旅行者急性呼吸道感染 (COVID-19) 风险预测:一个随机森林模型
Jingbo Yu1, Hao Yu2, Yuming Wang3
1Orthopaedics Institute of Tianjin, Tianjin Hospital, Tianjin, People's Republic of China.
Infectious Disease Modelling
|March 11, 2026
概括
一个随机的森林模型有效地预测了旅行者的传染病风险,优于传统方法. 这种方法有助于在疫情爆发期间进行早期查,增强公共卫生反应.
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:
- 对急性呼吸道感染 (ARI) 的早期查对于控制国际旅行者传播疾病至关重要.
- 在疫情爆发期间手动选旅行者数据是不切实际的,因为大量.
- 感染预测模型为有效查提供了可行的解决方案.
研究的目的:
- 开发和比较旅行者COVID-19感染风险的预测模型.
- 评估随机森林和多变量物流回归模型的性能.
- 确定感染风险的关键预测变量.
主要方法:
- 从乘客行程中提取出旅行史和座位号码.
- 使用随机森林和多变量物流回归构建风险评估模型.
- 通过使用灵敏度,特异性,精度,AUC和Brier分数来评估模型性能.
主要成果:
- 随机森林模型显示出卓越的区分能力和校准.
- 最重要的预测因素包括密切接触 (0.419),逃跑风险 (0.286) 和逗留风险 (0.182).
- 在高风险群体中,感染的发病率显著更高.
结论:
- 随机森林模型优于多变量回归来识别受感染的乘客.
- 流行病史变量对于预测呼吸道传染病至关重要.
- 开发的模型显示了管理未来传染病爆发的巨大潜力.
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