Evidence map›Paper›PMID 34295529›Full record

ArticleRoyal Society open science2021

June: open-source individual-based epidemiology simulation.

Joseph Aylett-Bullock, Carolina Cuesta-Lazaro, Arnau Quera-Bofarull, Miguel Icaza-Lizaola, Aidan Sedgewick, Henry Truong, Aoife Curran, Edward Elliott, Tristan Caulfield, Kevin Fong and 4 more

Abstract read
In one paragraph

Article in Royal Society open science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 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

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. REPLAY: a GPU-accelerated tool for temporal contact network epidemiology.BMC medical informatics and decision making · 2025
    Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Bayesian emulation and history matching of JUNE.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022
    Article
  13. Estimating SARS-CoV-2 variant fitness and the impact of interventions in England using statistical and geo-spatial agent-based models.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022
    Article
  14. Article
  15. Article
  16. 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

14 authors.

Joseph Aylett-BullockInstitute for Data Science, Durham University, Durham DH1 3LE, UK.ORCID 0000-0001-7551-3423
Carolina Cuesta-LazaroInstitute for Data Science, Durham University, Durham DH1 3LE, UK.
Arnau Quera-BofarullInstitute for Data Science, Durham University, Durham DH1 3LE, UK.ORCID 0000-0001-5055-9863
Miguel Icaza-LizaolaInstitute for Data Science, Durham University, Durham DH1 3LE, UK.
Aidan SedgewickInstitute for Data Science, Durham University, Durham DH1 3LE, UK.ORCID 0000-0002-9158-750X
Henry TruongInstitute for Data Science, Durham University, Durham DH1 3LE, UK.ORCID 0000-0002-0105-1278
Aoife CurranInstitute for Data Science, Durham University, Durham DH1 3LE, UK.
Edward ElliottInstitute for Data Science, Durham University, Durham DH1 3LE, UK.
Tristan CaulfieldDepartment of Computer Science, University College London, London WC1E 6BT, UK.
Kevin FongDepartment of Science, Technology, Engineering and Public Policy, University College London, London WC1E 6BT, UK.
Ian VernonInstitute for Data Science, Durham University, Durham DH1 3LE, UK.
Julian WilliamsInstitute of Hazard, Risk and Resilience, Durham University, Durham DH1 3LE, UK.
Richard BowerInstitute for Data Science, Durham University, Durham DH1 3LE, UK.
Frank KraussInstitute for Data Science, Durham University, Durham DH1 3LE, UK.ORCID 0000-0001-5043-3099

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We introduce June, an open-source framework for the detailed simulation of epidemics on the basis of social interactions in a virtual population constructed from geographically granular census data, reflecting age, sex, ethnicity and socio-economic indicators. Interactions between individuals are modelled in groups of various sizes and properties, such as households, schools and workplaces, and other social activities using social mixing matrices. June provides a suite of flexible parametrizations that describe infectious diseases, how they are transmitted and affect contaminated individuals. In this paper, we apply June to the specific case of modelling the spread of COVID-19 in England. We discuss the quality of initial model outputs which reproduce reported hospital admission and mortality statistics at national and regional levels as well as by age strata.

Indexed as

individual-based modelinfectious diseasesimulation

Identifiers

PMID34295529
PMCPMC8261230

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.