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Mobile Sensing in the COVID-19 Era: A Review.
Wang, Zhiyuan; Xiong, Haoyi; Tang, Mingyue; Boukhechba, Mehdi; Flickinger, Tabor E; Barnes, Laura E.
  • Wang Z; School of Engineering and Applied Science, University of Virginia, Charlottesville, USA.
  • Xiong H; Big Data Lab, Baidu Research, Baidu Inc., Beijing, China.
  • Tang M; School of Engineering and Applied Science, University of Virginia, Charlottesville, USA.
  • Boukhechba M; School of Engineering and Applied Science, University of Virginia, Charlottesville, USA.
  • Flickinger TE; Department of Medicine, University of Virginia, Charlottesville, Virginia, USA.
  • Barnes LE; School of Engineering and Applied Science, University of Virginia, Charlottesville, USA.
Health Data Sci ; 2022: 9830476, 2022.
Article in English | MEDLINE | ID: covidwho-2286297
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.

Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study Topics: Long Covid Language: English Journal: Health Data Sci Year: 2022 Document Type: Article Affiliation country: 2022

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study Topics: Long Covid Language: English Journal: Health Data Sci Year: 2022 Document Type: Article Affiliation country: 2022