The surveillance of a pandemic is a challenging task, especially when crucial data is distributed and stakeholders cannot or are unwilling to share. To overcome this obstacle, federated methodologies should be developed to incorporate less sensitive evidence that entities are willing to provide. This study aims to explore the feasibility of pushing hypothesis tests behind each custodian's firewall and then meta-analysis to combine the results, and to determine the optimal approach for reconstructing the hypothesis test and optimizing the inference. We propose a hypothesis testing framework to identify a surge in the indicators and conduct power analyses and experiments on real and semi-synthetic data to showcase the properties of our proposed hypothesis test and suggest suitable methods for combining $p$-values. Our findings highlight the potential of using $p$-value combination as a federated methodology for pandemic surveillance and provide valuable insights into integrating available data sources.

翻译：疫情监测是一项具有挑战性的任务，尤其是当关键数据分布在多个利益相关方手中，且这些方无法或不愿共享数据时。为克服这一障碍，需开发联邦化方法，整合相关方愿意提供的低敏感度证据。本研究旨在探讨将假设检验置于各数据持有方防火墙内部、再通过元分析合并结果的可行性，并确定重构假设检验及优化推断的最优方法。我们提出一个假设检验框架，用于识别指标激增，并基于真实数据与半合成数据开展统计功效分析与实验，以展示所提出假设检验的特性，同时推荐合适的$p$值合并方法。研究结果凸显了将$p$值合并作为疫情监测联邦化方法的潜力，并为整合可用数据源提供了重要见解。