Impact of broader ecological and socio-environmental components on Aedes mosquito population dynamics: a spatial-temporal longitudinal study.

Abstract

The increasing spread of mosquito-borne diseases is a significant problem globally, but mosquito management strategies are less efficient. Therefore, a comprehensive understanding of the population dynamics of Aedes mosquito is essential for improving mosquito management strategies. Constructing a model to understand Aedes mosquito development in response to environmental factors is crucial to addressing these challenges. An extensive data set on Aedes spp. mosquito populations was constructed, considering the environmental factors temperature, water vapor pressure, wind speed, daylength, and rainfall. This data set, compiled from mosquito collections over a period of four years across multiple locations in Alabama, USA, facilitated the prediction of mosquito dynamics. The random forest model was used to explain mosquito population changes in response to these factors. These findings indicated that temperature, daylength, and water vapor pressure had the most significant impacts on mosquito population dynamics. The model also allowed predictions of mosquito population changes over time and across different geographic regions, extending beyond Alabama to the southeastern USA. This study provided valuable insights into the impacts of environmental factors on mosquito populations. This novel approach using machine learning and the random forest model will enable researchers to predict future mosquito populations and contribute to developing more-effective strategies for mosquito management. © 2024 Society of Chemical Industry. Published by John Wiley & Sons Ltd.