Mosquito-borne diseases cause millions of deaths each year and are increasingly spreading from tropical and subtropical regions into temperate zones, creating significant public health risks. The establishment of mosquito species in new areas increases the risk of local transmission (autochthonous cases), driven by both rising mosquito populations and viremic imported cases, infected travelers who can spark local transmission. Such developments present new challenges for public health systems in non-endemic regions. In Spain, in the Basque Country region, the spread of mosquitoes, driven by changing climatic conditions, has enhanced mosquito adaptation alongside an increase in imported cases of dengue, Zika, and chikungunya. By employing a model that captures the complexities of the mosquito life cycle driven by the interaction with weather variables, including temperature, precipitation, and humidity, and leveraging machine learning techniques, this study aims to predict \textit invasive mosquito abundance in provinces of the Basque Country, using egg count as a proxy and the weather features as key independent variables. Statistical analyses explored the impact of temperature, precipitation, and humidity on mosquito egg abundance. Using lagged climate variables and ovitrap egg counts, models were evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) metrics. The Random Forest (RF) model demonstrated the highest accuracy, followed by the Seasonal Autoregressive Integrated Moving Average (SARIMAX) model. Lastly, the best models were implemented to forecast \textit invasive mosquito abundance in the Basque Country provinces. This forecasting tool aids vector control strategies in regions with expanding mosquito populations, highlighting the need for ongoing entomological surveillance to improve mosquito spread assessments.

The authors have declared no competing interest.

This research is supported by the Basque Government through the Mathematical Modeling Applied to Health Project, BERC 2022-2025 program and by the Spanish Ministry of Sciences, Innovation and Universities: BCAM Severo Ochoa accreditation CEX2021-001142-S / MICIN / AEI/ 10.13039/501100011033. This work is also supported by the ARBOSKADI project for monitoring vector-borne diseases in the Basque Country, Euskadi. The collection of the data was funded by the Department of Food, Rural Development, Agriculture and Fisheries, and the Department of Health of the Basque Government, the Ministry of Health, Social Policy, and Equality of the Government of Spain and the project EU-LIFE 18 IPC/ES/000001 (Urban Klima 2050). Maira Aguiar and Aitor Cevidanes acknowledges the financial support by the Ministerio de Ciencia e Innovacion (MICINN) of the Spanish Government and European Union Next Generation EU/PRTR through the Ramon y Cajal grants RYC2021-031380-I and RYC2021-033084-I, respectively.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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The environmental data used in this study were retrieved from several meteorological stations managed by Euskalmet, the Basque Agency of Meteorology. This data is openly available through the OpenData Euskadi platform. The mosquito egg counts, collected using ovitraps, were provided by NEIKER, the Basque Institute for Agricultural Research and Development. Due to ethical considerations and commercial sensitivity, these data are not publicly available.

https://opendata.euskadi.eus/catalogo/-/estaciones-meteorologicas-lecturas-recogidas-en-2023/