Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada.
Department of Pediatrics, Izaak Walton Killam Health Center, Canadian Center for Vaccinology, Halifax, NS, Canada.
JMIR Public Health Surveill. 2023 Jun 22;9:e44286. doi: 10.2196/44286.
The higher movement of people was one of the variables that contributed to the spread of the infectious agent SARS-CoV-2 during the COVID-19 pandemic. Governments worldwide responded to the virus by implementing measures that would restrict people's movements, and consequently, the spread of the disease. During the onset of the pandemic, the technology companies Apple, Google, and Meta used their infrastructure to anonymously gather mobility reports from their users.
This study aims to compare mobility data reports collected by Apple, Google, and Meta (formerly Facebook) during the COVID-19 pandemic and a major winter storm in Texas in 2021. We aim to explore the hypothesis that different people exhibit similar mobility trends during dramatic events and to emphasize the importance of this type of data for public health measures. The study also aims to promote evidence for companies to continue releasing mobility trends data, given that all 3 companies have discontinued these services.
In this study, we collected mobility data spanning from 2020 to 2022 from 3 major tech companies: Apple, Google, and Meta. Our analysis focused on 58 countries that are common to all 3 databases, enabling us to conduct a comprehensive global-scale analysis. By using the winter storm that occurred in Texas in 20201 as a benchmark, we were able to assess the robustness of the mobility data obtained from the 3 companies and ensure the integrity of our findings.
Our study revealed convergence in the mobility trends observed across different companies during the onset of significant disasters, such as the first year of the COVID-19 pandemic and the winter storm that impacted Texas in 2021. Specifically, we observed strong positive correlations (r=0.96) in the mobility data collected from different tech companies during the first year of the pandemic. Furthermore, our analysis of mobility data during the 2021 winter storm in Texas showed a similar convergence of trends. Additionally, we found that periods of stay-at-home orders were reflected in the data, with record-low mobility and record-high stay-at-home figures.
Our findings provide valuable insights into the ways in which major disruptive events can impact patterns of human mobility; moreover, the convergence of data across distinct methodologies highlights the potential value of leveraging mobility data from multiple sources for informing public health decision-making. Therefore, we conclude that the use of mobility data is an asset for health authorities to consider during natural disasters, as we determined that the data sets from 3 companies yielded convergent mobility patterns. Comparatively, data obtained from a single source would be limited, and therefore, more difficult to interpret, requiring careful analysis.
人员流动量的增加是导致 SARS-CoV-2 传染性病原体在 COVID-19 大流行期间传播的因素之一。为应对病毒，全球各国政府采取了限制人员流动的措施，从而控制疾病的传播。在大流行初期，苹果、谷歌和元（原 Facebook）等科技公司利用其基础设施，匿名从用户处收集流动报告。
本研究旨在比较苹果、谷歌和元（原 Facebook）在 COVID-19 大流行期间和 2021 年德克萨斯州一场重大冬季风暴期间收集的流动数据报告。我们旨在探索这样一种假设，即在重大事件中，不同人群表现出相似的流动趋势，并强调此类数据对于公共卫生措施的重要性。该研究还旨在促使各公司继续发布流动趋势数据，因为这 3 家公司均已停止此类服务。
本研究从苹果、谷歌和元（原 Facebook）这 3 家主要科技公司收集了 2020 年至 2022 年的流动数据。我们的分析重点是 3 个数据库共有的 58 个国家，从而能够进行全面的全球范围分析。通过将 2020 年德克萨斯州的冬季风暴作为基准，我们评估了从 3 家公司获得的流动数据的稳健性，并确保了研究结果的完整性。
我们的研究表明，在 COVID-19 大流行的第一年和 2021 年德克萨斯州冬季风暴等重大灾害发生之初，不同公司的流动趋势趋同。具体而言，我们观察到在大流行的第一年，不同科技公司收集的流动数据之间存在很强的正相关（r=0.96）。此外，我们对 2021 年德克萨斯州冬季风暴期间流动数据的分析也显示出类似的趋势趋同。此外，我们发现居家令期间的数据也有所反映，移动性降至历史新低，居家率则升至历史新高。
我们的研究结果为重大干扰事件如何影响人类流动模式提供了宝贵的见解；此外，不同方法收集的数据趋同，突出了利用来自多个来源的流动数据为公共卫生决策提供信息的潜力。因此，我们得出结论，在自然灾害期间，移动数据对卫生当局而言是一项有用的资源，因为我们发现 3 家公司的数据集中存在趋同的流动模式。相比之下，从单一来源获取的数据将受到限制，因此更难以解释，需要仔细分析。
