Evidence map›Paper›PMID 38738254›Full record

ArticleJournal of thoracic disease2024

Relationship of various COVID-19 antibody titer with individual characteristics and prediction of future epidemic trend in Xiamen City, China.

Liansheng Xu, Buasiyamu Abudunaibi, Zhiqi Zeng, Yunkang Zhao, Yao Wang, Xiaohao Guo, Yidun Zhang, Tao Li, Wenkui Lu, Weiliang Tian and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of thoracic disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

13 authors.

Liansheng Xu *Xiamen Center for Disease Control and Prevention, Xiamen, China.
Buasiyamu Abudunaibi *State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen, China.
Zhiqi Zeng *Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications, Macao University of Science and Technology, Macao, China.
Yunkang ZhaoState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen, China.
Yao WangState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen, China.
Xiaohao GuoState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen, China.
Yidun ZhangXiamen Center for Disease Control and Prevention, Xiamen, China.
Tao LiState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen, China.
Wenkui LuXiamen Center for Disease Control and Prevention, Xiamen, China.
Weiliang TianSchool of Medicine, Duke University, Durham, NC, USA.
Zhinan Guo *Xiamen Center for Disease Control and Prevention, Xiamen, China.
Chenghao Su *Zhongshan Hospital, Fudan University (Xiamen Branch), Xiamen, China.
Tianmu Chen *State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Reinfection of coronavirus disease 2019 (COVID-19) has raised concerns about how reliable immunity from infection and vaccination is. With mass testing for the virus halted, understanding the current prevalence of COVID-19 is crucial. This study investigated 1,191 public health workers at the Xiamen Center for Disease Control, focusing on changes in antibody titers and their relationship with individual characteristics. Methods: The study began by describing the epidemiological characteristics of the study participants. Multilinear regression (MLR) models were employed to explore the associations between individual attributes and antibody titers. Additionally, group-based trajectory models (GBTMs) were utilized to identify trajectories in antibody titer changes. To predict and simulate future epidemic trends and examine the correlation of antibody decay with epidemics, a high-dimensional transmission dynamics model was constructed. Results: Analysis of epidemiological characteristics revealed significant differences in vaccination status between infected and non-infected groups (χ Conclusions: Our study shows that antibody titer could be influenced by age, previous pulmonary diseases as well as smoking. Furthermore, the decline in IgG titers is consistent with epidemic trends. These findings emphasize the need for further exploration of these factors and the development of optimized self-protection countermeasures against reinfection.

Indexed as

antibody titerCoronavirus disease 2019 (COVID-19)reinfectionstatistical modelstransmission dynamics model

Identifiers

PMID38738254
PMCPMC11087623

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.