The DOI matches its Crossref record; 8 of the 9 references it cites were found in a registry or an index.
COVID-19, digital privacy, and the social limits on data-focused public health responses
Open accessAbstract
The implementation of digital contact tracing applications around the world to help reduce the spread of the COVID-19 pandemic represents one of the most ambitious uses of massive-scale citizen data ever attempted. There is major divergence among nations, however, between a "privacy-first" approach which protects citizens' data at the cost of extremely limited access for public health authorities and researchers, and a "data-first" approach which stores large amounts of data which, while of immeasurable value to epidemiologists and other researchers, may significantly intrude upon citizens' privacy. The lack of a consensus on privacy protection in the contact tracing process creates risks of non-compliance or deliberate obfuscation from citizens who fear revealing private aspects of their lives - a factor greatly exacerbated by recent major scandals over online privacy and the illicit use of citizens' digital information, which have heightened public consciousness of these issues and created significant new challenges for any collection of large-scale public data. While digital contact tracing for COVID-19 remains in its infancy, the lack of consensus around best practices for its implementation and for reassuring citizens of the protection of their privacy may already have impeded its capacity to contribute to the pandemic response.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“For many people in developing countries the digital divide is most apparent due to social and cultural barriers reinforced by poverty and a lack of education amongst the population (Barnes 2020;De et al. 2020;Fahey et al. 2020).”
From Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life (2020) · cites this paper as Fahey et al. 2020
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
An interactive web-based dashboard to track COVID-19 in real time
Dong, E., Du, H., & Gardner, L. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30120-1 · Cited by 10,048
User Data Privacy: Facebook, Cambridge Analytica, and Privacy Protection
Isaak, J., & Hanna, M. J. (2018) · Computer · doi:10.1109/mc.2018.3191268 · Cited by 700
Contact Tracing Mobile Apps for COVID-19: Privacy Considerations and Related Trade-offsPreprint
Cho, H., Ippolito, D., & Yu, Y. W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11511 · Cited by 461
Análise da gravidade da pandemia de Covid-19
Freitas, A. R. R., Napimoga, M. H., & Donalísio, M. R. (2020) · Epidemiologia e Serviços de Saúde · doi:10.5123/s1679-49742020000200008 · Cited by 314
Privacy-enhancing technologies for the Internet
Goldberg, I., Wagner, D., & Brewer, E. (2002) · doi:10.1109/cmpcon.1997.584680 · Cited by 181
Apps Gone Rogue: Maintaining Personal Privacy in an EpidemicPreprint
Raskar, R., Schunemann, I., Barbar, R., Vilcans, K., Gray, J., Vepakomma, P., Kapa, S., Nuzzo, A., Gupta, R., Berke, A., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.08567 · Cited by 142
Novel Framework for Assessing Epidemiologic Effects of Influenza Epidemics and Pandemics
Reed, C., Biggerstaff, M., Finelli, L., Koonin, L. M., Beauvais, D., Uzicanin, A., Plummer, A., Bresee, J., Redd, S. C., & Jernigan, D. B. (2012) · Emerging infectious diseases · doi:10.3201/eid1901.120124 · Cited by 130
Portrait of a year-old pandemic
Butler, D. (2010) · Nature · doi:10.1038/4641112a · Cited by 42
Cited by
Sorted by citations
Dwivedi, Y. K., Hughes, D. L., Coombs, C., Constantiou, I., Duan, Y., Edwards, J. S., Gupta, B., Lal, B., Misra, S., Prashant, P., et al. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102211 · Cited by 1,232
Information technology solutions, challenges, and suggestions for tackling the COVID-19 pandemic
He, W., Zhang, Z., & Li, W. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102287 · Cited by 482
Adoption of a Contact Tracing App for Containing COVID-19: A Health Belief Model Approach
Walrave, M., Waeterloos, C., & Ponnet, K. S. (2020) · JMIR Public Health and Surveillance · doi:10.2196/20572 · Cited by 268
Lal, B., Dwivedi, Y. K., & Haag, M. (2021) · Information Systems Frontiers · doi:10.1007/s10796-021-10182-0 · Cited by 215
Li, L., Tong, Y., Wei, L., & Yang, S. (2022) · Information & Management · doi:10.1016/j.im.2022.103689 · Cited by 190
Metadata
- Authors
- Fahey, R. A., & Hino, A.
- Affiliations
- Waseda University (JP)
- Corresponding author
- Hino, Airo
- Venue
- International Journal of Information Management
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 55
- Pages
- 102181
- ISSN
- 0268-4012 (ISSN-L)
- PMC
- PMC7328565
- OpenAlex
- W3038461398
- Cited by
- 187
- References
- 9
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 22 · 2023: 32 · 2022: 50 · 2021: 55 · 2020: 16
- FWCI
- 36.02
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- International Journal of Information Management (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Japan Society for the Promotion of Science (20KK0026, 18H03642, 18K01423, 20H00066, 17H00971)
- Topics
- Physical Sciences › Computer Science › Information Systems › COVID-19 Digital Contact Tracing
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- COVID-19 pandemic, digital contact tracing, digital privacy, public health data, research data access, privacy compliance, pandemic response
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
