Evidence map›Paper›PMID 33727212›Full record

ArticleJMIR public health and surveillance2021

Test, Trace, and Put on the Blockchain?: A Viewpoint Evaluating the Use of Decentralized Systems for Algorithmic Contact Tracing to Combat a Global Pandemic.

Moritz Platt, Anton Hasselgren, Juan Manuel Román-Belmonte, Marcela Tuler de Oliveira, Hortensia De la Corte-Rodríguez, Sílvia Delgado Olabarriaga, E Carlos Rodríguez-Merchán, Tim Ken Mackey

Abstract read
In one paragraph

Article in JMIR public health and surveillance, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Integrating blockchain technology with artificial intelligence for the diagnosis of tibial plateau fractures.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2025
    Article
  2. Review
  3. Review
  4. Appraisal of a Contact Tracing Training Program for COVID-19 in Greece Focusing on Vulnerable Populations.International journal of environmental research and public health · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Moritz PlattDepartment of Informatics, King's College London, London, United Kingdom.ORCID 0000-0003-2137-4370
Anton HasselgrenDepartment of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.ORCID 0000-0002-6245-6041
Juan Manuel Román-BelmonteDepartment of Physical Medicine and Rehabilitation, Hospital Central de la Cruz Roja San José y Santa Adela, Madrid, Spain.ORCID 0000-0003-2693-1439
Marcela Tuler de OliveiraDepartment of Epidemiology and Data Science, Amsterdam UMC, Amsterdam, Netherlands.ORCID 0000-0001-5739-5590
Hortensia De la Corte-RodríguezDepartment of Physical Medicine and Rehabilitation, La Paz University Hospital, Madrid, Spain.ORCID 0000-0002-9179-201X
Sílvia Delgado OlabarriagaDepartment of Epidemiology and Data Science, Amsterdam UMC, Amsterdam, Netherlands.ORCID 0000-0002-5935-4482
E Carlos Rodríguez-MerchánDepartment of Orthopaedic Surgery, La Paz University Hospital, Madrid, Spain.ORCID 0000-0002-6360-0113
Tim Ken MackeyDepartment of Anesthesiology, Division of Infectious Diseases and Global Public Health, School of Medicine, UC San Diego, La Jolla, CA, United States.ORCID 0000-0002-2191-7833

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The enormous pressure of the increasing case numbers experienced during the COVID-19 pandemic has given rise to a variety of novel digital systems designed to provide solutions to unprecedented challenges in public health. The field of algorithmic contact tracing, in particular, an area of research that had previously received limited attention, has moved into the spotlight as a crucial factor in containing the pandemic. The use of digital tools to enable more robust and expedited contact tracing and notification, while maintaining privacy and trust in the data generated, is viewed as key to identifying chains of transmission and close contacts, and, consequently, to enabling effective case investigations. Scaling these tools has never been more critical, as global case numbers have exceeded 100 million, as many asymptomatic patients remain undetected, and as COVID-19 variants begin to emerge around the world. In this context, there is increasing attention on blockchain technology as a part of systems for enhanced digital algorithmic contact tracing and reporting. By analyzing the literature that has emerged from this trend, the common characteristics of the designs proposed become apparent. An archetypal system architecture can be derived, taking these characteristics into consideration. However, assessing the utility of this architecture using a recognized evaluation framework shows that the added benefits and features of blockchain technology do not provide significant advantages over conventional centralized systems for algorithmic contact tracing and reporting. From our study, it, therefore, seems that blockchain technology may provide a more significant benefit in other areas of public health beyond contact tracing.

Indexed as

AlgorithmsBlockchainContact TracingCoronavirus InfectionsPrivacyCOVID-19FemaleHumansMalePublic Healthblockchaincontact tracingCOVID-19distributed ledger technologyepidemiological monitoringmobile appspandemic mitigationpublic health

Identifiers

PMID33727212
PMCPMC8108567

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.