Role of vector phenotypic plasticity in disease transmission as illustrated by the spread of dengue virus by Aedes albopictus

Dominic P. Brass (), Christina A. Cobbold, Bethan V. Purse, David A. Ewing, Amanda Callaghan and Steven M. White
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Dominic P. Brass: Benson Lane
Christina A. Cobbold: University of Glasgow
Bethan V. Purse: Benson Lane
David A. Ewing: Biomathematics and Statistics Scotland
Amanda Callaghan: University of Reading
Steven M. White: Benson Lane

Nature Communications, 2024, vol. 15, issue 1, 1-22

Abstract: Abstract The incidence of vector-borne disease is on the rise globally, with burdens increasing in endemic countries and outbreaks occurring in new locations. Effective mitigation and intervention strategies require models that accurately predict both spatial and temporal changes in disease dynamics, but this remains challenging due to the complex and interactive relationships between environmental variation and the vector traits that govern the transmission of vector-borne diseases. Predictions of disease risk in the literature typically assume that vector traits vary instantaneously and independently of population density, and therefore do not capture the delayed response of these same traits to past biotic and abiotic environments. We argue here that to produce accurate predictions of disease risk it is necessary to account for environmentally driven and delayed instances of phenotypic plasticity. To show this, we develop a stage and phenotypically structured model for the invasive mosquito vector, Aedes albopictus, and dengue, the second most prevalent human vector-borne disease worldwide. We find that environmental variation drives a dynamic phenotypic structure in the mosquito population, which accurately predicts global patterns of mosquito trait-abundance dynamics. In turn, this interacts with disease transmission to capture historic dengue outbreaks. By comparing the model to a suite of simpler models, we reveal that it is the delayed phenotypic structure that is critical for accurate prediction. Consequently, the incorporation of vector trait relationships into transmission models is critical to improvement of early warning systems that inform mitigation and control strategies.

Date: 2024
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DOI: 10.1038/s41467-024-52144-5

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