Steindorf Vanessa, K B Hamna Mariyam, Stollenwerk Nico, Cevidanes Aitor, Barandika Jesús F, Vazquez Patricia, García-Pérez Ana L, Aguiar Maíra
M3A, Basque Center for Applied Mathematics, Mazarredo 14, 48009, Bilbao, Bizkaia, Spain.
Animal Health Department, NEIKER-Basque Institute for Agricultural Research and Development, Basque Research and Technology Alliance (BRTA), 48160, Derio, Bizkaia, Spain.
Parasit Vectors. 2025 Mar 15;18(1):109. doi: 10.1186/s13071-025-06733-y.
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.
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.
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.
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.
蚊媒疾病每年造成数百万人死亡，并且越来越多地从热带和亚热带地区传播到温带地区，构成重大的公共卫生风险。在西班牙的巴斯克地区，气候变化推动了入侵蚊子的传播，增加了登革热、寨卡病毒和基孔肯雅热等疾病在当地传播的可能性。新地区蚊子物种的建立，加上蚊子数量的增加和输入性病毒血症病例，给非流行地区的公共卫生系统带来了挑战。
本研究使用的模型捕捉了蚊子生命周期的复杂性，该复杂性由与天气变量（包括温度、降水和湿度）的相互作用驱动。利用机器学习技术，我们旨在以卵计数作为替代指标，以天气特征作为关键自变量，预测巴斯克地区各省埃及伊蚊入侵蚊子的丰度。使用斯皮尔曼相关性来评估气候变量与蚊卵计数之间的关系，以及它们的滞后时间序列版本。使用均方根误差（RMSE）和平均绝对误差（MAE）指标评估包括随机森林（RF）和季节性自回归积分移动平均（SARIMAX）在内的预测模型。
统计分析表明温度、降水和湿度对蚊卵丰度有显著影响。随机森林（RF）模型显示出最高的预测准确性，其次是SARIMAX模型。将滞后的气候变量和诱蚊产卵器卵计数纳入模型可改善预测，从而能够更准确地预测埃及伊蚊入侵蚊子的丰度。
研究结果强调了整合气候驱动的预测工具以预测有数据地区蚊子丰度的重要性。此外，本研究突出了持续进行昆虫学监测的迫切需求，以加强蚊子传播预测，并为蚊子扩散地区有效病媒控制策略的制定和评估做出贡献。
