Evidence map›Paper›PMID 34631122›Full record

ArticleRoyal Society open science2021

Vaccination and testing of the border workforce for COVID-19 and risk of community outbreaks: a modelling study.

Michael J Plank, Rachelle N Binny, Shaun C Hendy, Audrey Lustig, Kannan Ridings

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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

5 authors.

Michael J PlankSchool of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand.ORCID 0000-0002-7539-3465
Rachelle N BinnyManaaki Whenua, Lincoln, New Zealand.ORCID 0000-0002-3433-0417
Shaun C HendyDepartment of Physics, University of Auckland, Auckland, New Zealand.ORCID 0000-0003-3468-6517
Audrey LustigManaaki Whenua, Lincoln, New Zealand.
Kannan RidingsDepartment of Physics, University of Auckland, Auckland, New Zealand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Throughout 2020 and the first part of 2021, Australia and New Zealand have followed a COVID-19 elimination strategy. Both countries require overseas arrivals to quarantine in government-managed facilities at the border. In both countries, community outbreaks of COVID-19 have been started via infection of a border worker. This workforce is rightly being prioritized for vaccination. However, although vaccines are highly effective in preventing disease, their effectiveness in preventing infection with and transmission of SARS-CoV-2 is less certain. There is a danger that vaccination could prevent symptoms of COVID-19 but not prevent transmission. Here, we use a stochastic model of SARS-CoV-2 transmission and testing to investigate the effect that vaccination of border workers has on the risk of an outbreak in an unvaccinated community. We simulate the model starting with a single infected border worker and measure the number of people who are infected before the first case is detected by testing. We show that if a vaccine reduces transmission by 50%, vaccination of border workers increases the risk of a major outbreak from around 7% per seed case to around 9% per seed case. The lower the vaccine effectiveness against transmission, the higher the risk. The increase in risk as a result of vaccination can be mitigated by increasing the frequency of routine testing for high-exposure vaccinated groups.

Indexed as

COVID-19 eliminationepidemic modelquarantinesurveillance testingvaccines

Identifiers

PMID34631122
PMCPMC8479330

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.