Spatial modeling of the population dynamics of Anopheles mosquitoes in Madagascar

Abstract Background Malaria, whose parasites are transmitted by Anopheles mosquitoes, remains a major public health burden in Madagascar despite the control measures led by the National Malaria Control Program. Understanding the population dynamics of Anopheles mosquitoes is therefore essential to optimize malaria surveillance and control. This study aimed to develop a model incorporating environmental, climatic and agricultural determinants of Anopheles abundance to predict their spatiotemporal distribution. Methods We developed a model of spatiotemporal dynamics for four Anopheles species, vectors of malaria parasite in Madagascar: Anopheles arabiensis, Anopheles coustani, Anopheles funestus and Anopheles gambiae. This model was based on the life cycle of Anopheles and accounted for both the aquatic and aerial phases of their development. It used a system of differential equations to estimate the number of Anopheles mosquitoes at each stage of development. The Ocelet language, dedicated to the modeling of spatial dynamics, was used to produce simulations based on climate and environmental data. The model explicitly integrates the agricultural calendar to adjust the environmental carrying capacity of larval habitats. Model outputs were validated with entomological data collected in Vohimasy (Farafangana districts, 2014–2017). Results 24 simulation outputs, from three Anopheles species and eight sites, were obtained and the validation revealed a significant correlation between field observations and model predictions: the correlation coefficients obtained ranged from 0.70 to 0.76. The predicted abundance of host-seeking Anopheles varied seasonally influenced by precipitation, temperature and environmental carrying capacity. The model exhibited robustness across sites with diverse climates and accurately reproduced interannual dynamics. The integration of the agricultural calendar significantly reduced the overestimation of the density of host-seeking adult females. Conclusion The developed Anopheles dynamics model provides a valuable tool for predicting mosquito abundance and distribution over time and space. It correctly predicted the abundance at villages with contrasting climates and reproduced interannual dynamics well. A distinctive aspect of this work lies in the explicit integration of seasonal agricultural practices into the estimation of larval habitat availability. This allows for a more accurate and transferable modeling of Anopheles population dynamics.