Evidence map›Paper›PMID 41029585›Full record

ArticleBMC public health2025

Risk assessment of communicable respiratory diseases transmission based on social contact networks: a primary school contact data survey conducted with portable high-precision devices.

Qiuying Lv, Rijing Luo, Xiaoheng Li, Simu Deng, Kang Liu, Lei Liu, Kemin Zhu, Ying Wen, Xingyu Ma, Rendeji Ci and 3 more

Abstract read
In one paragraph

Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 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

13 authors.

Qiuying Lv *Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
Rijing Luo *School of Public Health, Southern Medical University, Guangzhou, China.
Xiaoheng Li *Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
Simu DengShenzhen Longgang District Nanwan Public Health Service Center, Shenzhen, China.
Kang LiuShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Lei LiuShenzhen Longgang District Nanwan Public Health Service Center, Shenzhen, China.
Kemin ZhuShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Ying WenShenzhen Center for Disease Control and Prevention, Shenzhen, China.
Xingyu MaSchool of Public Health, Guangdong Medical University, Dongguan, China.
Rendeji CiShenzhen Center for Disease Control and Prevention, Shenzhen, China.
Ling YinShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Zhen ZhangShenzhen Center for Disease Control and Prevention, Shenzhen, China. zhangzhen@wjw.sz.gov.cn.
Xuan ZouShenzhen Center for Disease Control and Prevention, Shenzhen, China. xuanzou2024@163.com.

Funding

Sanming Project of Medicine in Shenzen Municipality SZSM202011008Shenzhen Key Medical Discipline Construction Fund SZXK064Shenzhen Municipal Technological Project JCYJ20200109150715644the Key Project of Shenzhen Science and Technology Innovation Commission JCYJ20210324115411030; JCYJ20200109150715644; JSGG20200225153023511
6 · The paper itself

Abstract

backgroundDuring the 2020 COVID-19 pandemic, class suspension and school closures, as non-pharmacological interventions, effectively curbed on-campus communicable diseases transmission by minimizing contact. Targeted temporary measures taken for high-risk groups and activities, such as suspending a certain group activity and isolating students with symptoms at home, can significantly reduce transmission without the need for a complete suspension. However, there is currently a lack of in-depth analysis of teacher and student contact behavior. The aim is to assess the risk of communicable respiratory diseases transmission among different populations and activities in primary school.

methodsWe utilized Ultra-Wideband (UWB) wearable devices - a wireless positioning technology enabling centimeter-level proximity detection to record 143,328 close contacts among 292 teachers and students in a primary school throughout the day. By converting data into a network matrix, we constructed a dynamic contact network for the corresponding analysis and calculated network indicators. We also analyzed the contact patterns and distribution of contagion risk among different groups and activity types of contacts.

resultsNetwork analysis reveals that among the top 10% of individuals in terms of closeness centrality and eigenvector centrality, the proportion of younger students was the highest. Additionally,the duration of single contact was longer among younger students. High-risk contacts predominantly occurred during classes, after-school service, large recess activity and lunch break. The risk of contact was highest among teachers and students of the same grade, with an average contact duration of 57.46 and 56.17 min, while inter-grade durations were only 4.15 and 1.17 min. Moreover, same-grade teachers and students had higher proportion of medium-high risk and above interactions. Inter-grade contacts primarily occurred among students (38.99% medium-high-risk interactions), with 90.45% concentrated during morning self-study and after-school services.

conclusionsThe identification of high-risk contact activities and populations on campus provides important quantitative parameters for targeted intervention particular during the early stage of outbreak.

Indexed as

Contact TracingCOVID-19SchoolsStudentsWearable Electronic DevicesChildFemaleHumansMaleRisk AssessmentSchool TeachersSurveys and QuestionnairesClose proximity interactionCommunicable diseasesContagion risk levelSchool pandemic preventionWearable devices

Identifiers

PMID41029585
PMCPMC12487565

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.