Evidence map›Paper›PMID 40585005›Full record

ArticleWellcome open research2025

GRAPEVNE - Graphical Analytical Pipeline Development Environment for Infectious Diseases.

John-Stuart Brittain, Joseph Tsui, Rhys Inward, Bernardo Gutierrez, Gaspary Mwanyika, Houriiyah Tegally, Tuyen Huynh, George Githinji, Sofonias Kifle Tessema, John T McCrone and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Wellcome open research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

14 authors.

John-Stuart BrittainOxford Research Software Engineering Group, University of Oxford, Oxford, England, UK.ORCID https://orcid.org/0000-0002-4172-190X
Joseph TsuiPandemic Sciences Institute, University of Oxford, Oxford, England, UK.ORCID https://orcid.org/0000-0001-7871-8627
Rhys InwardPandemic Sciences Institute, University of Oxford, Oxford, England, UK.ORCID https://orcid.org/0000-0003-0016-661X
Bernardo GutierrezPandemic Sciences Institute, University of Oxford, Oxford, England, UK.
Gaspary MwanyikaCentre for Epidemic Response and Innovation (CERI), Stellenbosch University, Stellenbosch, Western Cape, South Africa.
Houriiyah TegallyCentre for Epidemic Response and Innovation (CERI), Stellenbosch University, Stellenbosch, Western Cape, South Africa.
Tuyen HuynhOxford University Clinical Research Unit, Ho Chi Minh City, Ho Chi Minh, Vietnam.ORCID https://orcid.org/0009-0009-4878-8953
George GithinjiKEMRI-Wellcome Trust Research Programme, Kilifi, Kenya.ORCID https://orcid.org/0000-0001-9640-7371
Sofonias Kifle TessemaAfrica Centres for Disease Control and Prevention (Africa CDC), Addis Ababa, Ethiopia.
John T McCroneVaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA.
Samir BhattDepartment of Public Health, University of Copenhagen, 1352 Copenhagen, Denmark.
Abhishek DasguptaOxford Research Software Engineering Group, University of Oxford, Oxford, England, UK.
Stephen RatcliffeGoogle Inc., Mountain View, USA.
Moritz U G KraemerPandemic Sciences Institute, University of Oxford, Oxford, England, UK.ORCID https://orcid.org/0000-0001-8838-7147

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increase in volume and diversity of relevant data on infectious diseases and their drivers provides opportunities to generate new scientific insights that can support 'real-time' decision-making in public health across outbreak contexts and enhance pandemic preparedness. However, utilising the wide array of clinical, genomic, epidemiological, and spatial data collected globally is difficult due to differences in data preprocessing, data science capacity, and access to hardware and cloud resources. To facilitate large-scale and routine analyses of infectious disease data at the local level (i.e. without sharing data across borders), we developed GRAPEVNE (Graphical Analytical Pipeline Development Environment), a platform enabling the construction of modular pipelines designed for complex and repetitive data analysis workflows through an intuitive graphical interface. Built on the

Indexed as

automated workflowsdata scienceepidemiologygenomicsgraphical interfaceopen-sourceoutbreakssnakemake

Identifiers

PMID40585005
PMCPMC12205304

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.