Berlin Institute of Health, Berlin, Germany.
Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
JMIR Mhealth Uhealth. 2020 Nov 10;8(11):e22594. doi: 10.2196/22594.
The novel coronavirus SARS-CoV-2 rapidly spread around the world, causing the disease COVID-19. To contain the virus, much hope is placed on participatory surveillance using mobile apps, such as automated digital contact tracing, but broad adoption is an important prerequisite for associated interventions to be effective. Data protection aspects are a critical factor for adoption, and privacy risks of solutions developed often need to be balanced against their functionalities. This is reflected by an intensive discussion in the public and the scientific community about privacy-preserving approaches.
Our aim is to inform the current discussions and to support the development of solutions providing an optimal balance between privacy protection and pandemic control. To this end, we present a systematic analysis of existing literature on citizen-centered surveillance solutions collecting individual-level spatial data. Our main hypothesis is that there are dependencies between the following dimensions: the use cases supported, the technology used to collect spatial data, the specific diseases focused on, and data protection measures implemented.
We searched PubMed and IEEE Xplore with a search string combining terms from the area of infectious disease management with terms describing spatial surveillance technologies to identify studies published between 2010 and 2020. After a two-step eligibility assessment process, 27 articles were selected for the final analysis. We collected data on the four dimensions described as well as metadata, which we then analyzed by calculating univariate and bivariate frequency distributions.
We identified four different use cases, which focused on individual surveillance and public health (most common: digital contact tracing). We found that the solutions described were highly specialized, with 89% (24/27) of the articles covering one use case only. Moreover, we identified eight different technologies used for collecting spatial data (most common: GPS receivers) and five different diseases covered (most common: COVID-19). Finally, we also identified six different data protection measures (most common: pseudonymization). As hypothesized, we identified relationships between the dimensions. We found that for highly infectious diseases such as COVID-19 the most common use case was contact tracing, typically based on Bluetooth technology. For managing vector-borne diseases, use cases require absolute positions, which are typically measured using GPS. Absolute spatial locations are also important for further use cases relevant to the management of other infectious diseases.
We see a large potential for future solutions supporting multiple use cases by combining different technologies (eg, Bluetooth and GPS). For this to be successful, however, adequate privacy-protection measures must be implemented. Technologies currently used in this context can probably not offer enough protection. We, therefore, recommend that future solutions should consider the use of modern privacy-enhancing techniques (eg, from the area of secure multiparty computing and differential privacy).
新型冠状病毒 SARS-CoV-2 在全球迅速传播，引发了 COVID-19 疾病。为了控制病毒，人们寄希望于利用移动应用进行参与式监测，例如自动数字接触追踪，但广泛采用是相关干预措施有效实施的一个重要前提。数据保护方面是采用的一个关键因素，开发的解决方案的隐私风险通常需要与它们的功能相平衡。这反映在公众和科学界对隐私保护方法的激烈讨论中。
我们旨在为当前的讨论提供信息，并支持开发在保护隐私和控制大流行之间提供最佳平衡的解决方案。为此，我们对收集个人层面空间数据的以公民为中心的监测解决方案的现有文献进行了系统分析。我们的主要假设是，以下维度之间存在依赖性：支持的用例、用于收集空间数据的技术、关注的特定疾病以及实施的数据保护措施。
我们在 PubMed 和 IEEE Xplore 上使用了一个搜索字符串，该搜索字符串结合了传染病管理领域的术语和描述空间监测技术的术语，以确定 2010 年至 2020 年期间发表的研究。经过两步资格评估过程，选择了 27 篇文章进行最终分析。我们收集了描述的四个维度的数据以及元数据，然后通过计算单变量和双变量频率分布来对其进行分析。
我们确定了四个不同的用例，它们侧重于个体监测和公共卫生（最常见：数字接触追踪）。我们发现，所描述的解决方案非常专业化，27 篇文章中有 89%（24/27）仅涵盖一个用例。此外，我们还确定了用于收集空间数据的八种不同技术（最常见：GPS 接收器）和五种不同疾病（最常见：COVID-19）。最后，我们还确定了六种不同的数据保护措施（最常见：假名化）。正如假设的那样，我们发现这些维度之间存在关系。我们发现，对于 COVID-19 等传染性很强的疾病，最常见的用例是接触追踪，通常基于蓝牙技术。对于管理媒介传播疾病，用例需要绝对位置，通常使用 GPS 测量。绝对空间位置对于管理其他传染病的其他用例也很重要。
我们看到未来通过结合不同技术（例如蓝牙和 GPS）支持多种用例的解决方案具有很大的潜力。然而，要实现这一点，必须实施足够的隐私保护措施。目前在这方面使用的技术可能无法提供足够的保护。因此，我们建议未来的解决方案应考虑使用现代隐私增强技术（例如来自安全多方计算和差分隐私领域的技术）。
