Abstract

The emergence and rapid spread of insecticide resistance in aphid populations is a significant concern for sustainable agriculture pest management worldwide. In this study, we develop a detailed population dynamics model based on an SEIR (Susceptible-Exposed-Infectious-Resistant) compartmental framework to capture the intricate biological and ecological processes that fuel resistance development. Incorporating robust field data on aphid populations' demographics and resistance phenotypes, we create and execute an algorithmic simulation designed to track and quantify the temporal dynamics of resistance growth for various insecticide exposure scenarios estimation procedures, such as sensitivity and uncertainty analyses, assessed model accuracy and reliability. The simulation results expose the impact of mutation rates, gene flow, intensity of selective pressures, and population heterogeneity on resistance evolution Moreover, the model illustrates the pivotal insecticide application thresholds that may alternatively prolong or hasten resistance accumulation. This helps broaden understanding of aphids' resistance mechanisms while offering a flexible computational framework for adaptive, optimized pest management. The methodological approach and algorithmic framework proposed here are relevant for studying resistance evolution in other arthropod pests and vectors.