In pandemic mitigation, strategies such as social distancing and mask-wearing are vital to prevent disease resurgence. Yet, monitoring adherence is challenging, as individuals might be reluctant to share behavioral data with public health authorities. To address this challenge and demonstrate a framework for conducting observational research with sensitive data in a privacy-conscious manner, we employ a privacy-centric epidemiological study design: the federated cohort. This approach leverages recent computational advances to allow for distributed participants to contribute to a prospective, observational research study while maintaining full control of their data. We apply this strategy here to explore pandemic intervention adherence patterns. Participants (n = 3808) were enrolled in our federated cohort via the “Google Health Studies” mobile application. Participants completed weekly surveys and contributed empirically measured mobility data from their Android devices between November 2020 to August 2021. Using federated analytics, differential privacy, and secure aggregation, we analyzed data in five 6-week periods, encompassing the pre- and post-vaccination phases. Our results showed that participants largely utilized non-pharmaceutical intervention strategies until they were fully vaccinated against COVID-19, except for individuals without plans to become vaccinated. Furthermore, this project offers a blueprint for conducting a federated cohort study and engaging in privacy-preserving research during a public health emergency.

在缓解大流行病方面，保持社交距离和戴口罩等策略对于防止疾病复发至关重要。然而，监测依从性具有挑战性，因为个人可能不愿意与公共卫生当局分享行为数据。为了应对这一挑战并展示以注重隐私的方式利用敏感数据进行观察研究的框架，我们采用了以隐私为中心的流行病学研究设计：联合队列。这种方法利用最新的计算进展，允许分布式参与者为前瞻性、观察性研究做出贡献，同时保持对其数据的完全控制。我们在这里应用这一策略来探索大流行干预的依从模式。参与者（ n = 3808）通过“Google Health Studies”移动应用程序加入我们的联合队列。参与者完成了每周调查，并提供了 2020 年 11 月至 2021 年 8 月期间通过 Android 设备进行的实证测量的移动数据。使用联合分析、差异隐私和安全聚合，我们分析了 5 个为期 6 周的数据，包括疫苗接种前和疫苗接种后阶段。我们的结果显示，除了没有计划接种疫苗的个人外，参与者在完全接种 COVID-19 疫苗之前大多采用非药物干预策略。此外，该项目还为在公共卫生紧急情况下进行联合队列研究和参与隐私保护研究提供了蓝图。