Abstract
AbstractMalaria control uses insecticides to killAnophelesmosquitoes. Recent successes in malaria control are threatened by increasing levels of insecticide resistance (IR), requiring insecticide resistance management (IRM) strategies to mitigate this problem. Field trials of IRM strategies are usually prohibitively expensive with long timeframes, and mathematical modelling is often used to evaluate alternative options. Previous IRM models in the context of malaria control assumed IR to have a simple (monogenic) basis, whereas in natural populations, IR will often be a complex polygenic trait determined by multiple genetic variants.A quantitative genetics model was developed to model IR as a polygenic trait. The model allows insecticides to be deployed as sequences (continuous deployment until a defined withdrawal threshold, termed “insecticide lifespan”, as indicated by resistance diagnosis in bioassays), rotations (periodic switching of insecticides), or full-dose mixtures (two insecticides in one formulation). These IRM strategies were compared based on their “strategy lifespan” (capped at 500 generations). Partial rank correlation and generalised linear modelling was used to identify and quantify parameters driving the evolution of resistance. Random forest models were used to identify parameters offering predictive value for decision-making.Deploying single insecticides as sequences or rotations usually made little overall difference to their “strategy lifespan”, though rotations displayed lower mean and peak resistances. Deploying two insecticides in a full-dose mixture formulation was found to extend the “strategy lifespan” when compared to deploying each in sequence or rotation. This pattern was observed regardless of the level of cross resistance between the insecticides or the starting level of resistance. Statistical analysis highlighted the importance of insecticide coverage, cross resistance, heritability, and fitness costs for selecting an appropriate IRM strategy.Full-dose mixtures appear the most promising of the strategies evaluated, with the longest “strategy lifespans”. These conclusions broadly corroborate previous results from monogenic models.Author Summary:Insecticides impregnated into bed-nets or sprayed on walls are used to kill theAnophelesmosquitoes which transmit malaria. Unfortunately, the usage of insecticides has inevitably led to mosquitoes evolving resistance to the toxic effect of these insecticides. Insecticide resistance management strategies may be used to slow the rate of resistance evolution, however which strategies are effective, and when they are effective, is often unclear. Previous models evaluating insecticide resistance management strategies have assumed resistance is encoded by a single gene (is a monogenic trait). However, in natural populations resistance may be determined by multiple genes (is a polygenic trait). It is unclear whether such increased model complexity may change predictions We modelled resistance as a polygenic trait and found little difference in the benefit between rotating insecticides regularly versus deploying continuously until resistance reaches a critical threshold then switching. In contrast, mixtures combining two insecticides extended the projected lifespan of the insecticides, even when they share resistance mechanisms (cross resistance). Similar findings from previous monogenic models, strengthen support for the use of full-dose mixtures.
Publisher
Cold Spring Harbor Laboratory