Wang Zhiyuan, Xiong Haoyi, Tang Mingyue, Boukhechba Mehdi, Flickinger Tabor E, Barnes Laura E
School of Engineering and Applied Science, University of Virginia, Charlottesville, USA.
Big Data Lab, Baidu Research, Baidu Inc., Beijing, China.
Health Data Sci. 2022 Aug 8;2022:9830476. doi: 10.34133/2022/9830476. eCollection 2022.
During the COVID-19 pandemic, mobile sensing and data analytics techniques have demonstrated their capabilities in monitoring the trajectories of the pandemic, by collecting behavioral, physiological, and mobility data on individual, neighborhood, city, and national scales. Notably, mobile sensing has become a promising way to detect individuals' infectious status, track the change in long-term health, trace the epidemics in communities, and monitor the evolution of viruses and subspecies.
We followed the PRISMA practice and reviewed 60 eligible papers on mobile sensing for monitoring COVID-19. We proposed a taxonomy system to summarize literature by the and under mobile sensing studies.
We found that existing literature can be naturally grouped in , including , , , and . We summarized each group and analyzed representative works with regard to the system design, health outcomes, and limitations on techniques and societal factors. We further discussed the implications and future directions of mobile sensing in communicable diseases from the perspectives of technology and applications.
Mobile sensing techniques are effective, efficient, and flexible to surveil COVID-19 in scales of time and populations. In the post-COVID era, technical and societal issues in mobile sensing are expected to be addressed to improve healthcare and social outcomes.
在新冠疫情期间，移动传感和数据分析技术通过收集个人、社区、城市和国家层面的行为、生理和移动性数据，展现了其在监测疫情动态方面的能力。值得注意的是，移动传感已成为检测个体感染状况、追踪长期健康变化、追踪社区疫情以及监测病毒和亚种演变的一种有前景的方式。
我们遵循PRISMA规范，回顾了60篇关于利用移动传感监测新冠疫情的合格论文。我们提出了一个分类系统，以便按移动传感研究中的[具体内容1]和[具体内容2]对文献进行总结。
我们发现现有文献可自然地分为[具体类别1]，包括[子类别1]、[子类别2]、[子类别3]和[子类别4]。我们总结了每个类别，并分析了在系统设计、健康结果以及技术和社会因素方面的局限性等方面的代表性作品。我们还从技术和应用的角度进一步讨论了移动传感在传染病方面的意义和未来方向。
移动传感技术在时间和人群规模上对新冠疫情进行监测是有效、高效且灵活的。在新冠后时代，预计将解决移动传感中的技术和社会问题，以改善医疗保健和社会成果。
