Evidence map›Paper›PMID 35428853›Full record

ArticleScientific reports2022

Risk of sustained SARS-CoV-2 transmission in Queensland, Australia.

Paula Sanz-Leon, Nathan J Stevenson, Robyn M Stuart, Romesh G Abeysuriya, James C Pang, Stephen B Lambert, Cliff C Kerr, James A Roberts

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Modelling herd immunity requirements in Queensland: impact of vaccination effectiveness, hesitancy and variants of SARS-CoV-2.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022
    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

8 authors.

Paula Sanz-LeonBrain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia. paula.sanz-leon@qimrberghofer.edu.au.
Nathan J StevensonBrain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
Robyn M StuartDepartment of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark.
Romesh G AbeysuriyaBurnet Institute, Melbourne, VIC, Australia.
James C PangBrain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
Stephen B LambertNational Centre for Immunisation Research and Surveillance for Vaccine Preventable Diseases, Westmead, Sydney, NSW, Australia.
Cliff C KerrInstitute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, WA, USA.
James A RobertsBrain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We used an agent-based model Covasim to assess the risk of sustained community transmission of SARSCoV-2/COVID-19 in Queensland (Australia) in the presence of high-transmission variants of the virus. The model was calibrated using the demographics, policies, and interventions implemented in the state. Then, using the calibrated model, we simulated possible epidemic trajectories that could eventuate due to leakage of infected cases with high-transmission variants, during a period without recorded cases of locally acquired infections, known in Australian settings as "zero community transmission". We also examined how the threat of new variants reduces given a range of vaccination levels. Specifically, the model calibration covered the first-wave period from early March 2020 to May 2020. Predicted epidemic trajectories were simulated from early February 2021 to late March 2021. Our simulations showed that one infected agent with the ancestral (A.2.2) variant has a 14% chance of crossing a threshold of sustained community transmission (SCT) (i.e., > 5 infections per day, more than 3 days in a row), assuming no change in the prevailing preventative and counteracting policies. However, one agent carrying the alpha (B.1.1.7) variant has a 43% chance of crossing the same threshold; a threefold increase with respect to the ancestral strain; while, one agent carrying the delta (B.1.617.2) variant has a 60% chance of the same threshold, a fourfold increase with respect to the ancestral strain. The delta variant is 50% more likely to trigger SCT than the alpha variant. Doubling the average number of daily tests from ∼ 6,000 to 12,000 results in a decrease of this SCT probability from 43 to 33% for the alpha variant. However, if the delta variant is circulating we would need an average of 100,000 daily tests to achieve a similar decrease in SCT risk. Further, achieving a full-vaccination coverage of 70% of the adult population, with a vaccine with 70% effectiveness against infection, would decrease the probability of SCT from a single seed of alpha from 43 to 20%, on par with the ancestral strain in a naive population. In contrast, for the same vaccine coverage and same effectiveness, the probability of SCT from a single seed of delta would decrease from 62 to 48%, a risk slightly above the alpha variant in a naive population. Our results demonstrate that the introduction of even a small number of people infected with high-transmission variants dramatically increases the probability of sustained community transmission in Queensland. Until very high vaccine coverage is achieved, a swift implementation of policies and interventions, together with high quarantine adherence rates, will be required to minimise the probability of sustained community transmission.

Indexed as

COVID-19SARS-CoV-2AdultAustraliaHumansQueensland

Identifiers

PMID35428853
PMCPMC9012253

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.