Digital Contact Tracing Applications against COVID-19: A Systematic Review
- PMID: 36030770
- PMCID: PMC9801373
- DOI: 10.1159/000526672
Digital Contact Tracing Applications against COVID-19: A Systematic Review
Abstract
Objective: The novel coronavirus 2019 (COVID-19) pandemic has triggered public anxiety around the world. So far, the evidence suggests that prevention on a public scale is the most effective health measure for thwarting the progress of COVID-19. Another critical aspect of preventing COVID-19 is contact tracing. We aimed to investigate the effectiveness of digital contact tracing applications currently available in the context of the COVID-19 pandemic.
Methods: We undertook a systematic review and narrative synthesis of all literature relating to digital contact tracing applications in the context of COVID-19. We searched 3 major scientific databases. Only articles that were published in English and were available as full-text articles were selected for review. Data were extracted and narrative syntheses conducted.
Results: Five studies relating to COVID-19 were included in the review. Our results suggest that digitalized contact tracing methods can be beneficial for impeding the progress of COVID-19. Three key themes were generated from this systematic review. First, the critical mass of adoption of applications must be attained at the population level before the sensitivity and positive predictive value of the solution can be increased. Second, usability factors such as access, ease of use, and the elimination of barriers are essential in driving this uptake. Third, privacy must be ensured where possible as it is the single most significant barrier against achieving critical mass.
Conclusion: Contact tracing methods have proved to be beneficial for impeding the progress of COVID-19 as compared to older, more labour-intensive manual methods.
Keywords: COVID-19; Contact tracing; Pandemic.
© 2022 The Author(s). Published by S. Karger AG, Basel.
Conflict of interest statement
The authors declare that they have no conflict of interest.
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References
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- John Hopkins University COVID-19 dashboard. 2021. Available from: https://coronavirus.jhu.edu/map.html.
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- Altuwaiyan T, Hadian M, Liang X, editors. 2018 IEEE International Conference on Communications (ICC) 20–24 May 2018. 2018. EPIC: efficient privacy-preserving contact tracing for infection detection.
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