Evidence map›Paper›PMID 33177507›Full record

ArticleNature communications2020

Modelling transmission and control of the COVID-19 pandemic in Australia.

Sheryl L Chang, Nathan Harding, Cameron Zachreson, Oliver M Cliff, Mikhail Prokopenko

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 229 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
229citing papers in PubMed, 1 pooled it
13.3field-weighted citation impact, top 1% of its field
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

229 citing papers in PubMed, 1 synthesis or guideline pooled it, 438 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

169 more citing papers are in PubMed but not listed here.

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

5 authors at 1 institution in 1 country.

Sheryl L ChangCentre for Complex Systems, Faculty of Engineering, University of Sydney, Sydney, NSW, 2006, Australia.ORCID 0000-0002-6119-828X
Nathan HardingCentre for Complex Systems, Faculty of Engineering, University of Sydney, Sydney, NSW, 2006, Australia.ORCID 0000-0003-1707-9248
Cameron ZachresonCentre for Complex Systems, Faculty of Engineering, University of Sydney, Sydney, NSW, 2006, Australia.ORCID 0000-0002-0578-4049
Oliver M CliffCentre for Complex Systems, Faculty of Engineering, University of Sydney, Sydney, NSW, 2006, Australia.ORCID 0000-0001-5041-4090
Mikhail ProkopenkoCentre for Complex Systems, Faculty of Engineering, University of Sydney, Sydney, NSW, 2006, Australia. mikhail.prokopenko@sydney.edu.au.ORCID 0000-0002-4215-0344
The University of Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is a continuing debate on relative benefits of various mitigation and suppression strategies aimed to control the spread of COVID-19. Here we report the results of agent-based modelling using a fine-grained computational simulation of the ongoing COVID-19 pandemic in Australia. This model is calibrated to match key characteristics of COVID-19 transmission. An important calibration outcome is the age-dependent fraction of symptomatic cases, with this fraction for children found to be one-fifth of such fraction for adults. We apply the model to compare several intervention strategies, including restrictions on international air travel, case isolation, home quarantine, social distancing with varying levels of compliance, and school closures. School closures are not found to bring decisive benefits unless coupled with high level of social distancing compliance. We report several trade-offs, and an important transition across the levels of social distancing compliance, in the range between 70% and 80% levels, with compliance at the 90% level found to control the disease within 13-14 weeks, when coupled with effective case isolation and international travel restrictions.

Indexed as

AdultAustraliaBetacoronavirusChildCommunicable Disease ControlComputer SimulationCoronavirus InfectionsCOVID-19HumansPandemicsPneumonia, ViralQuarantineSARS-CoV-2SchoolsSocial Isolation

Identifiers

PMID33177507
PMCPMC7659014
OpenAlexW3012742975

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.