全球COVID-19负担的系统性决定因素:使用大数据驱动的人工智能进行纵向时间序列分析
Zicheng Cao1,2,3, Wenjie Han2,3, Xue Zhang2,3
1School of Public Health, Shantou University, Shantou, Guangdong, China.
Journal of medical Internet research
|December 29, 2025
概括
病毒变异驱动COVID-19的传播,而免疫力和疫苗接种显著减少了严重的疾病. 常规免疫接种提供交叉保护,具有有效的流行病控制的具体门.
科学领域:
- 流行病学 流行病学
- 机器学习 机器学习
- 公共卫生 公共卫生
背景情况:
- 在全球范围内,COVID-19继续复苏,受到病毒演变,人口免疫力和环境因素的影响.
- 塑造疾病传播和严重程度的动态相互作用仍然不太了解.
研究的目的:
- 量化病毒变种,免疫力,环境条件和公共卫生措施对COVID-19负担的相对贡献和时间动态.
- 分析对传播 (Rt),住院,ICU入院和死亡的影响.
主要方法:
- 使用大数据和可解释的机器学习 (XGBoost与Shapley附加解释) 进行了回顾性,纵向的时间序列分析.
- 来自38个国家的综合全球数据 (大流行开始至2022年12月) 关于变种,免疫力,环境,政策,医疗保健和移民.
主要成果:
- 病毒变异显著推动传播 (Rt),对严重结果的影响正在减少.
- 自然感染和COVID-19疫苗接种贡献随着疾病的严重程度而增加.
- 确定了关键疫苗接种覆盖率值,以减少传播 (29.9%) 和预防ICU (72.3%).
- 常规免疫接种,如黄热病疫苗,显示出交叉保护作用.
- 温度和医疗保健基础设施等环境因素也影响了疾病负担.
结论:
- 这种可解释的框架量化了变体适应性,双重疫苗接种值和对常规免疫接种的交叉保护.
- 它整合了多领域的数据,以实现对COVID-19负担的动态理解.
- 应用包括适应性疫苗接种策略,有针对性的变种监测和通过常规免疫计划加强流行病准备.
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