Location: Subtropical Plant Pathology ResearchTitle: Sampling for disease absence - deriving informed monitoring from epidemic traits Author
|Bourhis, Yoann - Rothamsted Research|
|Lopez-ruiz, Fancisco - Curtin University|
|Patarapuwadol, Sujin - Kasetsart University|
|Van Den Bosch, Frank - Rothamsted Research|
Submitted to: Journal of Theoretical Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/17/2018
Publication Date: 1/19/2019
Citation: Bourhis, Y., Gottwald, T.R., Lopez-Ruiz, F.J., Patarapuwadol, S., Van Den Bosch, F. 2019. Sampling for disease absence - deriving informed monitoring from epidemic traits. Journal of Theoretical Biology. 461(2019):8-16. https://doi.org/10.1016/j.jtbi.2018.10.038.
DOI: https://doi.org/10.1016/j.jtbi.2018.10.038 Interpretive Summary: Regulatory agencies commodity growers and scientific researchers often need to survey for disease that affect crops to determine if a pathogen exists in an area and if it does, at what prevalence. This knowledge helps the practitioners and regulatory agencies to decide on the most appropriate control/mitigation measure to apply. However, when no pathogen is found after multiple rounds of survey, does that mean it is absence entirely or merely below the threshold of detection? We have developed a statistical method to address this question. The method uses information on the total number of plants in a field or area and the number of samples taken during survey. With this information we can estimate the potential incidence of the disease even though it was not detected. This is highly useful to regulatory agencies and growers who rely on survey for regulatory action and for disease control/management.
Technical Abstract: Monitoring for disease requires subsets of the host population to be sampled and tested for the disease. 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 sample size larger than 20, and we demonstrate its use applying it to three plant diseases: citrus canker, bacterial blight and grey mold.