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Dengue is a life-threatening disease that has been increasing at an unprecedented rate in India as well as around the globe. The disease is affected by various factors such as population, urbanization, as well as climate change. Dynamic transmission of dengue fever is majorly influenced by climate change. Rising temperatures, altered rainfall patterns, and changing ecosystems contribute to the spread and severity of the disease. Random Forest, Long Short-Term Memory (LSTM), k-Nearest Neighbors (KNN), and LightGBM, are used here to understand and predict dengue fever dynamics, aiding in developing effective prevention and control strategies. This study focuses on the importance of interdisciplinary approaches, integrating artificial intelligence, climatology, epidemiology, ecology, and public health, to lessen the impact of climate change on dengue fever transmission and safeguard global health security.

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Modeling of Dengue Fever Outbreaks Using Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • Divyanshi Singh,
  • Harshita,
  • Ananya Srivastava,
  • Yajnaseni Dash,
  • Ajith Abraham

摘要

Dengue is a life-threatening disease that has been increasing at an unprecedented rate in India as well as around the globe. The disease is affected by various factors such as population, urbanization, as well as climate change. Dynamic transmission of dengue fever is majorly influenced by climate change. Rising temperatures, altered rainfall patterns, and changing ecosystems contribute to the spread and severity of the disease. Random Forest, Long Short-Term Memory (LSTM), k-Nearest Neighbors (KNN), and LightGBM, are used here to understand and predict dengue fever dynamics, aiding in developing effective prevention and control strategies. This study focuses on the importance of interdisciplinary approaches, integrating artificial intelligence, climatology, epidemiology, ecology, and public health, to lessen the impact of climate change on dengue fever transmission and safeguard global health security.