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ARS Home » Southeast Area » Fort Pierce, Florida » U.S. Horticultural Research Laboratory » Subtropical Plant Pathology Research » Research » Publications at this Location » Publication #356163
Research Project: Mitigating High Consequence Domestic, Exotic, and Emerging Diseases of Fruits, Vegetables, and Ornamentals

Location: Subtropical Plant Pathology Research

Title: Translating surveillance data into incidence estimates

Author
item BOURHIS, YOANN - Rothamsted Research
item Gottwald, Timothy
item VAN DEN BOSCH, FRANK - Rothamsted Research

Submitted to: Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/8/2019
Publication Date: 5/20/2019
Citation: Bourhis, Y., Gottwald, T.R., Van Den Bosch, F. 2019. Translating surveillance data into incidence estimates. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences. 374:20180262. https://doi.org/10.1098/rstb.2018.0262.
DOI: https://doi.org/10.1098/rstb.2018.0262

Interpretive Summary: Monitoring a population for a disease requires the hosts to be sampled and tested for the pathogen. This results in sampling series from which to estimate the disease incidence, i.e. the proportion of hosts infected. Existing estimation methods assume that disease incidence is not changing between monitoring rounds, resulting in underestimation of the disease incidence. In this paper we develop an incidence estimation model accounting for epidemic growth with monitoring rounds sampling varying incidence. We also show how to accommodate the asymptomatic period characteristic to most diseases. For practical use, we produce an approximation of the model, which is subsequently shown accurate for relevant epidemic and sampling parameters. Both the approximation and the full model are applied to stochastic spatial simulations of epidemics. The results prove their consistency for a very wide range of situations.

Technical Abstract: Regulatory agencies, growers and research all often survey for plant diseases, often conducting multiple rounds of survey, sampling, and testing of the samples for the pathogen. After multiple rounds of survey, if we detect the pathogen, we have sufficient information to estimate the incidence of the pathogen, that is, the proportion of host plants infected in the area being surveyed. In this paper we show how prior statistic methods often tend to underestimate this disease incidence because they do not take into account the period of time when infections have occurred but are not visible or detectable by testing samples. The method we provide is consistent across a very wide range of diseases. This method will be of benefit for regulatory agencies and practitioners for decision making on disease control/mitigation practices.