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Review
. 2022 Aug 8:2022:9830476.
doi: 10.34133/2022/9830476. eCollection 2022.

Mobile Sensing in the COVID-19 Era: A Review

Zhiyuan Wang  1 , Haoyi Xiong  2 , Mingyue Tang  1 , Mehdi Boukhechba  1 , Tabor E Flickinger  3 , Laura E Barnes  1
Affiliations
Review

Mobile Sensing in the COVID-19 Era: A Review

Zhiyuan Wang et al. Health Data Sci. .

Abstract

Background: 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.

Methods: 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 time duration and population scale under mobile sensing studies.

Results: We found that existing literature can be naturally grouped in four clusters, including remote detection, long-term tracking, contact tracing, and epidemiological study. 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.

Conclusion: 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.

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Conflict of interest statement

The authors declare no conflict of interest regarding this article.

Figures

Figure 1
Figure 1
Study selection process under PRISMA framework.
Figure 2
Figure 2
Mapping literature to a two-dimensional taxonomy.
See this image and copyright information in PMC

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