Explaining Environmental Distribution of <i>Aedes albopictus</i> using Machine Learning

Abstract

The Aedes albopictus mosquito, known for its role in transmitting diseases such as dengue fever, Zika virus, and chikungunya, poses a significant public health threat globally. Understanding its distribution patterns is crucial for effective disease surveillance and control. This study employs machine learning techniques, specifically MaxEnt modeling, to elucidate the relationship between environmental factors and the distribution of Aedes albopictus. Using presence-only data and a suite of environmental variables, we trained MaxEnt models to predict the potential distribution of Aedes albopictus across a geographical region. The models were validated using independent datasets and evaluated for their predictive accuracy and robustness. Our results reveal significant associations between Aedes albopictus presence and environmental factors such as temperature related variables. Furthermore, we employed spatial analysis techniques to identify areas at high risk of Aedes albopictus presence, aiding in targeted vector control strategies and disease prevention efforts. MaxEnt models demonstrated high predictive performance, effectively capturing the complex relationships between environmental variables and mosquito distribution in Nepal, India and Myanmar, along with Spain and Italy. By integrating machine learning algorithms with environmental data, this study provides valuable insights into the ecological drivers of Aedes albopictus distribution, enhancing our ability to mitigate the risk of mosquito-borne diseases in affected regions.