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<p>Traditional influenza surveillance suffers from 1–2 week reporting delays that compromise outbreak response. We analyzed 21.08 million digital prescription transactions from China’s largest on-demand medication delivery platform across 31 provinces (2022–2024) and demonstrated that prescription data provide causally validated epidemic proxies. Digital prescriptions exhibited 2-week predictive lead time (convergent cross-mapping skills Δ<i>ρ</i><sub>CCM</sub> = 0.339; <i>p</i> &lt; 0.001; 28/31 provinces), substantially exceeding environmental predictors (Δ<i>ρ</i><sub>CCM</sub> = 0.032–0.196) while matching online search indices (Δ<i>ρ</i><sub>CCM</sub> = 0.349). Critically, prescriptions demonstrated bidirectional causal coupling with laboratory-confirmed influenza positivity (forward: 28/31; reverse: 22/31 provinces). This dynamical signature was absent in online search (reverse: 2/31) and environmental variables (reverse: 0–5/31), distinguishing validated disease signals from confounded correlates. Prescriptions also exhibited greater environmental sensitivity compared with laboratory surveillance (air pollutants: 14–26/31 versus 3–7/31 provinces). Leveraging this validated proxy, a spatiotemporal deep learning framework integrating GNN, Mamba, and LSTM achieved 96-day forecasting of daily prescription rates (mean absolute error (MAE) = 1.166; MAE &lt; 3.0 in 29/31 provinces). Digital prescriptions thus enable both immediate epidemic detection (24 h data availability) and actionable long-range forecasting, providing an additional validated data stream for multi-source epidemic surveillance.</p><p></p>

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Real–time digital prescriptions unlock influenza dynamics: evidence from 21 million transactions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月4日

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  • Rui Shen,
  • Xueying Xu,
  • Lei Yang,
  • Zixing Wang,
  • Rongrong Yang,
  • Xiao Liu,
  • Zilu Xu,
  • Hao Jiang,
  • Na Sun,
  • Dayan Wang,
  • Hongbo Liu,
  • Luzhao Feng

摘要

Traditional influenza surveillance suffers from 1–2 week reporting delays that compromise outbreak response. We analyzed 21.08 million digital prescription transactions from China’s largest on-demand medication delivery platform across 31 provinces (2022–2024) and demonstrated that prescription data provide causally validated epidemic proxies. Digital prescriptions exhibited 2-week predictive lead time (convergent cross-mapping skills ΔρCCM = 0.339; p < 0.001; 28/31 provinces), substantially exceeding environmental predictors (ΔρCCM = 0.032–0.196) while matching online search indices (ΔρCCM = 0.349). Critically, prescriptions demonstrated bidirectional causal coupling with laboratory-confirmed influenza positivity (forward: 28/31; reverse: 22/31 provinces). This dynamical signature was absent in online search (reverse: 2/31) and environmental variables (reverse: 0–5/31), distinguishing validated disease signals from confounded correlates. Prescriptions also exhibited greater environmental sensitivity compared with laboratory surveillance (air pollutants: 14–26/31 versus 3–7/31 provinces). Leveraging this validated proxy, a spatiotemporal deep learning framework integrating GNN, Mamba, and LSTM achieved 96-day forecasting of daily prescription rates (mean absolute error (MAE) = 1.166; MAE < 3.0 in 29/31 provinces). Digital prescriptions thus enable both immediate epidemic detection (24 h data availability) and actionable long-range forecasting, providing an additional validated data stream for multi-source epidemic surveillance.