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Forecasting invasive mosquito abundance in the Basque Country, Spain using machine learning techniques.

Vanessa Steindorf¹, Hamna Mariyam K B², Nico Stollenwerk²

¹M3A, Basque Center for Applied Mathematics, Mazarredo 14, 48009, Bilbao, Bizkaia, Spain. vsteindorf@bcamath.org.

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Summary

Climate change drives invasive mosquito spread. Machine learning models accurately forecast Aedes mosquito abundance using weather data, aiding public health in new regions.

Keywords:

Aedes albopictus Dengue Entomological surveillance Machine learning Mosquito eggs Vector-borne diseases

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Area of Science:

Environmental Science
Epidemiology
Vector Ecology

Background:

Mosquito-borne diseases pose increasing global health risks due to climate change and species expansion into new territories.
Invasive mosquitoes in the Basque Country, Spain, heighten the risk of local transmission of diseases like dengue, Zika, and chikungunya.
Public health systems face challenges managing invasive mosquito populations and potential disease outbreaks in non-endemic areas.

Purpose of the Study:

To forecast the abundance of invasive Aedes mosquitoes in the Basque Country using machine learning.
To identify key weather variables influencing mosquito populations.
To evaluate the predictive accuracy of different forecasting models.

Main Methods:

Utilized machine learning models (Random Forest, SARIMAX) to predict Aedes mosquito abundance based on egg counts.
Analyzed the relationship between weather variables (temperature, precipitation, humidity) and mosquito egg counts, including lagged variables.
Assessed model performance using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE).

Main Results:

Temperature, precipitation, and humidity significantly impact mosquito egg abundance.
The Random Forest model achieved the highest accuracy in forecasting mosquito abundance.
Including lagged climate and egg count data improved prediction accuracy.

Conclusions:

Climate-driven forecasting tools are crucial for predicting mosquito abundance in expanding regions.
Continuous entomological surveillance is essential for refining mosquito spread forecasts.
These tools support the development and evaluation of effective vector control strategies.