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.

翻译：准确预测传染病动态对于公共卫生规划与干预至关重要。人类移动行为在塑造疫情空间传播中起着核心作用，但移动数据存在噪声、具有间接性，且难以与疾病记录可靠整合。同时，疫情病例时间序列通常较短且以粗时间分辨率上报。这些条件限制了依赖洁净丰富数据的参数密集型移动感知预测模型的有效性。本研究提出移动感知因果适配器（MiCA），一种用于疫情预测的轻量级且架构无关的模块。MiCA通过因果发现推断移动关系，并通过门控残差混合将其整合到时序预测模型中。该设计使得轻量级预测器能够选择性地利用移动衍生的空间结构，同时在噪声和数据受限条件下保持鲁棒性，而无需引入图神经网络或完整注意力机制等重型关系组件。在四个真实世界疫情数据集（包括COVID-19发病率、COVID-19死亡率、流感和登革热）上的大量实验表明，MiCA能持续改进轻量级时序主干模型，在预测时间范围内平均实现7.5%的相对误差降低。此外，MiCA在保持轻量级的同时，获得了与最先进时空模型相竞争的性能。