Hobbs Neil Philip, Hastings Ian
Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, United Kingdom.
Department of Tropical Disease Biology, Liverpool School of Tropical Medicine, Liverpool, United Kingdom.
PLoS Comput Biol. 2025 Apr 28;21(4):e1012944. doi: 10.1371/journal.pcbi.1012944. eCollection 2025 Apr.
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.
杀虫剂抗性管理（IRM）对于维持公共卫生病媒控制中使用的杀虫剂的操作有效性至关重要。评估IRM策略主要依赖于计算模型。大多数模型假设为单基因抗性，但多基因抗性可能是更合适的假设。传统上，多基因模型假设连续几代的选择差异是恒定的。我们提出了一种动态方法来计算考虑抗性水平和杀虫剂效力的选择差异。这允许纳入关键参数，即杀虫剂剂量、杀虫剂衰减和交叉抗性，从而提高生物学和操作的真实性。比较了两种计算杀虫剂选择差异的方法：截断法（种群中只有抗性最强的个体存活）和概率法（个体存活取决于其抗性水平）。概率计算可扩展到多个生殖营养周期，即蚊子在其生命周期内可能接触不同的杀虫剂。可以模拟一系列与政策直接相关的IRM策略，包括降低剂量混合物的影响。我们详细描述了这些模型的计算和校准。我们展示了这些模型模拟各种IRM策略的能力以及纳入这些模型特征的影响。在简单的IRM策略评估中，截断模型和概率模型相互之间以及与已发表的多基因和单基因模型的结果相当。对模型模拟的分析表明，杀虫剂的序列或轮换之间通常差异不大。全剂量混合物仍然是评估最好的IRM策略。模型之间的一致性增加了对其预测的信心，特别是当证明模型假设不会显著影响关键操作决策时。使用多生殖营养周期模型，我们计算了蚊子的年龄分布，这提供了一个将抗性管理与疾病传播联系起来的框架。未来的应用将研究针对IRM策略的更多特定场景评估，以为公共卫生政策提供信息。
