在COVID-19大流行期间的死亡风险是由人类发展决定的
Kolja Nenoff1, Sarah Habershon2, Miguel D Mahecha2
1Institute for Earth System Science and Remote Sensing, University Leipzig, Talstr. 35, 04103, Leipzig, Sachsen, Germany. kolja.nenoff@uni-leipzig.de.
BMC global and public health
|March 1, 2026
概括
国家框架条件 (NFC) 显著影响了COVID-19的过度死亡率,超过了病例数. 机器学习模型揭示了社会经济因素,而不仅仅是报告的病例,是理解流行病结果和准备的关键.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 卫生经济学 卫生经济学
背景情况:
- 全球COVID-19大流行 (2020-2021) 在不同国家出现了多样化的过度死亡率.
- 高中收入国家比低收入和高收入国家显示出更高的过度死亡率变化.
- COVID-19病例和死亡率之间的差异表明潜在的结构脆弱性.
研究的目的:
- 调查国家框架条件 (NFC) 与COVID-19过度死亡率之间的关系.
- 利用社会经济因素和流行病数据,开发出过度死亡率的预测模型.
- 识别流行病结果跨国差异的关键决定因素.
主要方法:
- 使用报告的COVID-19病例和来自世界发展指标 (WDI) 的NFC构建了一个预测模型.
- 使用XGBoost (基于树的机器学习) 和e-Isomap (非线性维度减少) 来提取压缩的国家框架条件 (cNFC).
- 使用了SHapley添加式扩展 (SHAP) 来确定特征重要性和量化cNFC贡献.
主要成果:
- 机器学习模型解释了大约一半的过度死亡率的全球差异 (R2:中位数为49.7%).
- 压缩的国家框架条件 (cNFCs) 是比报告的COVID-19病例更强的预测者,比报告的COVID-19病例更强的预测者 (SHAP:中位数8.1比6.4).
- 可解释的人工智能 (XAI) 确定了相互关联的社会经济因素,如劳动力参与率和卫生支出,作为关键的死亡率决定因素.
结论:
- 与传统指标相比,压缩的国家框架条件 (cNFC) 为COVID-19过度死亡率提供了更好的解释.
- cNFC框架突出了报告病例数和其他指标所忽略的系统脆弱性.
- 这种方法为结构性弹性和流行病准备策略提供了新的见解.
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