The changing nature of the COVID-19 pandemic has highlighted the importance of comprehensively considering its impacts and considering changes over time. Most COVID-19 related research addresses narrowly focused research questions and is therefore limited in addressing the complexities created by the interrelated impacts of the pandemic. Such research generally makes use of only one of either (1) actively collected data such as surveys, or (2) passively collected data from sources such as mobile phones or financial transactions. So far, only one other study collects both active and passive data, and does so longitudinally. Here we describe a rich panel dataset of active and passive data from US residents collected between August 2020 and September 2022. Active data includes a repeated survey measuring travel behavior, compliance with COVID-19 mandates and restrictions, physical health, economic well-being, vaccination status, and other factors. Passively collected data consists of Point of Interest (POI) check in data indicating all the locations visited by study participants. We also closely tracked COVID-19 policies across counties of residence of study participants throughout the study period. The combination of the longitudinal active and passive data helps overcome the limitations of active or passive data when used individually as well as the limitations posed by cross-sectional dataset and allows important research questions to be answered; for example, to determine the factors underlying the heterogeneous behavioral responses to COVID-19 restrictions imposed by local governments. Better information about such responses is critical to our ability to understand the societal and economic impacts of the COVID-19 pandemic and possible future pandemics. The development of this data infrastructure can also help researchers explore new frontiers in behavioral science. This article explains how this approach fills gaps in COVID-19 related data collection; describes the study design and data collection procedures; presents key demographic characteristics of study participants; and shows how fusing different data streams helps uncover behavioral insights often difficult to reveal from either data streams individually.

COVID-19 大流行性质的变化凸显了全面考虑其影响并考虑随时间变化的重要性。大多数与 COVID-19 相关的研究只解决狭隘的研究问题，因此在解决大流行的相互关联影响所造成的复杂性方面受到限制。此类研究通常仅使用（1）主动收集的数据（例如调查）或（2）从移动电话或金融交易等来源被动收集的数据之一。到目前为止，只有另一项研究同时收集主动和被动数据，并且是纵向收集的。在这里，我们描述了 2020 年 8 月至 2022 年 9 月期间收集的美国居民主动和被动数据的丰富面板数据集。主动数据包括衡量旅行行为、遵守 COVID-19 规定和限制、身体健康、经济福祉、疫苗接种情况以及其他因素。被动收集的数据包括兴趣点 (POI) 签入数据，指示研究参与者访问过的所有位置。我们还在整个研究期间密切跟踪研究参与者居住县的 COVID-19 政策。纵向主动和被动数据的结合有助于克服主动或被动数据单独使用时的局限性以及横截面数据集带来的局限性，并可以回答重要的研究问题；例如，确定对地方政府施加的 COVID-19 限制的异质行为反应背后的因素。更好地了解此类应对措施对于我们了解 COVID-19 大流行病以及未来可能发生的大流行病的社会和经济影响的能力至关重要。这种数据基础设施的发展还可以帮助研究人员探索行为科学的新领域。本文解释了这种方法如何填补 COVID-19 相关数据收集的空白；描述研究设计和数据收集程序；呈现研究参与者的主要人口统计特征；并展示了融合不同的数据流如何帮助揭示通常难以从单独的数据流中揭示的行为见解。