A Survey of COVID-19 Contact Tracing Apps
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Abstract (arXiv)
The recent outbreak of COVID-19 has taken the world by surprise, forcing lockdowns and straining public health care systems. COVID-19 is known to be a highly infectious virus, and infected individuals do not initially exhibit symptoms, while some remain asymptomatic. Thus, a non-negligible fraction of the population can, at any given time, be a hidden source of transmissions. In response, many governments have shown great interest in smartphone contact tracing apps that help automate the difficult task of tracing all recent contacts of newly identified infected individuals. However, tracing apps have generated much discussion around their key attributes, including system architecture, data management, privacy, security, proximity estimation, and attack vulnerability. In this article, we provide the first comprehensive review of these much-discussed tracing app attributes. We also present an overview of many proposed tracing app examples, some of which have been deployed countrywide, and discuss the concerns users have reported regarding their usage. We close by outlining potential research directions for next-generation app design, which would facilitate improved tracing and security performance, as well as wide adoption by the population at large.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jun 2020.
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Citing papers per year: 2021: 5; 2022: 2; 2023: 1.
8 papers.
- 2022Data-Centric Epidemic Forecasting: A Surveycited by 8
- 2023Protect Your Score: Contact Tracing With Differential Privacy Guaranteescited by 5
- 2022Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learningcited by 4
- 2021Change of human mobility during COVID-19: A United States case studycited by 3
- 2021Machine learning applications for COVID-19: A state-of-the-art reviewcited by 2
- 2021Sentiment Analysis of Users' Reviews on COVID-19 Contact Tracing Apps with a Benchmark Datasetcited by 2
- 2021An Overview of Ontologies and Tool Support for COVID-19 Analyticscited by 1
- 2021Internet of Everything enabled solution for COVID-19, its new variants and future pandemics: Framework, Challenges, and Research Directionscited by 0
- 2023Protect Your Score: Contact Tracing With Differential Privacy Guarantees18 Dec 2023
- 2022Data-Centric Epidemic Forecasting: A Survey19 Jul 2022
- 2022Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learning24 May 2022
- 2021An Overview of Ontologies and Tool Support for COVID-19 Analytics12 Oct 2021
- 2021Change of human mobility during COVID-19: A United States case study18 Sep 2021
- 2021Sentiment Analysis of Users' Reviews on COVID-19 Contact Tracing Apps with a Benchmark Dataset1 Mar 2021
- 2021Machine learning applications for COVID-19: A state-of-the-art review19 Jan 2021
- 2021Internet of Everything enabled solution for COVID-19, its new variants and future pandemics: Framework, Challenges, and Research Directions2 Jan 2021