Last Updated: 21/09/2026
Modelling geospatial spread of resistance against chemical control in disease vectors and pathogens, and projecting impact of pyrethroid and pyrrole resistance on malaria vector control
Objectives
This project aims to develop quantitative models to assess and manage insecticide resistance in malaria vectors, particularly focusing on the transition from pyrethroid to pyrrole-treated bed nets. By integrating various modeling techniques, the study seeks to predict the geospatial dynamics of resistance and its impact on malaria control, ultimately enhancing strategies for sustainable vector management and reducing future malaria deaths.
Swiss Tropical and Public Health Institute (Swiss TPH), Switzerland
Bed nets laden with pyrethroid insecticides are the most important malaria intervention in Africa since the year 2000 but have led to wide-spread pyrethroid resistance in mosquito populations. Newly adopted pyrethroid-pyrrole treated bed nets restore mosquito susceptibility, yet the evolution of resistance to pyrrole insecticides is a simple question of time. It is thus urgent to develop quantitative models now to inform corresponding resistance monitoring efforts and future net deployment strategies in order to prepare for the first mass occurrence of pyrrole resistance and hence prevent a new surge of hundreds of thousands malaria deaths. Weighing protecting humans now vs preventing future insecticide resistance requires careful prediction of the corresponding epidemiological endpoints. The proposed research plan aims to provide a calibrated, spatial modelling framework to assess resistance management strategies for malaria vector control with respect to epidemiological endpoints. To address separate objectives building up towards this aim, a range of empirical and mechanistic models will be developed and calibrated with different kinds of data, using techniques from spatial statistics, neural-networks, Bayesian non-parametrics, Gaussian Process regression, biostatistics and numerical simulations. Taken separately, these models will (A) improve the understanding of movement and genetic variability of Anopheles mosquitoes in Africa, (B) forecast geospatial dynamics of resistance against chemical control and (C) quantify the impact of insecticide resistance on the efficacy of pyrethroid-pyrrole treated bed nets against African malaria vectors. Integrating these modelling steps and coupling with an existing simulation platform of malaria epidemiology will provide a proof of concept of spatial insecticide resistance management with respect to epidemiological endpoints. The current roll-out of pyrethroid-pyrrole treated bed nets and the change of resistance surveillance methods towards genetic marker testing will fundamentally change insecticide resistance management. The methods to be developed under this proposal will be first in utilising genetic marker data to project epidemiological outcomes and an important step towards more sustainable malaria vector control.
Jan 2026 — Dec 2029
$1.07M
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