Evidence map›Paper›PMID 39148103›Full record

SynthesisArchives of public health = Archives belges de sante publique2024

Trends in symptom prevalence and sequential onset of SARS-CoV-2 infection from 2020 to 2022 in East and Southeast Asia: a trajectory pattern exploration based on summary data.

Tian Song, Jing Guo, Bin Liu, Lu Yang, Xiangwei Dai, Fuqiang Zhang, Zhaoyuan Gong, Mingzhi Hu, Qianzi Che, Nannan Shi

Abstract readSystematic Review
In one paragraph

Synthesis in Archives of public health = Archives belges de sante publique, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

10 authors.

Tian Song *Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei Ave, Dongcheng District, Beijing, 100700, P.R. China.
Jing Guo *Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei Ave, Dongcheng District, Beijing, 100700, P.R. China.
Bin LiuInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei Ave, Dongcheng District, Beijing, 100700, P.R. China.
Lu YangInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei Ave, Dongcheng District, Beijing, 100700, P.R. China.
Xiangwei DaiInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei Ave, Dongcheng District, Beijing, 100700, P.R. China.
Fuqiang ZhangInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei Ave, Dongcheng District, Beijing, 100700, P.R. China.
Zhaoyuan GongInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei Ave, Dongcheng District, Beijing, 100700, P.R. China.
Mingzhi HuInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei Ave, Dongcheng District, Beijing, 100700, P.R. China.
Qianzi Che *Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei Ave, Dongcheng District, Beijing, 100700, P.R. China. cheqianzi123@126.com.
Nannan Shi *Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei Ave, Dongcheng District, Beijing, 100700, P.R. China. 13811839164@vip.126.com.

Funding

2022 Qi Huang Young Scholar programme of the National Administration of Traditional Chinese Medicine Z0841China Academy of Chinese Medical Sciences Innovation Fund CI2022C004-01Natural Science Foundation of Beijing, China 7244507
6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic's diverse symptomatology, driven by variants, underscores the critical need for a comprehensive understanding. Employing stochastic models, our study evaluates symptom sequences across SARS-CoV-2 variants on aggregated data, yielding essential insights for targeted interventions.

methodsWe conducted a meta-analysis based on research literature published before December 9, 2022, from PubMed, LitCovid, Google Scholar, and CNKI databases, to investigate the prevalence of COVID-19 symptoms during the acute phase. Registered in PROSPERO (CRD42023402568), we performed random-effects meta-analyses using the R software to estimate pooled prevalence and 95% CI. Based on our findings, we introduced the Stochastic Progression Model and Sequential Pattern Discovery using Equivalence classes (SPADE) algorithm to analyze patterns of symptom progression across different variants.

resultsEncompassing a total of 430,100 patients from east and southeast Asia, our results reveal the highest pooled estimate for cough/dry cough across wild-type, Delta, and Omicron variants, with fever (78.18%; 95% CI: 67-89%) being the most prominent symptom for the Alpha variant. Symptoms associated with the Omicron variant primarily manifested in upper respiratory tracts, cardiovascular, and neuropsychiatric systems. Stochastic models indicate early symptoms including dry cough and fever, followed by subsequent development of sleep disorders, fatigue, and more.

conclusionOur study underscores the evolving symptomatology across SARS-CoV-2 variants, emphasizing similarities in fever, cough, and fatigue. The Omicron variant presents a distinct profile characterized by milder symptoms yet heightened neuropsychological challenges. Advanced analytical models validate the observed sequential progression of symptoms, reinforcing the consistency of disease trajectory.

Indexed as

Disease progressionSARS-CoV-2 variantsStochastic modelSymptom trajectories

Identifiers

PMID39148103
PMCPMC11325837

What OpenQuestion 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 OpenQuestion graph.