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© Research
Publication : PLoS pathogens

Epidemiological significance of dengue virus genetic variation in mosquito infection dynamics

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in PLoS pathogens - 13 Jul 2018

Fontaine A, Lequime S, Moltini-Conclois I, Jiolle D, Leparc-Goffart I, Reiner RC, Lambrechts L

Link to Pubmed [PMID] – 30005085

PLoS Pathog. 2018 Jul;14(7):e1007187

The kinetics of arthropod-borne virus (arbovirus) transmission by their vectors have long been recognized as a powerful determinant of arbovirus epidemiology. The time interval between virus acquisition and transmission by the vector, termed extrinsic incubation period (EIP), combines with vector mortality rate and vector competence to determine the proportion of infected vectors that eventually become infectious. However, the dynamic nature of this process, and the amount of natural variation in transmission kinetics among arbovirus strains, are poorly documented empirically and are rarely considered in epidemiological models. Here, we combine newly generated empirical measurements in vivo and outbreak simulations in silico to assess the epidemiological significance of genetic variation in dengue virus (DENV) transmission kinetics by Aedes aegypti mosquitoes. We found significant variation in the dynamics of systemic mosquito infection, a proxy for EIP, among eight field-derived DENV isolates representing the worldwide diversity of recently circulating type 1 strains. Using a stochastic agent-based model to compute time-dependent individual transmission probabilities, we predict that the observed variation in systemic mosquito infection kinetics may drive significant differences in the probability of dengue outbreak and the number of human infections. Our results demonstrate that infection dynamics in mosquitoes vary among wild-type DENV isolates and that this variation potentially affects the risk and magnitude of dengue outbreaks. Our quantitative assessment of DENV genetic variation in transmission kinetics contributes to improve our understanding of heterogeneities in arbovirus epidemiological dynamics.

https://www.ncbi.nlm.nih.gov/pubmed/30005085