Forecasting invasive mosquito abundance in the Basque Country, Spain using machine learning techniques
Background Mosquito‑borne diseases cause millions of deaths each year and are increasingly spreading from tropical and subtropical regions into temperate zones, posing significant public health risks. In the Basque Country region of Spain, changing climatic conditions have driven the spread of invas...
| Autores: | , , , , , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Recursos: | Basque Center for Applied Mathematics (BCAM) |
| Repositorio: | BIRD. BCAM's Institutional Repository Data |
| OAI Identifier: | oai:bird.bcamath.org:20.500.11824/1933 |
| Acesso em linha: | http://hdl.handle.net/20.500.11824/1933 https://doi.org/10.1186/s13071-025-06733-y |
| Access Level: | acceso abierto |
| Palavra-chave: | Mosquito eggs Dengue Aedes albopictus Machine learning Vector‑borne diseases Entomological |
| Resumo: | Background Mosquito‑borne diseases cause millions of deaths each year and are increasingly spreading from tropical and subtropical regions into temperate zones, posing significant public health risks. In the Basque Country region of Spain, changing climatic conditions have driven the spread of invasive mosquitoes, increasing the potential for local transmission of diseases such as dengue, Zika, and chikungunya. The establishment of mosquito species in new areas, coupled with rising mosquito populations and viremic imported cases, presents challenges for public health systems in non‑endemic regions. Methods This study uses models that capture the complexities of the mosquito life cycle, driven by interactions with weather variables, including temperature, precipitation, and humidity. Leveraging machine learning techniques, we aimed to forecast Aedes invasive mosquito abundance in the provinces of the Basque Country, using egg count as a proxy and weather features as key independent variables. A Spearman correlation was used to assess relationships between climate variables and mosquito egg counts, as well as their lagged time series versions. Forecasting models, including random forest (RF) and seasonal autoregressive integrated moving average (SARIMAX), were evaluated using root mean squared error (RMSE) and mean absolute error (MAE) metrics. Results Statistical analysis revealed significant impacts of temperature, precipitation, and humidity on mosquito egg abundance. The random forest (RF) model demonstrated the highest forecasting accuracy, followed by the SARIMAX model. Incorporating lagged climate variables and ovitrap egg counts into the models improved predictions, enabling more accurate forecasts of Aedes invasive mosquito abundance. Conclusions The findings emphasize the importance of integrating climate‑driven forecasting tools to predict the abundance of mosquitoes where data are available. Furthermore, this study highlights the critical need for ongoing entomological surveillance to enhance mosquito spread forecasting and contribute to the development and assessment of effective vector control strategies in regions of mosquito expansion. |
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