Evidence map›Paper›PMID 38098851›Full record

ArticleFrontiers in medicine2023

"Low-risk groups" deserve more attention than "high-risk groups" in imported COVID-19 cases.

Wanshan Zheng, Ying Tan, Zedi Zhao, Jin Chen, Xiaomei Dong, Xiongfei Chen

Open access · goldAbstract read
In one paragraph

Article in Frontiers in medicine, 2023. 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, top 80% of its field
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, 0 citations in OpenAlex.

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

6 authors at 2 institutions in 1 country.

Wanshan ZhengDepartment of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, Guangdong, China.
Ying TanDepartment of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, Guangdong, China.
Zedi ZhaoDepartment of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, Guangdong, China.
Jin ChenDepartment of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, Guangdong, China.
Xiaomei DongDepartment of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, Guangdong, China.
Xiongfei ChenGuangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China.
Jinan University · CNGuangzhou Center for Disease Control and Prevention · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To estimate the optimal quarantine period for inbound travelers and identify key risk factors to provide scientific reference for emerging infectious diseases. Methods: A parametric survival analysis model was used to calculate the time interval between entry and first positive nucleic acid test of imported cases in Guangzhou, to identify the influencing factors. And the COVID-19 epidemic risk prediction model based on multiple risk factors among inbound travelers was constructed. Results: The approximate 95th percentile of the time interval was 14 days. Multivariate analysis found that the mean time interval for inbound travelers in entry/exit high-risk occupations was 29% shorter (OR 0.29, 95% CI 0.18-0.46, Conclusion: Different strata of imported cases can result in varying degrees of risk of indigenous outbreaks. "low-risk groups" with entry/exit low-risk occupations, fully vaccinated, or from Asia deserve more attention than "high-risk groups."

Indexed as

COVID-19imported casequarantine periodrisk factortime interval

Identifiers

PMID38098851
PMCPMC10720434
OpenAlexW4389200638

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.