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实时数字处方解锁流感动态:来自2100万笔交易的证据

Rui Shen^1,2,3,4, Xueying Xu⁵, Lei Yang⁶

¹School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

NPJ digital medicine

|March 3, 2026

概括

来自中国的数字处方数据为流感流行病提供了2周的预测时间,优于环境数据. 这种验证的代理可以快速检测和96天的预测,增强公共卫生监测.

科学领域:

流行病学流行病学
数字健康数字健康
数据科学数据科学数据科学

背景情况:

传统的流感监测方法面临着显著的报告延迟 (1-2周),阻碍了及时的疫情应对.
数字健康数据为实时监测提供了潜力,但需要作为流行病代理的验证.
现有的环境和在线搜索数据显示,流感的预测准确性和因果验证存在局限性.

研究的目的:

评估数字处方数据的实用性,作为流感流行病的因果验证代理.
将数字处方的预测能力与环境和在线搜索数据进行比较.
开发和评估使用数字处方数据进行流感预测的深度学习框架.

主要方法:

分析了来自中国 (2022-2024) 31个省份的2108万笔数字处方交易.
应用融合交叉映射 (CCM) 来评估因果关系和预测头时间.
为预测开发一个时空深度学习框架 (GNN,Mamba,LSTM).

主要成果:

数字处方证明了流感的2周预测领先时间 (Δρ_CCM = 0.339),明显超过环境预测指标.
处方数据显示双向因果关系与实验室确认的流感,在线搜索和环境数据中缺少的签名.
一个深度学习模型以高精度实现了每日处方率的96天预测 (MAE<3.0在29/31个省份).

结论:

数字处方数据为流感监测提供了有效的实时代理,具有显著的预测能力.
这种数据流可以立即检测疫情 (24小时可用性) 和可操作的长期预测.
整合数字处方可以增强多来源的流行病监测系统,以改善公共卫生应对.

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