Evidence map›Paper›PMID 38736991›Full record

ArticleChina CDC weekly2024

Genomic Surveillance for SARS-CoV-2 Variants: Dominance of XBB Replacement - China, January-June 2023.

Yenan Feng, Qiudong Su, Lili Li, Xiaozhou He, Peihua Niu, Xiaojuan Guo, Xiang Zhao, Jing Tang, Zhiyuan Jia, Ji Wang and 9 more

Abstract read
In one paragraph

Article in China CDC weekly, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

19 authors.

Yenan FengNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Qiudong SuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Lili LiNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Xiaozhou HeNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Peihua NiuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Xiaojuan GuoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Xiang ZhaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Jing TangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Zhiyuan JiaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Ji WangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Changcheng WuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Baicheng XiaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Zhixiao ChenNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Yuchao WuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Jing YangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Songqi ChenNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Cao ChenNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Shiwen WangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Xiaoping DongNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: In the first half of 2023, a global shift was observed towards the predominance of XBB variants. China faced a significant epidemic between late 2022 and early 2023 due to Omicron subvariants BA.5.2 and BF.7. This study aims to depict the evolving variant distribution among provincial-level administrative divisions (PLADs) in China and explore the factors driving the predominance of XBB replacement. Methods: Sequences from local and imported coronavirus disease 2019 (COVID-19) cases recorded between January 1 and June 30, 2023, were included. The study analyzed the changing distribution of viral variants and assessed how the prior dominance of specific variants, XBB subvariants, and imported cases influenced the prevalence of the XBB replacement variant. Results: A total of 56,486 sequences were obtained from local cases, and 8,669 sequences were from imported cases. Starting in April, there was a shift in the prevalence of XBB from imported to local cases, with varying dominance among PLADs. In PLADs previously high in BF.7, the rise of XBB was delayed. A positive correlation was found between XBB proportions in imported cases from January to March and local cases in April. The distribution pattern of XBB subvariants differed between local and imported cases within the same PLAD. No significant differences were noted in the replacement rates of XBB subvariants. Conclusions: The timing of XBB dominance differed among various PLADs in China in the first half of 2023, correlating closely with the prevalence of XBB variants among imported cases.

Indexed as

Genomic surveillanceSARS-CoV-2XBB

Identifiers

PMID38736991
PMCPMC11082056

What OpenQuestion holds

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LicenceCC BY-NC-SA
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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.