Machine Learning Modeling of Aedes albopictus Habitat Suitability in the 21st Century

SIMPLE SUMMARY: The Asian tiger mosquito, Aedes albopictus, is a highly invasive and adaptive vector of viruses that can cause human diseases, such as dengue, chikungunya, and zika. As climate and socio-economic changes continue, the mosquito’s suitable habitat range is expected to expand, posing a significant threat to global public health. To predict the shifts in the mosquito’s global habitat suitability, we developed an ensemble machine learning model that combines a Random Forest and XGBoost binary classifiers. The model was trained using global vector surveillance data and a collection of climate and environmental constraints. We project a significant expansion of the mosquito’s habitat suitability, with at least an additional billion people at risk of vector-borne diseases by the mid-21st century. A number of highly populated areas of the world, such as the northern parts of the USA, Europe, and India, will be at risk of Ae. albopictus-borne diseases by the end of the century. Our findings highlight the need for coordinated preventive surveillance efforts by local authorities and stakeholders to control the spread of the mosquito and prevent disease outbreaks. ABSTRACT: The Asian tiger mosquito, Aedes albopictus, is an important vector of arboviruses that cause diseases such as dengue, chikungunya, and zika. The vector is highly invasive and adapted to survive in temperate northern territories outside its native tropical and sub-tropical range. Climate and socio-economic change are expected to facilitate its range expansion and exacerbate the global vector-borne disease burden. To project shifts in the global habitat suitability of the vector, we developed an ensemble machine learning model, incorporating a combination of a Random Forest and XGBoost binary classifiers, trained with a global collection of vector surveillance data and an extensive set of climate and environmental constraints. We demonstrate the reliable performance and wide applicability of the ensemble model in comparison to the known global presence of the vector, and project that suitable habitats will expand globally, most significantly in the northern hemisphere, putting at least an additional billion people at risk of vector-borne diseases by the middle of the 21st century. We project several highly populated areas of the world will be suitable for Ae. albopictus populations, such as the northern parts of the USA, Europe, and India by the end of the century, which highlights the need for coordinated preventive surveillance efforts of potential entry points by local authorities and stakeholders.