Abstract
Accurate forecasting of infectious disease dynamics is critical for public health planning and intervention. Human mobility plays a central role in shaping the spatial spread of epidemics, but mobility data are noisy, indirect, and difficult to integrate reliably with disease records. Meanwhile, epidemic case time series are typically short and reported at coarse temporal resolution. These conditions limit the effectiveness of parameter-heavy mobility-aware forecasters that rely on clean and abundant data. In this work, we propose the Mobility-Informed Causal Adapter (MiCA), a lightweight and architecture-agnostic module for epidemic forecasting. MiCA infers mobility relations through causal discovery and integrates them into temporal forecasting models via gated residual mixing. This design allows lightweight forecasters to selectively exploit mobility-derived spatial structure while remaining robust under noisy and data-limited conditions, without introducing heavy relational components such as graph neural networks or full attention. Extensive experiments on four real-world epidemic datasets, including COVID-19 incidence, COVID-19 mortality, influenza, and dengue, show that MiCA consistently improves lightweight temporal backbones, achieving an average relative error reduction of 7.5\% across forecasting horizons. Moreover, MiCA attains performance competitive with SOTA spatio-temporal models while remaining lightweight.
Abstract (translated)
准确预测传染病的动态对于公共卫生规划和干预至关重要。人类移动性在塑造疫情空间传播方面起着核心作用，但移动数据往往嘈杂、间接且难以与疾病记录可靠地整合。同时，流行病病例的时间序列通常较短，并以较低的时间分辨率报告。这些条件限制了依赖于清洁而丰富数据的参数密集型移动感知预测模型的有效性。 为此，本文提出了一种轻量级且架构无关的模块——受移动信息启发的因果适配器（Mobility-Informed Causal Adapter, MiCA），用于流行病预测。MiCA 通过因果发现推断移动关系，并通过门控残差混合将其集成到时间序列预测模型中。这一设计使轻量级预测器能够在嘈杂且数据有限的情况下，选择性地利用从移动信息衍生的空间结构，而不引入复杂的图神经网络或全注意力机制等重型关系组件。 在四个真实世界流行病数据集上的广泛实验（包括COVID-19发病率、COVID-19死亡率、流感和登革热）表明，MiCA 在预测时间范围内始终提高了轻量级的时间序列骨干模型的性能，平均相对误差降低了7.5%。此外，在保持轻量级的同时，MiCA 的表现可与最先进的时空模型相媲美。 总的来说，这项工作展示了一种新颖的方法，通过结合因果推理和门控混合机制来改进流行病预测，从而在实际应用中提供了有效的解决方案。
URL
https://arxiv.org/abs/2601.11089
