A - Papers appearing in refereed journals
Mastin, A. J., Van Den Bosch, F., Gottwald, T. R., Alonso Chavez, V. and Parnell, S. R. 2017. A method of determining where to target surveillance efforts in heterogeneous epidemiological systems. PLOS Computational Biology. 13 (8), p. e1005712.
|Authors||Mastin, A. J., Van Den Bosch, F., Gottwald, T. R., Alonso Chavez, V. and Parnell, S. R.|
The spread of pathogens into new environments poses a considerable threat to human, animal, and plant health, and by extension, human and animal wellbeing, ecosystem function, 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 realisations 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 distributions of the prevalences of infection at first detection in both hosts and vectors. We also show how this method can be adapted in order to maximise 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. Under the assumption of a linear cost function, we find that sampling costs are generally minimised when either hosts or vectors, but not both, are sampled.
|Year of Publication||2017|
|Journal||PLOS Computational Biology|
|Journal citation||13 (8), p. e1005712|
|Digital Object Identifier (DOI)||doi:10.1371/journal.pcbi.1005712|
|Open access||Published as ‘gold’ (paid) open access|
|Online||28 Aug 2017|
|Copyright license||CC BY|
|Publisher||Public Library of Science (PLOS)|
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