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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 #338323
Title: A Method of Determining Where to Target Surveillance Efforts in Heterogeneous Epidemiological Systems

Author
item MASTIN, ALEXANDER - University Of Salford
item VAN DEN BOSCH, FRANK - Rothamsted Research
item Gottwald, Timothy
item CHAVEZ, VASTHI - Rothamsted Research
item PARNELL, STEPHEN - University Of Salford

Submitted to: PLoS Computational Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/2/2017
Publication Date: 8/28/2017
Citation: Mastin, A., Van Den Bosch, F., Gottwald, T.R., Chavez, V.A., Parnell, S.R. 2017. A Method of Determining Where to Target Surveillance Efforts in Heterogeneous Epidemiological Systems. PLoS Computational Biology. 13(8):1005712. https://doi.org/10.1371/journal.pcbi.1005712.
DOI: https://doi.org/10.1371/journal.pcbi.1005712

Interpretive Summary: The spread of plant animal and human pathogens into new environments poses a considerable threat to health, ecosystems, and agricultural productivity worldwide. Early detection through effective surveillance (surveys for diseases) is a key strategy to reduce the risk of their establishment. Cost of surveillance and statistically proper deployment of surveillance activities and highly important for maximum effectiveness and early detection. What is often underutilized is the biological and epidemiological characteristics of the disease, that can reveal much about how and where to sample effectively during surveys. When disease are found, there are almost never uniformly distributed over fields or within animal/human populations. Rather, they are usual more prevalent in some areas and less in others. This lack of uniformity is called heterogeneity. This is very often the case when diseases are vectored (carried from one host to another) by insects. A common example is malaria which is carried and transmitted from one person to another by mosquitoes. The spread of vectored diseases is often very nonuniform, i.e., heterogeneous, again meaning the disease will occur in a patchy distribution within a field or population. Therefore, an important question when planning for a survey is where to place sampling resources in order to detect the pathogen as early as possible within the limits of the number of samples that is practical to collect. To address this question we have developed a statistical function which describes the probability of finding initial infections within both hosts and vectors. We use this function to then plan how to distribute our survey samples most effectively. We can take into account the cost of sampling, how many samples we can take, and then where to place/take the samples. In the paper we demonstrate this method using two vector-borne citrus pathogens as examples. The deliverable from this study is a method that can be used by scientist/practitioners, growers, and regulatory agencies for early disease detection.

Technical Abstract: The spread of pathogens into new environments poses a considerable threat to health, ecosystems, and agricultural productivity worldwide. Early detection through effective surveillance is a key strategy to reduce the risk of their establishment. Whilst it is well established that statistical and economic considerations are of vital importance when planning surveillance efforts, it is also important to consider epidemiological characteristics of the pathogen in question - including heterogeneities within the epidemiological system itself. One of the most pronounced realizations of this heterogeneity is seen in the case of vector-borne pathogens, which spread between 'hosts' and 'vectors' - with each group possessing distinct epidemiological characteristics. As a result, an important question when planning surveillance for emerging vector-borne pathogens is where to place sampling resources in order to detect the pathogen as early as possible. We answer this question by developing a statistical function which describes the probability distribution of prevalences of infection at first detection in both hosts and vectors. We also show how this method can be adapted in order to maximize the probability of early detection of an emerging pathogen within imposed sample size and/or cost constraints, and demonstrate its application using two simple models of vector-borne citrus pathogens.