如何（不）将神经模型与机制模型混合用于流行病预测
How (Not) to Hybridize Neural and Mechanistic Models for Epidemiological Forecasting
AI总结：
针对流行病预测中神经-机制混合模型在部分可观测和非平稳动态下失效的问题，提出通过显式分解感染序列的多尺度结构作为控制信号，驱动受控神经ODE与流行病模型耦合，实现最优预测和时变参数推断。
摘要：
基于监测数据进行流行病预测是一个难题，将机制性房室模型与神经模型混合是一个自然的方向。机制结构有助于保持轨迹在流行病学上的合理性，而神经组件可以捕捉非平稳、数据自适应的效应。然而在实践中，许多看似直接的耦合在部分可观测性以及由行为、免疫力衰退、季节性和干预措施驱动的持续变化的传播动态下会失效。我们记录了这些失效模式，并表明鲁棒性能需要将非平稳性显式化：我们从观测到的感染序列中提取并外推多尺度结构，并将其作为可解释的控制信号，用于与流行病模型耦合的受控神经ODE。具体来说，我们将感染分解为趋势、季节和残差分量，并利用这些信号驱动连续时间潜在动力学，同时联合预测和推断时变的传播率、恢复率和免疫力丧失率。在早期爆发和多波次情景中，我们的方法在所有五个数据集上均达到了最低的RMSE（相比最强基线降低27-70%），实现了最佳的峰值检测精度，并在不依赖辅助协变量的情况下，恢复出真实范围内的时变流行病学参数。
英文摘要：
Epidemiological forecasting from surveillance data is a hard problem and hybridizing mechanistic compartmental models with neural models is a natural direction. The mechanistic structure helps keep trajectories epidemiologically plausible, while neural components can capture non-stationary, data-adaptive effects. In practice, however, many seemingly straightforward couplings fail under partial observability and continually shifting transmission dynamics driven by behavior, waning immunity, seasonality, and interventions. We catalog these failure modes and show that robust performance requires making non-stationarity explicit: we extract multi-scale structure from the observed infection series and use it as an interpretable control signal for a controlled neural ODE coupled to an epidemiological model. Concretely, we decompose infections into trend, seasonal, and residual components and use these signals to drive continuous-time latent dynamics while jointly forecasting and inferring time-varying transmission, recovery, and immunity-loss rates. Across early outbreak and multi-wave regimes, our approach attains the lowest RMSE on five datasets (21-63% reduction over the strongest default-configured baseline), achieves the best peak detection accuracy, and infers time-varying epidemiological rates within ground-truth ranges, without relying on auxiliary covariates.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
