Evidence map›Paper›PMID 40247690›Full record

ArticleThe Journal of infectious diseases2025

Estimating the Waning Effectiveness of COVID-19 Vaccines From Population-Level Surveillance Data in Hong Kong.

Haoling Chen, Xiaotong Huang, Can Wang, Benjamin J Cowling, Tim K Tsang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Journal of infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Haoling ChenWHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.
Xiaotong HuangWHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.
Can WangWHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.
Benjamin J CowlingWHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.ORCID 0000-0002-6297-7154
Tim K TsangWHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.

Funding

Hong Kong SAR GovernmentHong Kong SAR Government 17110221Hong Kong SAR Government HKUHong Kong SAR Government SRFS2021-7S03Hong Kong SAR Government T11-705/21-NInnovation and Technology CommissionResearch Grants Council
6 · The paper itself

Abstract

Assessing the time-varying effectiveness of coronavirus disease 2019 (COVID-19) vaccines is critical to guiding vaccine strategies and public health policies. We developed a Bayesian framework to estimate the waning vaccine effectiveness of various doses of CoronaVac and Comirnaty based on population-level surveillance data. We applied this framework to data on a large Omicron BA.2 epidemic in Hong Kong from January to May 2022. Our results indicated that despite high vaccination rates during this period, high vaccine uptake mitigated but did not prevent rapid spread of infection.

Indexed as

COVID-19COVID-19 VaccinesVaccine EfficacyBayes TheoremHong KongHumansPopulation SurveillanceSARS-CoV-2VaccinationCOVID-19 VaccinesBayesian inferenceCOVID-19infectious disease modelingpopulation-level surveillance datavaccine effectiveness

Identifiers

PMID40247690
PMCPMC12349934

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.