Fine-scale estimation of key life-history parameters of malaria vectors: implications for next-generation vector control technologies

Aaron L Morris,Neil Ferguson,Azra Ghani

doi:10.1186/s13071-021-04789-0

Abstract

BackgroundMosquito control has the potential to significantly reduce malaria burden on a region, but to influence public health policy must also show cost-effectiveness. Gaps in our knowledge of mosquito population dynamics mean that mathematical modelling of vector control interventions have typically made simplifying assumptions about key aspects of mosquito ecology. Often, these assumptions can distort the predicted efficacy of vector control, particularly next-generation tools such as gene drive, which are highly sensitive to local mosquito dynamics.MethodsWe developed a discrete-time stochastic mathematical model of mosquito population dynamics to explore the fine-scale behaviour of egg-laying and larval density dependence on parameter estimation. The model was fitted to longitudinal mosquito population count data using particle Markov chain Monte Carlo methods.ResultsBy modelling fine-scale behaviour of egg-laying under varying density dependence scenarios we refine our life history parameter estimates, and in particular we see how model assumptions affect population growth rate (Rm), a crucial determinate of vector control efficacy.ConclusionsSubsequent application of these new parameter estimates to gene drive models show how the understanding and implementation of fine-scale processes, when deriving parameter estimates, may have a profound influence on successful vector control. The consequences of this may be of crucial interest when devising future public health policy.Graphical abstract

Highlights

Mosquito control has the potential to significantly reduce malaria burden on a region, but to influence public health policy must show cost-effectiveness
Gaps in our knowledge lead to an oversimplification of fine-scale mosquito dynamics, which can have a significant impact on our predictions of vector control efficacy
Effect on gene drive To illustrate the effect on vector control, we looked at a test case of the impact on gene drive from changes in both density dependence and the addition of clumped egg-laying, by reconstructing the gene drive model formulated by Deredec et al [20]

Summary

Introduction

Mosquito control has the potential to significantly reduce malaria burden on a region, but to influence public health policy must show cost-effectiveness. Gaps in our knowledge of mosquito population dynamics mean that mathematical modelling of vector control interventions have typically made simplifying assumptions about key aspects of mosquito ecology. Often, these assumptions can distort the predicted efficacy of vector control, next-generation tools such as gene drive, which are highly sensitive to local mosquito dynamics. Gaps in our knowledge lead to an oversimplification of fine-scale mosquito dynamics, which can have a significant impact on our predictions of vector control efficacy. In Africa, where 90% of malaria cases occur [1], Anopheles gambiae sensu stricto and Anopheles funestus are the dominant vectors in the majority of regions [4, 5], with Anopheles arabiensis and Anopheles coluzzii

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