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Review

Contact Tracing Strategies for COVID-19 Prevention and Containment: A Scoping Review

by
Bolanle Adefowoke Ojokoh
1,
Benjamin Aribisala
2,
Oluwafemi A. Sarumi
3,4,*,
Arome Junior Gabriel
5,
Olatunji Omisore
6,
Abiola Ezekiel Taiwo
7,
Tobore Igbe
8,
Uchechukwu Madukaku Chukwuocha
9,
Tunde Yusuf
10,
Abimbola Afolayan
1,
Olusola Babalola
4,
Tolulope Adebayo
4 and
Olaitan Afolabi
4
1
Department of Information Systems, Federal University of Technology, Akure PMB 704, Ondo State, Nigeria
2
Department of Computer Science, Lagos State University, Ojo PMB 0001, Lagos State, Nigeria
3
Department of Mathematics and Computer Science, University of Marburg, 35037 Marburg, Germany
4
Department of Computer Science, Federal University of Technology, Akure PMB 704, Ondo State, Nigeria
5
Department of Cyber Security, Federal University of Technology, Akure PMB 704, Ondo State, Nigeria
6
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
7
Faculty of Engineering, Mangosuthu University of Technology, Durban 4026, South Africa
8
Center for Diabetes Technology, Department of Psychiatry & Neurobehavioral Sciences, University of Virginia, Charlottesville, VA 22903, USA
9
Department of Public Health, Federal University of Technology, Owerri PMB 1526, Imo State, Nigeria
10
Department of Mathematical Sciences, Federal University of Technology, Akure PMB 704, Ondo State, Nigeria
*
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2022, 6(4), 111; https://doi.org/10.3390/bdcc6040111
Submission received: 22 August 2022 / Revised: 14 September 2022 / Accepted: 26 September 2022 / Published: 9 October 2022
(This article belongs to the Collection Machine Learning and Artificial Intelligence for Health Applications on Social Networks)
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Abstract

Coronavirus Disease 2019 (COVID-19) spreads rapidly and is easily contracted by individuals who come near infected persons. With this nature and rapid spread of the contagion, different types of research have been conducted to investigate how non-pharmaceutical interventions can be employed to contain and prevent COVID-19. In this review, we analyzed the key elements of digital contact tracing strategies developed for the prevention and containment of the dreaded epidemic since its outbreak. We carried out a scoping review through relevant studies indexed in three databases, namely Google Scholar, PubMed, and ACM Digital Library. Using some carefully defined search terms, a total of 768 articles were identified. The review shows that 86.32% (n = 101) of the works focusing on contact tracing were published in 2020, suggesting there was an increased awareness that year, increased research efforts, and the fact that the pandemic was given a very high priority by most journals. We observed that many (47.86%, n = 56) of the studies were focused on design and implementation issues in the development of COVID-19 contact tracing systems. In addition, has been established that most of the studies were conducted in 41 countries and that contract tracing app development are characterized by some sensitive issues, including privacy-preserving and case-based referral characteristics.
Keywords: contact tracing; COVID-19; design considerations; data ethics; user privacy contact tracing; COVID-19; design considerations; data ethics; user privacy

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MDPI and ACS Style

Ojokoh, B.A.; Aribisala, B.; Sarumi, O.A.; Gabriel, A.J.; Omisore, O.; Taiwo, A.E.; Igbe, T.; Chukwuocha, U.M.; Yusuf, T.; Afolayan, A.; et al. Contact Tracing Strategies for COVID-19 Prevention and Containment: A Scoping Review. Big Data Cogn. Comput. 2022, 6, 111. https://doi.org/10.3390/bdcc6040111

AMA Style

Ojokoh BA, Aribisala B, Sarumi OA, Gabriel AJ, Omisore O, Taiwo AE, Igbe T, Chukwuocha UM, Yusuf T, Afolayan A, et al. Contact Tracing Strategies for COVID-19 Prevention and Containment: A Scoping Review. Big Data and Cognitive Computing. 2022; 6(4):111. https://doi.org/10.3390/bdcc6040111

Chicago/Turabian Style

Ojokoh, Bolanle Adefowoke, Benjamin Aribisala, Oluwafemi A. Sarumi, Arome Junior Gabriel, Olatunji Omisore, Abiola Ezekiel Taiwo, Tobore Igbe, Uchechukwu Madukaku Chukwuocha, Tunde Yusuf, Abimbola Afolayan, and et al. 2022. "Contact Tracing Strategies for COVID-19 Prevention and Containment: A Scoping Review" Big Data and Cognitive Computing 6, no. 4: 111. https://doi.org/10.3390/bdcc6040111

APA Style

Ojokoh, B. A., Aribisala, B., Sarumi, O. A., Gabriel, A. J., Omisore, O., Taiwo, A. E., Igbe, T., Chukwuocha, U. M., Yusuf, T., Afolayan, A., Babalola, O., Adebayo, T., & Afolabi, O. (2022). Contact Tracing Strategies for COVID-19 Prevention and Containment: A Scoping Review. Big Data and Cognitive Computing, 6(4), 111. https://doi.org/10.3390/bdcc6040111

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