Evidence map›Paper›PMID 35841012›Full record

ArticleBMC public health2022

Mathematical modelling of vaccination rollout and NPIs lifting on COVID-19 transmission with VOC: a case study in Toronto, Canada.

Elena Aruffo, Pei Yuan, Yi Tan, Evgenia Gatov, Iain Moyles, Jacques Bélair, James Watmough, Sarah Collier, Julien Arino, Huaiping Zhu

Open access · goldAbstract read
In one paragraph

Article in BMC public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
2.6field-weighted citation impact, top 10% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.

  1. Pooled it
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  5. Review
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  7. Article
  8. Retrospective Analysis of Equity-Based Optimization for COVID-19 Vaccine Allocation.medRxiv : the preprint server for health sciences · 2023
    Article
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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

10 authors at 3 institutions in 1 country.

Elena Aruffo *Centre for Disease Modelling (CDM), York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada.
Pei Yuan *Centre for Disease Modelling (CDM), York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada.
Yi Tan *Centre for Disease Modelling (CDM), York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada.
Evgenia GatovToronto Public Health, City of Toronto, Toronto, ON, Canada.
Iain MoylesCentre for Disease Modelling (CDM), York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada.
Jacques BélairCentre for Disease Modelling (CDM), York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada.
James WatmoughCentre for Disease Modelling (CDM), York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada.
Sarah CollierToronto Public Health, City of Toronto, Toronto, ON, Canada.
Julien ArinoCentre for Disease Modelling (CDM), York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada.
Huaiping ZhuCentre for Disease Modelling (CDM), York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada. huaiping@yorku.ca.
York University · CAToronto Public Health · CAUniversity of Manitoba · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSince December 2020, public health agencies have implemented a variety of vaccination strategies to curb the spread of SARS-CoV-2, along with pre-existing Nonpharmaceutical Interventions (NPIs). Initial strategies focused on vaccinating the elderly to prevent hospitalizations and deaths, but with vaccines becoming available to the broader population, it became important to determine the optimal strategy to enable the safe lifting of NPIs while avoiding virus resurgence.

methodsWe extended the classic deterministic SIR compartmental disease-transmission model to simulate the lifting of NPIs under different vaccine rollout scenarios. Using case and vaccination data from Toronto, Canada between December 28, 2020, and May 19, 2021, we estimated transmission throughout past stages of NPI escalation/relaxation to compare the impact of lifting NPIs on different dates on cases, hospitalizations, and deaths, given varying degrees of vaccine coverages by 20-year age groups, accounting for waning immunity.

resultsWe found that, once coverage among the elderly is high enough (80% with at least one dose), the main age groups to target are 20-39 and 40-59 years, wherein first-dose coverage of at least 70% by mid-June 2021 is needed to minimize the possibility of resurgence if NPIs are to be lifted in the summer. While a resurgence was observed for every scenario of NPI lifting, we also found that under an optimistic vaccination coverage (70% coverage by mid-June, along with postponing reopening from August 2021 to September 2021) can reduce case counts and severe outcomes by roughly 57% by December 31, 2021.

conclusionsOur results suggest that focusing the vaccination strategy on the working-age population can curb the spread of SARS-CoV-2. However, even with high vaccination coverage in adults, increasing contacts and easing protective personal behaviours is not advisable since a resurgence is expected to occur, especially with an earlier reopening.

Indexed as

COVID-19AdultAgedCanadaHumansModels, TheoreticalSARS-CoV-2VaccinationAge structureCOVID-19Mathematical modelingNonpharmaceutical InterventionsResurgenceSARS-CoV-2VaccineVOCWaning

Identifiers

PMID35841012
PMCPMC9283818
OpenAlexW4285589900

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.