Integrating data from across insecticide resistance bioassay types using a mechanistic model incorporating mosquito genetic variation and behaviour

Malaria claims approximately 500,000 lives each year, and insecticide-treated nets (ITNs), which kill mosquitoes that transmit the disease, remain the most effective intervention. However, resistance to pyrethroids, the primary insecticide class used in ITNs, has risen dramatically in Africa, making it difficult to assess the current public health impact of pyrethroid-ITNs. Past work has modelled the relation between pyrethroid susceptibility measured in discriminating-dose susceptibility bioassays and ITN effectiveness in experimental hut trials. Here, we introduce a new predictive approach that accounts for heterogeneity in insecticide resistance within wild mosquito populations, for example, due to genetic variability, by incorporating data from newly recommended intensity-dose susceptibility bioassays. We fit our mathematical model to a comprehensive data set that combines discriminating dose bioassays from all over Africa, intensity dose bioassays from Burkina Faso, and concurrent experimental hut trials. Our analysis estimates location- and insecticide-specific variation in resistance heterogeneity in Burkina Faso and quantifies differences in insecticide exposure in bioassays and experimental huts. By providing a mechanistic understanding of these experimental data, our approach could be integrated into malaria transmission models to account for the public health impact of insecticide resistance detected by surveillance programmes. Author summaryBednets treated with insecticides that kill mosquitoes have been responsible for major reductions in malaria burden over recent decades. However, the spread of insecticide resistance in mosquito populations threatens to slow or reverse these gains. It is therefore important to be able to gauge insecticide resistance in local mosquito populations and the remaining effectiveness of bednets. Important tools for quantifying insecticide resistance include susceptibility bioassays, which expose mosquitoes to specific insecticide doses and measure mortality, and experimental hut trials, which aim to mimic how mosquitoes interact with insecticides on bednets in the field. Here, we develop a mathematical model that incorporates mechanistic features of how mosquitoes interact with insecticides in both types of experiments. We show that this model can accurately predict mosquito mortality in experimental hut trials using data from intensity-dose susceptibility bioassays. These bioassays are more sensitive than traditional discriminating-dose assays while being less costly and logistically demanding than experimental hut trials. Our model provides a more granular understanding of insecticide resistance measurements and could be embedded into malaria transmission models to predict the public health impact of insecticide resistance measured by surveillance programmes.