Simulating dynamic insecticide selection pressures for resistance management in mosquitoes assuming polygenic resistance

Neil Philip Hobbs and Ian Hastings

PLOS Computational Biology, 2025, vol. 21, issue 4, 1-37

Abstract: Insecticide resistance management (IRM) is critical to maintain the operational effectiveness of insecticides used in public health vector control. Evaluating IRM strategies rests primarily on computational models. Most models assume monogenic resistance, but polygenic resistance may be a more appropriate assumption. Conventionally, polygenic models assume selection differentials are constant over successive generations. We present a dynamic method for calculating the selection differentials accounting for the level of resistance and insecticide efficacy. This allows the inclusion of key parameters namely insecticide dosing, insecticide decay and cross resistance, increasing biological and operational realism. Two methods for calculating the insecticide selection differential were compared: truncation (only the most resistant individuals in the population survive) and probabilistic (individual survival depends on their level of resistance). The probabilistic calculation is extendable to multiple gonotrophic cycles, whereby mosquitoes may encounter different insecticides over their life span. A range of IRM strategies of direct policy relevance can be simulated, including the implication of reduced dose mixtures. We describe in detail the calculation and calibration of these models. We demonstrate the ability of the models to simulate a variety of IRM strategies and implications of including these features of the models. In simple IRM strategy evaluations, the truncation and probabilistic models give comparable results to each other and against published polygenic and monogenic models. Analysis of model simulations indicates there is often little difference between sequences or rotations of insecticides. Full-dose mixtures remain the best evaluated IRM strategy. Consistency between models increases confidence in their predictions especially when demonstrating model assumptions do not significantly impact key operational decisions. Using the multiple-gonotrophic cycle model we calculate the age distributions of mosquitoes which provides a framework to link resistance management with disease transmission. Future applications will investigate more scenario-specific evaluations of IRM strategies to inform public health policy.Author summary: Insecticide resistance poses a threat to the continued control of vector-borne diseases. The use of insecticide resistance management strategies is recommended to slow the spread of insecticide resistance and limit its impact on disease transmission. However, what insecticide resistance management strategies work in specific circumstances is under debate. Simulation modelling is frequently used to evaluate insecticide resistance management strategies, as evaluating strategies in the field is inhibited by the need for long trial durations, replicated across settings. Previous models have generally assumed resistance is encoded by a single gene (monogenic), and evaluated a limited set of strategies. Here we consider insecticide resistance when encoded by many genes (polygenic) and use a dynamic quantitative genetics approach. We present a novel mathematical methodology for the simulation of insecticide resistance management strategies, which includes the key parameters of insecticide dosing, insecticide decay and cross resistance. These are generally absent in previous models, despite being considered important for strategy evaluation. Extending our computational model to allow for multiple blood-feeding cycles (and hence multiple rounds of selection) allows for more complex strategies to be evaluated which include household level deployments of different insecticides. In simple scenarios evaluating rotations, sequences and mixtures we generate further support for the results from other recent modelling studies. We further demonstrate that this computational model will have value for evaluating more specific insecticide resistance management scenarios, which can help inform and guide insecticide deployments.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1012944

DOI: 10.1371/journal.pcbi.1012944

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