Evidence map›Paper›PMID 35633868›Full record

ArticleBiomedical engineering and computational biology2022

A Scoping Review of Digital Twins in the Context of the Covid-19 Pandemic.

Asiya Khan, Madison Milne-Ives, Edward Meinert, Gloria E Iyawa, Ray B Jones, Alex N Josephraj

Abstract readScoping Review
In one paragraph

Article in Biomedical engineering and computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 2 pooled it
–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

9 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Digital Twins and Health Care: an Umbrella Review.Journal of medical systems · 2025
    Pooled it
  2. Pooled it
  3. Review
  4. Article
  5. Review
  6. Article
  7. Review
  8. What's next for computational systems biology?Frontiers in systems biology · 2023
    Review
  9. Review
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

6 authors.

Asiya KhanSchool of Engineering, Computing and Mathematics, University of Plymouth, Plymouth, UK.ORCID https://orcid.org/0000-0003-3620-3048
Madison Milne-IvesCentre for Health Technology, University of Plymouth, Plymouth, UK.ORCID https://orcid.org/0000-0001-7628-882X
Edward MeinertCentre for Health Technology, University of Plymouth, Plymouth, UK.
Gloria E IyawaDepartment of Computing, Sheffield Hallam University, Sheffield, UK.
Ray B JonesCentre for Health Technology, University of Plymouth, Plymouth, UK.
Alex N JosephrajKey Laboratory of Digital Signal and Image Processing of Guandong Province, Shantou University, Shantou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Digital Twins (DTs), virtual copies of physical entities, are a promising tool to help manage and predict outbreaks of Covid-19. By providing a detailed model of each patient, DTs can be used to determine what method of care will be most effective for that individual. The improvement in patient experience and care delivery will help to reduce demand on healthcare services and to improve hospital management. Objectives: The aim of this study is to address 2 research questions: (1) How effective are DTs in predicting and managing infectious diseases such as Covid-19? and (2) What are the prospects and challenges associated with the use of DTs in healthcare? Methods: The review was structured according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) framework. Titles and abstracts of references in PubMed, IEEE Xplore, Scopus, ScienceDirect and Google Scholar were searched using selected keywords (relating to digital twins, healthcare and Covid-19). The papers were screened in accordance with the inclusion and exclusion criteria so that all papers published in English relating to the use of digital twins in healthcare were included. A narrative synthesis was used to analyse the included papers. Results: Eighteen papers met the inclusion criteria and were included in the review. None of the included papers examined the use of DTs in the context of Covid-19, or infectious disease outbreaks in general. Academic research about the applications, opportunities and challenges of DT technology in healthcare in general was found to be in early stages. Conclusions: The review identifies a need for further research into the use of DTs in healthcare, particularly in the context of infectious disease outbreaks. Based on frameworks identified during the review, this paper presents a preliminary conceptual framework for the use of DTs for hospital management during the Covid-19 outbreak to address this research gap.

Indexed as

Covid-19Digital Twinsdisease outbreakshealthcarepublic health

Identifiers

PMID35633868
PMCPMC9136438

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.