Estimating epidemiological parameters from experiments in vector access to host plants, the method of matching gradients.
PLoS computational biology
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Donnelly, R., Sikazwe, G. W., & Gilligan, C. A. (2020). Estimating epidemiological parameters from experiments in vector access to host plants, the method of matching gradients.. PLoS computational biology, 16 (3)https://doi.org/10.1371/journal.pcbi.1007724
Estimation of pathogenic life-history values, for instance the duration a pathogen is retained in an insect vector (i.e., retention period) is of particular importance for understanding plant disease epidemiology. How can we extract values for these epidemiological parameters from conventional small-scale laboratory experiments in which transmission success is measured in relation to durations of vector access to host plants? We provide a solution to this problem by deriving formulae for the empirical curves that these experiments produce, called access period response curves (i.e., transmission success vs access period). We do this by writing simple equations for the fundamental life-cycle components of insect vectors in the laboratory. We then infer values of epidemiological parameters by matching the theoretical and empirical gradients of access period response curves. Using the example of Cassava brown streak virus (CBSV), which has emerged in sub-Saharan Africa and now threatens regional food security, we illustrate the method of matching gradients. We show how applying the method to published data produces a new understanding of CBSV through the inference of retention period, acquisition period and inoculation period parameters. We found that CBSV is retained for a far shorter duration in its insect vector (Bemisia tabaci whitefly) than had previously been assumed. Our results shed light on a number of critical factors that may be responsible for the transition of CBSV from sub- to super-threshold R0 in sub-Saharan Africa. The method is applicable to plant pathogens in general, to supply epidemiological parameter estimates that are crucial for practical management of epidemics and prediction of pandemic risk.
External DOI: https://doi.org/10.1371/journal.pcbi.1007724
This record's URL: https://www.repository.cam.ac.uk/handle/1810/304506
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/