Mathematical modeling and simulation of malaria vector propagation : a tool for evaluation of novel control tools

Abstract

Background: Malaria transmission is sustained by highly adaptable Anopheles mosquitoes that persist across dry seasons and rebound in large numbers when the rains return. Despite major progress in vector control, knowledge gaps remain regarding the survival mechanisms that sustain mosquito populations during dry seasons. This study investigated the evolutionary and ecological mechanisms enabling such persistence and spread. It contributes critical insights for evaluating the effectiveness and sustainability of current and emerging vector control tools, including gene drive technologies. Methods: To investigate the dry season persistence mechanisms of malaria mosquitoes, existing studies on survival strategies were critically evaluated to identify strengths, weaknesses, and knowledge gaps. A novel population genetic modeling framework was developed to estimate the proportion of aestivating adults, mosquitoes typically difficult to sample due to unknown habitats, and was initially applied to the Anopheles coluzzii dataset from Mali. This model was subsequently extended to jointly estimate both aestivation and long-distance migration and then applied to temporal genetic data from Eastern Uganda. Additionally, to assess the potential impact of novel vector control strategies such as gene drive, the population genetic structure and demographic history of Anopheles gambiae and Anopheles arabiensis were characterized using amplicon sequencing data collected from three island and three mainland sites in Uganda. Results: Malaria mosquito populations persist through the dry season and rapidly rebound at the onset of the rainy season via four key mechanisms: aestivation, local refugia, local migration, and long-distance migration. Application of the developed population genetic model to temporal data from Mali successfully estimated the proportion of aestivating adults. When extended to incorporate both aestivation and long-distance migration and applied to temporal data from Eastern Uganda, the model revealed that the Sahelian region exhibits stronger seasonality compared to Eastern Uganda. Furthermore, genomic analysis of amplicon sequencing data from island and mainland sites in Uganda showed pronounced spatial population structure, with island populations showing greater genetic differentiation not only from mainland populations but also among individual island sites. This strong within-island differentiation highlights their potential suitability for contained gene drive field trials. Conclusion: This study aimed to determine the role of seasonal and evolutionary dynamics in mosquito survival by demonstrating that population genetics models can effectively estimate proportions of aestivating mosquitoes typically difficult to sample in the field. Moreover, this modelling framework is adaptable to quantify the relative contributions of multiple mosquito survival mechanisms within diverse ecological contexts. These insights are critical for optimizing the design of field trials for novel vector control strategies, such as gene drive, by elucidating population connectivity and seasonal persistence patterns. Additionally, the findings contribute to improved insecticide resistance management by illuminating how seasonal dynamics and vector life-history strategies influence the strength of selection and the spread of adaptive alleles, thereby supporting the responsible and sustainable deployment of genetic control technologies.