Evidence map›Paper›PMID 39074840›Full record

ArticleRisk analysis : an official publication of the Society for Risk Analysis2025

Risk assessment of infection of COVID-19 contacts based on scenario simulation.

Wei-Wen Zhang, Yan-Ran Huang, Yu-Yuan Wang, Ze-Xi Lu, Jia-Lin Sun, Ming-Xia Jing

Abstract read
In one paragraph

Article in Risk analysis : an official publication of the Society for Risk Analysis, 2025. 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. Risk assessment of infection of COVID-19 contacts based on scenario simulation.Risk analysis : an official publication of the Society for Risk Analysis · 2025
    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

6 authors.

Wei-Wen ZhangDepartment of Preventive Medicine, Shihezi University School of Medicine, Shihezi, China.
Yan-Ran HuangDepartment of Preventive Medicine, Shihezi University School of Medicine, Shihezi, China.
Yu-Yuan WangDepartment of Preventive Medicine, Shihezi University School of Medicine, Shihezi, China.
Ze-Xi LuDepartment of Preventive Medicine, Shihezi University School of Medicine, Shihezi, China.
Jia-Lin SunDepartment of Preventive Medicine, Shihezi University School of Medicine, Shihezi, China.
Ming-Xia JingDepartment of Preventive Medicine, Shihezi University School of Medicine, Shihezi, China.

Funding

Financial Science and Technology Project of the Corps (provincial level) (2021AB034)Self funded and supported research projects at the school level by Shihezi University (ZZZC202014)USDA-NIFA 2022-69008-36645
6 · The paper itself

Abstract

We constructed a rapid infection risk assessment model for contacts of COVID-19. The improved Wells-Riley model was used to estimate the probability of infection for contacts of COVID-19 in the same place and evaluate their risk grades. We used COVID-19 outbreaks that were documented to validate the accuracy of the model. We analyzed the relationship between controllable factors and infection probability and constructed common scenarios to analyze the infection risk of contacts in different scenarios. The model showed the robustness of the fitting (mean relative error = 5.89%, mean absolute error = 2.03%, root mean squared error = 2.03%, R

Indexed as

COVID-19Computer SimulationHumansMasksProbabilityRisk AssessmentSARS-CoV-2VentilationcontactsCOVID‐19infection probabilityrisk gradesWells–Riley model

Identifiers

PMID39074840
PMCPMC11787960

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.