在载体控制中使用人工智能:公共卫生的新途径
Yusuf Hared Abdi1,2, Yakub Burhan Abdullahi2, Mohamed Sharif Abdi2
1Department of Environmental Science, Faculty of Geoscience & Environment, Hormuud University, Mogadishu, Somalia.
Journal of vector borne diseases
|September 12, 2025
概括
人工智能通过改善监测和疫情预测来增强载体传播疾病的控制. 由人工智能驱动的工具提供了精确的物种识别和优化干预措施,从而带来了更有弹性的公共卫生计划.
科学领域:
- 公共卫生 公共卫生
- 载体传播疾病 载体传播疾病
- 人工智能的人工智能
背景情况:
- 媒介性疾病对全球健康构成重大威胁,气候变化,城市化和抗杀虫剂耐药性加剧了这种威胁.
- 目前的控制策略往往是反应性的,并与诸如入侵性蚊子物种等新出现的挑战作斗争.
研究的目的:
- 审查人工智能 (AI) 在加强载体监测,预测疫情和优化公共卫生干预方面的应用.
- 评估AI在提高矢量控制程序的效率和有效性方面的潜力.
主要方法:
- 对人工智能驱动系统的审查,包括卷积神经网络 (CNN) 和用于矢量识别和疫情预测的机器学习模型.
- 将卫星图像,气候数据和公民科学输入集成到预测模型中.
- 强化学习和基于无人机的绘图的应用,用于资源分配和减少杀虫剂.
主要成果:
- 人工智能模型在实时识别蚊子物种方面达到90%以上的准确性,有助于针对入侵载体作出有针对性的反应.
- 机器学习模型,结合各种数据,预测登革热发病率30天前,准确率为81%.
- 人工智能优化了资源分配,可以减少20-40%的杀虫剂使用.
结论:
- 人工智能提供了一个强大的工具来增强传统方法,实现实时决策和数据驱动的洞察力,用于矢量控制.
- 克服数据不平等,算法偏见和基础设施差距等实施障碍对于公平的AI采用至关重要.
- 持续的跨部门伙伴关系和特定环境的适应对于有弹性的载体控制系统和减轻疾病负担至关重要.
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