Evidence map›Paper›PMID 40092435›Full record

ArticleResearch square2025

Exploring Temporal and Spatial Characteristics of Serious Adverse Event Reports Following COVID-19 Bivalent Vaccines.

Yiming Li, Wei Tao, Yifang Dang, Yong Chen, Cui Tao

Abstract readPreprint
In one paragraph

Article in Research square, 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.

Yiming Li
Wei Tao
Yifang Dang
Yong Chen
Cui Tao

Funding

VIOLIN 2.0: Vaccine Information and Ontology LInked kNowledgebaseU24AI171008 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Yongqun He, Junguk Hur · 2022 to 2026
$4.2M
Dynamic learning for post-vaccine event prediction using temporal information in VAERSR01AI130460 · NIAID · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CHEN, YONG, TAO, CUI · 2017 to 2021
$3.4M
NIAID NIH HHS R01 AI130460NIAID NIH HHS U24 AI171008
6 · The paper itself

Abstract

Background: To mitigate the spread of variants such as Omicron in COVID-19 pandemic, the development and utilization of COVID-19 bivalent vaccines have become essential. However, an expected subset of individuals may experience serious adverse events (AE) after receiving the COVID-19 bivalent vaccine. Methods: In this research, we conducted an in-depth analysis of data obtained from the Centers for Disease Control and Prevention (CDC) and the Vaccine Adverse Event Reporting System (VAERS) to evaluate the safety of COVID-19 bivalent vaccines administered between 9/15/2022 and 9/1/2023. The Standard Federal Regions were used for region partitions. To broaden our understanding of post-vaccination AE, we performed temporal analysis to investigate the trends of Top 10 reported AE in all serious adverse event reports. We also examined the similarity of AE across diverse regions within the United States. Results: Our findings indicated that a relatively stably decreasing trend was observed over time, with four peaks in December 2022, February 2023, Mar 2023 and April 2023. In terms of spatial analysis, the middle and northern regions exhibited higher rates of reported AEs associated with COVID-19 bivalent vaccine. An obvious similar pattern of AE is observed across regions (III, IV, V, VI, VII). Conclusion: Overall, our research underscores the ongoing need for vigilant post-licensure vaccine monitoring, emphasizing the continuous surveillance and analysis essential for upholding the safety and effectiveness of COVID-19 bivalent vaccines.

Indexed as

Adverse event following immunizationCOVID-19COVID-19 Bivalent VaccineCOVID-19 VaccineVaccineVaccine Adverse Event Reporting System

Identifiers

PMID40092435
PMCPMC11908350

What OpenQuestion holds

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Registered trials

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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.