We propose an online false discovery rate (FDR) controlling method based on conditional local FDR (LIS), designed for infectious disease datasets that are discrete and exhibit complex dependencies. Unlike existing online FDR methods, which often assume independence or suffer from low statistical power in dependent settings, our approach effectively controls FDR while maintaining high detection power in realistic epidemic scenarios. For disease modeling, we establish a Dynamic Bayesian Network (DBN) structure within the Susceptible-Infected-Susceptible (SIS) model, a widely used epidemiological framework for infectious diseases. Our method requires no additional tuning parameters apart from the width of the sliding window, making it practical for real-time disease monitoring. From a statistical perspective, we prove that our method ensures valid FDR control under stationary and ergodic dependencies, extending online hypothesis testing to a broader range of dependent and discrete datasets. Additionally, our method achieves higher statistical power than existing approaches by leveraging LIS, which has been shown to be more powerful than traditional $p$-value-based methods. We validate our method through extensive simulations and real-world applications, including the analysis of infectious disease incidence data. Our results demonstrate that the proposed approach outperforms existing methods by achieving higher detection power while maintaining rigorous FDR control.

翻译：本文提出了一种基于条件局部错误发现率（LIS）的在线错误发现率（FDR）控制方法，专为具有离散性和复杂依赖性的传染病数据集设计。与现有在线FDR方法通常假设独立性或在依赖场景下统计功效较低不同，我们的方法能在实际疫情场景中有效控制FDR，同时保持较高的检测功效。在疾病建模方面，我们在易感-感染-易感（SIS）模型（一种广泛使用的传染病流行病学框架）内建立了动态贝叶斯网络（DBN）结构。除滑动窗口宽度外，我们的方法无需额外调整参数，便于实时疾病监测。从统计角度，我们证明了该方法在平稳且遍历的依赖性条件下能确保有效的FDR控制，从而将在线假设检验扩展到更广泛的依赖性和离散数据集。此外，通过利用LIS（已被证明比传统的基于$p$值的方法更具统计功效），我们的方法获得了比现有方法更高的统计功效。我们通过大量模拟和实际应用（包括传染病发病率数据分析）验证了该方法。结果表明，所提出的方法在保持严格FDR控制的同时，实现了比现有方法更高的检测功效。