中国媒介生物学及控制杂志 ›› 2021, Vol. 32 ›› Issue (4): 503-508.DOI: 10.11853/j.issn.1003.8280.2021.04.024

• 综述 • 上一篇    

机器学习在蚊虫及蚊媒传染病研究中的应用进展

孙燕群1,2, 张守刚1, 赵姗姗1, 陆墨原1, 张艳1, 王冲1, 李成国1   

  1. 1. 南京市疾病预防控制中心消毒与病媒生物防制科, 南京医科大学附属南京疾病预防控制中心, 江苏南京 210003;
    2. 军事科学院军事医学研究院微生物流行病研究所, 病原微生物生物安全国家重点实验室, 北京 100071
  • 收稿日期:2021-02-22 出版日期:2021-08-20 发布日期:2021-08-20
  • 作者简介:孙燕群,男,主管医师,主要从事蚊虫及蚊媒病监测与防制工作,E-mail:sunyq@njcdc.cn
  • 基金资助:
    南京市卫生科技发展专项资金(YKK17200,YKK18178);南京市第十周期医学重点专科(传染病预防控制)

Application progress of machine learning in mosquito and mosquito-borne disease research

SUN Yan-qun1,2, ZHANG Shou-gang1, ZHAO Shan-shan1, LU Mo-yuan1, ZHANG Yan1, WANG Chong1, LI Cheng-guo1   

  1. 1. Department of Disinfection and Vector Control, Affiliated Nanjing Center for Disease Control and Prevention, Nanjing Center for Disease Control and Prevention, Nanjing Medical University, Nanjing, Jiangsu 210003, China;
    2. State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
  • Received:2021-02-22 Online:2021-08-20 Published:2021-08-20
  • Supported by:
    Supported by the Nanjing Medical Science and Technology Development Fund (No. YKK17200,YKK18178) and the Tenth Cycle of Nanjing’s Key Medical Specialty (Prevention and Control of Infectious Diseases)

摘要: 该文主要介绍机器学习在全球蚊虫及蚊媒传染病研究中的应用进展,系统搜索国内外数据库进行文献调研,简单回顾了机器学习的主要方法,对机器学习在蚊虫及蚊媒传染病研究中的几大应用进行了系统总结,主要聚集在蚊虫和蚊媒传染病预测预警、蚊虫图像和声音识别、蚊虫生物学等研究领域,为国内蚊虫及蚊媒传染病防制研究提供新的视角。

关键词: 蚊媒, 蚊媒传染病, 机器学习, 研究进展

Abstract: This article mainly introduced the application progress of machine learning in global mosquito and mosquito-borne disease research, systematically searched domestic and foreign databases for literature research, briefly reviewed the main methods of machine learning, and systematically summarized several major applications of machine learning in mosquito and mosquito-borne disease research. It is mainly concentrated in the research fields of mosquito and mosquito-borne disease prediction and early warning, mosquito image and sound recognition, and mosquito biology, providing a new perspective for domestic mosquito and mosquito-borne disease prevention and control.

Key words: Mosquito, Mosquito-borne disease, Machine learning, Research progress

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