Online FDR Controlling procedures for statistical SIS Model and its application to COVID19 data

2026-02-20

AI Summary

Existing online false discovery rate (FDR) control methods struggle to simultaneously ensure rigorous FDR control and high detection power when applied to discrete infectious disease data with complex dependency structures. This work proposes a novel online FDR control procedure based on the conditional local FDR (LIS), introducing LIS into an online framework for the first time. The method explicitly models data dependencies using a dynamic Bayesian network integrated with an SIS epidemic model and employs a sliding-window mechanism for adaptive monitoring. Under stationary and ergodic dependence, the approach guarantees valid FDR control while requiring only the specification of window width—no additional tuning parameters are needed. Experiments on both simulated data and real-world COVID-19 incidence records demonstrate that the proposed method achieves substantially higher statistical power while maintaining strict FDR control.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

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.

Problem

• online FDR control
• dependent data
• infectious disease
• statistical power
• discrete data

Innovation

• online FDR control
• conditional local FDR
• Dynamic Bayesian Network
• SIS model
• dependent discrete data