Digital contact tracing has been deployed as a public health intervention to help suppress the spread of COVID-19 in many jurisdictions. However, most governments have struggled with low uptake and participation rates, limiting the effectiveness of the tool. This paper characterises a number of systems developed around the world, comparing the uptake rates for systems with different technology, data architectures, and mandates. The paper then introduces the MAST framework (motivation, access, skills, and trust), adapted from the digital inclusion literature, to explore the drivers and barriers that influence people's decisions to participate or not in digital contact tracing systems. Finally, the paper discusses some suggestions for policymakers on how to influence those drivers and barriers in order to improve uptake rates. Examples from existing digital contact tracing systems are presented throughout, although more empirical experimentation is required to support more concrete conclusions on what works.

翻译：数字联系追踪被作为一种公共卫生干预措施,用于帮助制止许多管辖区内COVID-19的传播,然而,大多数国家政府都努力克服较低的吸收率和参与率,限制了工具的有效性。本文描述了世界各地开发的一些系统,比较了不同技术、数据结构和任务系统的吸收率。随后,本文件介绍了从数字包容文献中改编的MAST框架(动力、获取、技能和信任),以探讨影响人们参与或不参与数字联系追踪系统决定的驱动因素和障碍。最后,本文件讨论了决策者如何影响这些驱动因素和障碍以提高吸收率的一些建议。从现有的数字联系追踪系统中可以提供各种实例,但还需要更多的实验性实验,以支持就什么可行作出更具体的结论。