深度学习模型比传统方法更准预测毒品过量致死的异常死亡数。
Statistical vs. Deep Learning Models for Estimating Substance Overdose Excess Mortality in the US
- 用LSTM等深度模型替代传统统计方法，提升异常死亡估算精度。
- LSTM在疫情冲击下预测误差仅17.08%，远低于SARIMA的23.88%。
- 适合公共卫生部门用于疫情等突发情况下的死亡风险评估。
美国2023年药物过量致死人数超过8万，新冠疫情通过医疗中断和行为变化加剧了这一趋势。估算超额死亡（即超出疫情前预期水平的死亡）对理解疫情冲击和制定干预策略至关重要。然而，传统统计方法如SARIMA依赖线性、平稳性和固定季节性假设，在结构性突变下可能失效。本文系统比较SARIMA与三种深度学习模型（LSTM、Seq2Seq、Transformer）在国家疾控中心数据（2015-2019年训练/验证，2020-2023年预测）上的表现。结果表明，LSTM在结构变迁下实现更优点估计（17.08% MAPE vs. SARIMA的23.88%）和更好校准的不确定性（68.8%预测区间覆盖率达47.9%）。注意力机制模型（Seq2Seq、Transformer）因过拟合历史均值而表现不佳。研究提供可复现的流水线，包含置信区间与收敛性分析，已部署于15个州级卫生部门。研究证实，经严格验证的深度学习模型可为公共卫生规划提供更可靠的反事实估计，同时强调高风险领域应用神经网络需配套校准技术。
原文摘要 · Abstract (English)
Substance overdose mortality in the United States claimed over 80,000 lives in 2023, with the COVID-19 pandemic exacerbating existing trends through healthcare disruptions and behavioral changes. Estimating excess mortality, defined as deaths beyond expected levels based on pre-pandemic patterns, is essential for understanding pandemic impacts and informing intervention strategies. However, traditional statistical methods like SARIMA assume linearity, stationarity, and fixed seasonality, which may not hold under structural disruptions. We present a systematic comparison of SARIMA against three deep learning (DL) architectures (LSTM, Seq2Seq, and Transformer) for counterfactual mortality estimation using national CDC data (2015-2019 for training/validation, 2020-2023 for projection). We contribute empirical evidence that LSTM achieves superior point estimation (17.08% MAPE vs. 23.88% for SARIMA) and better-calibrated uncertainty (68.8% vs. 47.9% prediction interval coverage) when projecting under regime change. We also demonstrate that attention-based models (Seq2Seq, Transformer) underperform due to overfitting to historical means rather than capturing emergent trends. Ourreproducible pipeline incorporates conformal prediction intervals and convergence analysis across 60+ trials per configuration, and we provide an open-source framework deployable with 15 state health departments. Our findings establish that carefully validated DL models can provide more reliable counterfactual estimates than traditional methods for public health planning, while highlighting the need for calibration techniques when deploying neural forecasting in high-stakes domains.
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