Evidence map›Paper›PMID 35930904›Full record

ReviewEpidemics2022

The challenges of data in future pandemics.

Nigel Shadbolt, Alys Brett, Min Chen, Glenn Marion, Iain J McKendrick, Jasmina Panovska-Griffiths, Lorenzo Pellis, Richard Reeve, Ben Swallow

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Epidemics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Article
  2. Article
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  4. Observational
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  8. Review
  9. Review
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  11. Article
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  15. Tracking the national and regional COVID-19 epidemic status in the UK using weighted principal component analysis.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022
    Article
  16. Article
  17. Article
  18. 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

9 authors.

Nigel ShadboltDepartment of Computer Science, University of Oxford, UK; The Open Data Institute, London, UK. Electronic address: nigel.shadbolt@cs.ox.ac.uk.
Alys BrettUKAEA Software Engineering Group, UK; Scottish COVID-19 Response Consortium, UK.
Min ChenDepartment of Engineering Science, University of Oxford, UK; Scottish COVID-19 Response Consortium, UK.
Glenn MarionBiomathematics and Statistics Scotland, Edinburgh, UK; Scottish COVID-19 Response Consortium, UK.
Iain J McKendrickBiomathematics and Statistics Scotland, Edinburgh, UK; Scottish COVID-19 Response Consortium, UK.
Jasmina Panovska-GriffithsThe Big Data Institute, University of Oxford, UK; The Wolfson Centre for Mathematical Biology, University of Oxford, UK; The Queen's College, University of Oxford, UK.
Lorenzo PellisDepartment of Mathematics, University of Manchester, UK; The Alan Turing Institute, London, UK.
Richard ReeveScottish COVID-19 Response Consortium, UK; Institute of Biodiversity Animal Health & Comparative Medicine, University of Glasgow, UK.
Ben SwallowScottish COVID-19 Response Consortium, UK; School of Mathematics and Statistics, University of Glasgow, UK.

Funding

Medical Research Council MR/V038613/1Wellcome Trust 202562/Z/16/Z
6 · The paper itself

Abstract

The use of data has been essential throughout the unfolding COVID-19 pandemic. We have needed it to populate our models, inform our understanding, and shape our responses to the disease. However, data has not always been easy to find and access, it has varied in quality and coverage, been difficult to reuse or repurpose. This paper reviews these and other challenges and recommends steps to develop a data ecosystem better able to deal with future pandemics by better supporting preparedness, prevention, detection and response.

Indexed as

COVID-19PandemicsEcosystemForecastingHumansCOVID-19Data and modelsData ecosystemData lifecyclesFAIR dataPandemic preparedness

Identifiers

PMID35930904
PMCPMC9297658

What OpenQuestion holds

Textmetadata
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.