一个动态的决策支持系统来应对COVID-19的传播:预测和集群方法
Amirreza Salehi1, Ardavan Babaei2,3, Vladimir Simić4,5,6
1Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran.
Informatics for health & social care
|January 8, 2026
概括
这项研究引入了流行病准备的动态系统,改进了COVID-19病例预测和国家绩效分析. 它强调了政策干预对流行病结果的影响.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 疫情防控需要对健康,政策和社会经济因素进行强有力的预测和分析.
- 现有的模型往往缺乏长期COVID-19病例预测和动态国家绩效评估的准确性.
研究的目的:
- 开发一个动态决策支持系统 (DDSS) 以提高国家疫情防控能力.
- 通过先进的机器学习 (ML) 技术,提高COVID-19病例预测的准确性.
- 根据绩效指标动态地对国家进行集群,并确定影响转变的因素.
主要方法:
- 集成的ML模型具有递归错误调整,用于预测累积的COVID-19病例.
- 雇佣的K-意味着集群和多属性决策 (MADM) 进行年对年国家分类.
- 使用Shapley添加式解释 (SHAP) 来解释模型的可解释性.
主要成果:
- 与ARIMA,ETS和LSTM模型相比,DDSS框架显示出更高的长期预测准确性.
- 确定了严格性指数作为国家绩效转移和集群变化的关键因素.
- 展示了如何放松干预,即使在高疫苗接种率下,也可能对一个国家的分类产生负面影响.
结论:
- 拟议的DDSS为战略性流行病政策制定提供了一个数据驱动的框架.
- 适应性ML预测和时间聚类提供了对流行病应对有效性的动态见解.
- 严格的干预措施对于管理流行病结果至关重要,无论疫苗接种水平如何.
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