Evidence map›Paper›PMID 35062678›Full record

ArticleVaccines2021

COVID-19 Seroprevalence in Canada Modelling Waning and Boosting COVID-19 Immunity in Canada a Canadian Immunization Research Network Study.

David W Dick, Lauren Childs, Zhilan Feng, Jing Li, Gergely Röst, David L Buckeridge, Nick H Ogden, Jane M Heffernan

Abstract read
In one paragraph

Article in Vaccines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

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

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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

8 authors.

David W DickMathematics and Statistics, Centre for Disease Modelling, York University, Toronto, ON M3J 1P3, Canada.
Lauren ChildsDepartment of Mathematics, Virginia Tech, Blacksburg, VA 24061, USA.ORCID 0000-0003-3904-3895
Zhilan FengDepartment of Mathematics, Purdue University, West Lafayette, IN 46202, USA.
Jing LiDepartment of Mathematics, California State University, Northridge, CA 91330, USA.
Gergely RöstDepartment of Mathematics, University of Szeged, 6720 Szeged, Hungary.ORCID 0000-0001-9476-3284
David L BuckeridgeEpidemiology and Biostatistics, School of Population and Global Health, McGill University, Montreal, QC H3A 0G4, Canada.
Nick H OgdenNational Microbiology Laboratory, Public Health Risk Sciences Division, Public Health Agency of Canada, St. Hyacinthe, QC J2S 2M2, Canada.
Jane M HeffernanMathematics and Statistics, Centre for Disease Modelling, York University, Toronto, ON M3J 1P3, Canada.

Funding

Canadian Immunization Research Network (CIRN)Natural Sciences and Engineering Research Council of Canada (NSERC)
6 · The paper itself

Abstract

COVID-19 seroprevalence changes over time, with infection, vaccination, and waning immunity. Seroprevalence estimates are needed to determine when increased COVID-19 vaccination coverage is needed, and when booster doses should be considered, to reduce the spread and disease severity of COVID-19 infection. We use an age-structured model including infection, vaccination and waning immunity to estimate the distribution of immunity to COVID-19 in the Canadian population. This is the first mathematical model to do so. We estimate that 60-80% of the Canadian population has some immunity to COVID-19 by late Summer 2021, depending on specific characteristics of the vaccine and the waning rate of immunity. Models results indicate that increased vaccination uptake in age groups 12-29, and booster doses in age group 50+ are needed to reduce the severity COVID-19 Fall 2021 resurgence.

Indexed as

COVID-19infectious disease modellingmathematical epidmiologyseroprevalence

Identifiers

PMID35062678
PMCPMC8779812

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.