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<p>Vector-borne diseases, particularly those transmitted by <i>Aedes</i> mosquitoes, remain a major public health challenge globally, with species like <i>Aedes aegypti</i> and <i>Aedes albopictus</i> as primary vectors of dengue, chikungunya, and Zika viruses. The management of these mosquitoes is complicated by their adaptability to urban environments and the resource limitations of mosquito control programs. This study presents a proof-of-concept approach to improve <i>Aedes</i> surveillance and control by integrating remote sensing data and machine learning techniques. We used Sentinel-2 satellite imagery and environmental data to predict <i>Aedes</i> abundance across Harris County, Texas, incorporating both weather and socioeconomic features into predictive models. Using XGBoost, a machine learning algorithm, we identified high-risk areas for mosquito abundance with increased accuracy over traditional methods. This study demonstrates the potential of combining remote sensing with machine learning for enhancing mosquito surveillance programs, offering an innovative new strategy to improve vector control and mitigate the spread of mosquito-borne diseases. Future research should explore broader geographic applications and additional environmental variables to further refine predictive models.</p>

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The prediction of Aedes mosquito abundance using remote sensing and machine learning in Harris County, Texas

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月29日

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  • Huixuan Li,
  • Jun Zhou,
  • Kyndall Dye-Braumuller,
  • Max Vigilant,
  • Matthew DeGennaro,
  • Morgan H. Sekou,
  • Abiodun O. Oluyomi,
  • Melissa S. Nolan,
  • Sarah M. Gunter

摘要

Vector-borne diseases, particularly those transmitted by Aedes mosquitoes, remain a major public health challenge globally, with species like Aedes aegypti and Aedes albopictus as primary vectors of dengue, chikungunya, and Zika viruses. The management of these mosquitoes is complicated by their adaptability to urban environments and the resource limitations of mosquito control programs. This study presents a proof-of-concept approach to improve Aedes surveillance and control by integrating remote sensing data and machine learning techniques. We used Sentinel-2 satellite imagery and environmental data to predict Aedes abundance across Harris County, Texas, incorporating both weather and socioeconomic features into predictive models. Using XGBoost, a machine learning algorithm, we identified high-risk areas for mosquito abundance with increased accuracy over traditional methods. This study demonstrates the potential of combining remote sensing with machine learning for enhancing mosquito surveillance programs, offering an innovative new strategy to improve vector control and mitigate the spread of mosquito-borne diseases. Future research should explore broader geographic applications and additional environmental variables to further refine predictive models.