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Entomological surveillance is an important component of mosquito-borne disease control. Mosquito abundance, infection prevalence and the entomological inoculation rate are the most widely reported entomological metrics, although these data are notoriously noisy and difficult to interpret. For many infections, only older mosquitoes are infectious, which is why, in part, vector control tools that reduce mosquito life expectancy have been so successful. The age structure of wild mosquitoes has been proposed as a metric to assess the effectiveness of interventions that kill adult mosquitoes, and age grading tools are becoming increasingly advanced. Mosquito populations show seasonal dynamics with temporal fluctuations. How seasonal changes in adult mosquito emergence and vector control could affect the mosquito age distribution or other important metrics is unclear. We develop stochastic mathematical models of mosquito population dynamics to show how variability in mosquito emergence causes substantial heterogeneity in the mosquito age distribution, with low frequency, positively autocorrelated changes in emergence being the most important driver of this variability. Fitting a population model to mosquito abundance data collected in experimental hut trials indicates these dynamics are likely to exist in wild Anopheles gambiae populations. Incorporating age structuring into an established compartmental model of mosquito dynamics and vector control, indicates that the use of mosquito age as a metric to assess the efficacy of vector-control tools will require an understanding of underlying variability in mosquito ages, with the mean age and other entomological metrics affected by short-term and seasonal fluctuations in mosquito emergence.

Original publication

DOI

10.1371/journal.pcbi.1013035

Type

Journal article

Journal

PLoS computational biology

Publication Date

08/2025

Volume

21

Addresses

School of Public Health, Imperial College London, London, United Kingdom.

Keywords

Animals, Humans, Culicidae, Anopheles, Computational Biology, Seasons, Population Dynamics, Mosquito Control, Models, Biological, Mosquito Vectors