Sampling for disease absence-deriving informed monitoring from epidemic traits.


Rothamsted Research, Department of Biointeraction and Crop Protection, Harpenden, Herts. AL5 2JQ, UK. Electronic address: [Email]


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.


Bayes’ Rule,Disease absence,Early detection,Risk assessment,Sampling theory,