应对COVID-19的变异信息决策支持系统:转移学习和多属性决策方法
Amirreza Salehi Amiri1, Ardavan Babaei2, Vladimir Simic3,4
1Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran.
PeerJ. Computer science
|September 24, 2024
概括
变体信息决策支持系统 (VIDSS) 使用过去的COVID-19变体数据和多属性决策来改善未来的变体预测. 该系统为有效的流行病应对策略提供动态,数据驱动的洞察力.
科学领域:
- 流行病学和公共卫生.
- 数据科学和人工智能数据科学和人工智能
- 卫生政策和管理卫生政策和管理
背景情况:
- 由关注变体 (VOCs) 标志着的COVID-19流行病的演变,需要为政府提供敏捷的决策支持.
- 现有的系统很难适应新兴病毒株的动态性质.
研究的目的:
- 引入变异信息决策支持系统 (VIDSS) 以对特定VOC特征进行动态适应.
- 通过利用历史数据和转移学习,提高对未来变体的预测准确度.
主要方法:
- 利用多属性决策 (MADM) 技术,根据过去的改进和同行比较来评估国家表现.
- 整合了从以前的VOC预测模型中学习的转移,以改善对新变种的预测.
- 采用K折交叉验证和SHAP图表来评估模型准确性和特征重要性.
主要成果:
- VIDSS框架表现出强大的预测准确性,神经网络通过转移学习显著增强.
- 混合MADM方法提供了有洞察力的特定国家分数,确定了影响COVID-19传播的关键因素.
- 疫苗接种率,ICU入院率和住院率一直是不同变异的关键特征.
结论:
- 利用历史变体数据大大改善了对未来变体影响的预测.
- VIDSS为决策者提供动态的,数据驱动的决策支持,以优化流行病战略和资源配置.
- 该系统为导航不断发展的COVID-19流行病的复杂性提供了关键的见解.
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