Analyzing the Essential Attributes of Nationally Issued COVID-19 Contact Tracing Apps: Open-Source Intelligence Approach and Content Analysis
- PMID: 33724920
- PMCID: PMC8006898
- DOI: 10.2196/27232
Analyzing the Essential Attributes of Nationally Issued COVID-19 Contact Tracing Apps: Open-Source Intelligence Approach and Content Analysis
Abstract
Background: Contact tracing apps are potentially useful tools for supporting national COVID-19 containment strategies. Various national apps with different technical design features have been commissioned and issued by governments worldwide.
Objective: Our goal was to develop and propose an item set that was suitable for describing and monitoring nationally issued COVID-19 contact tracing apps. This item set could provide a framework for describing the key technical features of such apps and monitoring their use based on widely available information.
Methods: We used an open-source intelligence approach (OSINT) to access a multitude of publicly available sources and collect data and information regarding the development and use of contact tracing apps in different countries over several months (from June 2020 to January 2021). The collected documents were then iteratively analyzed via content analysis methods. During this process, an initial set of subject areas were refined into categories for evaluation (ie, coherent topics), which were then examined for individual features. These features were paraphrased as items in the form of questions and applied to information materials from a sample of countries (ie, Brazil, China, Finland, France, Germany, Italy, Singapore, South Korea, Spain, and the United Kingdom [England and Wales]). This sample was purposefully selected; our intention was to include the apps of different countries from around the world and to propose a valid item set that can be relatively easily applied by using an OSINT approach.
Results: Our OSINT approach and subsequent analysis of the collected documents resulted in the definition of the following five main categories and associated subcategories: (1) background information (open-source code, public information, and collaborators); (2) purpose and workflow (secondary data use and warning process design); (3) technical information (protocol, tracing technology, exposure notification system, and interoperability); (4) privacy protection (the entity of trust and anonymity); and (5) availability and use (release date and the number of downloads). Based on this structure, a set of items that constituted the evaluation framework were specified. The application of these items to the 10 selected countries revealed differences, especially with regard to the centralization of the entity of trust and the overall transparency of the apps' technical makeup.
Conclusions: We provide a set of criteria for monitoring and evaluating COVID-19 tracing apps that can be easily applied to publicly issued information. The application of these criteria might help governments to identify design features that promote the successful, widespread adoption of COVID-19 tracing apps among target populations and across national boundaries.
Keywords: COVID-19; app; assessment; contact tracing; design; feature; framework; monitoring; privacy; protocol; review; surveillance; usage.
©Jan-Patrick Weiß, Moritz Esdar, Ursula Hübner. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 26.03.2021.
Conflict of interest statement
Conflicts of Interest: None declared.
Similar articles
-
Technology, Privacy, and User Opinions of COVID-19 Mobile Apps for Contact Tracing: Systematic Search and Content Analysis.J Med Internet Res. 2021 Feb 9;23(2):e23467. doi: 10.2196/23467. J Med Internet Res. 2021. PMID: 33493125 Free PMC article.
-
Health Apps for Combating COVID-19: Descriptive Review and Taxonomy.JMIR Mhealth Uhealth. 2021 Mar 2;9(3):e24322. doi: 10.2196/24322. JMIR Mhealth Uhealth. 2021. PMID: 33626017 Free PMC article. Review.
-
Data Management and Privacy Policy of COVID-19 Contact-Tracing Apps: Systematic Review and Content Analysis.JMIR Mhealth Uhealth. 2022 Jul 12;10(7):e35195. doi: 10.2196/35195. JMIR Mhealth Uhealth. 2022. PMID: 35709334 Free PMC article.
-
Early Perceptions of COVID-19 Contact Tracing Apps in German-Speaking Countries: Comparative Mixed Methods Study.J Med Internet Res. 2021 Feb 8;23(2):e25525. doi: 10.2196/25525. J Med Internet Res. 2021. PMID: 33503000 Free PMC article.
-
The Roles of General Health and COVID-19 Proximity in Contact Tracing App Usage: Cross-sectional Survey Study.JMIR Public Health Surveill. 2021 Aug 18;7(8):e27892. doi: 10.2196/27892. JMIR Public Health Surveill. 2021. PMID: 34081602 Free PMC article.
Cited by
-
Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review.JMIR Public Health Surveill. 2024 Jan 19;10:e49185. doi: 10.2196/49185. JMIR Public Health Surveill. 2024. PMID: 38241067 Free PMC article.
-
Reasons for Nonuse, Discontinuation of Use, and Acceptance of Additional Functionalities of a COVID-19 Contact Tracing App: Cross-sectional Survey Study.JMIR Public Health Surveill. 2022 Jan 14;8(1):e22113. doi: 10.2196/22113. JMIR Public Health Surveill. 2022. PMID: 34794117 Free PMC article.
-
Digital Contact Tracing Apps for COVID-19: Development of a Citizen-Centered Evaluation Framework.JMIR Mhealth Uhealth. 2022 Mar 11;10(3):e30691. doi: 10.2196/30691. JMIR Mhealth Uhealth. 2022. PMID: 35084338 Free PMC article. Review.
-
Identifying and addressing digital health risks associated with emergency pandemic response: Problem identification, scoping review, and directions toward evidence-based evaluation.Int J Med Inform. 2022 Jan;157:104639. doi: 10.1016/j.ijmedinf.2021.104639. Epub 2021 Nov 6. Int J Med Inform. 2022. PMID: 34768031 Free PMC article.
-
A machine learning algorithm to analyse the effects of vaccination on COVID-19 mortality.Epidemiol Infect. 2022 Sep 12;150:e168. doi: 10.1017/S0950268822001418. Epidemiol Infect. 2022. PMID: 36093862 Free PMC article.
References
-
- Cen Y, Chen X, Shen Y, Zhang XH, Lei Y, Xu C, Jiang WR, Xu HT, Chen Y, Zhu J, Zhang LL, Liu YH. Risk factors for disease progression in patients with mild to moderate coronavirus disease 2019-a multi-centre observational study. Clin Microbiol Infect. 2020 Sep;26(9):1242–1247. doi: 10.1016/j.cmi.2020.05.041. http://europepmc.org/abstract/MED/32526275 - DOI - PMC - PubMed
-
- Ferretti L, Wymant C, Kendall M, Zhao L, Nurtay A, Abeler-Dörner L, Parker M, Bonsall D, Fraser C. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science. 2020 May 08;368(6491):eabb6936. doi: 10.1126/science.abb6936. http://europepmc.org/abstract/MED/32234805 - DOI - PMC - PubMed
-
- Hinch R, Probert W, Nurtay A, Kendall M, Wymant C, Hall M, Lythgoe K, Cruz AB, Zhao L, Stewart A, Ferretti L, Parker M, Montero D, Warren J, Mather NK, Finkelstein A, Abeler-Dörner L, Bonsall D, Fraser C. Effective configurations of a digital contact tracing app: A report to NHSX. en. GitHub. 2020. [2021-03-22]. https://tinyurl.com/h6ctnxtx.
-
- Trang S, Trenz M, Weiger WH, Tarafdar M, Cheung CMK. One app to trace them all? Examining app specifications for mass acceptance of contact-tracing apps. Eur J Inf Syst. 2020 Jul 27;29(4):415–428. doi: 10.1080/0960085x.2020.1784046. https://www.tandfonline.com/doi/full/10.1080/0960085X.2020.1784046 - DOI - DOI
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources
Medical