Article
dcterms.source.titleJournal of Theoretical Biology
curtin.departmentCentre for Crop and Disease Management (CCDM)
curtin.accessStatusFulltext not available

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