对禽流感的数学和机器学习模型进行系统审查
Shixun Huang1,2, Nicola Luigi Bragazzi3, Zahra Movahedi Nia1,4,5,6
1Artificial Intelligence & Mathematical Modeling Lab (AIMM Lab), Dalla Lana School of Public Health, University of Toronto, 155 College St, Room 500, Toronto, ON M5T 3M7, Canada.
One health (Amsterdam, Netherlands)
|October 10, 2025
概括
本综述分析了禽流感 (AI) 建模,发现数学,机器学习和混合方法对于预测疫情和评估风险至关重要. 改进数据共享和验证对于未来的人工智能控制至关重要.
科学领域:
- 兽医流行病学 兽医流行病学
- 计算生物学是一种计算生物学.
- 公共卫生建模公共卫生建模
背景情况:
- 禽流感对公众健康,农业和粮食安全构成重大威胁.
- 数学,统计和机器学习模型是理解人工智能传播,预测疫情和指导控制策略的重要工具.
研究的目的:
- 系统地审查和分类现有的AI建模方法.
- 评估这些模型在风险评估,疫情预测,动态建模和参数估计中的应用.
- 确定人工智能建模的挑战和未来方向.
主要方法:
- 按照PRISMA指南进行系统的文献审查.
- 在PubMed/MEDLINE,Scopus,Web of Science和Embase进行了搜索.
- 将43项研究分类为数学/统计,机器学习和混合模型.
主要成果:
- 数学/统计模型 (60.47%) 经常关注传输动态.
- 机器学习模型 (27.91%) 主要用于风险评估和疫情预测.
- 混合模型 (11.63%) 显示了提高准确性的潜力,但验证在所有模型类型中都不一致.
结论:
- 目前的人工智能建模工作的方法和验证严格程度各不相同.
- 增强数据共享,实时数据集成和标准化验证至关重要.
- 建议进一步开发混合模型,以改善AI爆发的预测和控制.
关键词:
禽流感是禽流感的一种疾病.分区模型是分区模型.流行病学建模的流行病学建模.混合动力模型 混合动力模型机器学习是机器学习.数学建模的数学建模疫情爆发的预测预测.风险评估 风险评估 风险评估统计模型 统计模型传输动态 传输动态更多相关视频
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