Evidence map›Paper›PMID 42432522›Full record

ArticleBMC infectious diseases2026

High-resolution contact data and infectious disease absenteeism in kindergarten populations.

Fan Jiang, Ying Wen, Fangfang Lu, Deji Ciren, Yingluan Zhang, Huiyang Sun, Rijing Luo, Zhen Zhang, Qiuying Lv

Abstract read
In one paragraph

Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Fan Jiang *School of Public Health, Southern Medical University, Guangzhou, 510515, China.
Ying Wen *School of Public Health, Southern Medical University, Guangzhou, 510515, China.
Fangfang Lu *Shenzhen Baoan District Fuyong Public Health Service Center, Shenzhen, 518115, China.
Deji CirenShenzhen Center for Disease Control and Prevention, NO.8 Longyuan Rd, Shenzhen, 518055, China.
Yingluan ZhangShenzhen Center for Disease Control and Prevention, NO.8 Longyuan Rd, Shenzhen, 518055, China.
Huiyang SunShenzhen Center for Disease Control and Prevention, NO.8 Longyuan Rd, Shenzhen, 518055, China.
Rijing LuoSchool of Public Health, Southern Medical University, Guangzhou, 510515, China.
Zhen ZhangShenzhen Center for Disease Control and Prevention, NO.8 Longyuan Rd, Shenzhen, 518055, China. zhangzhen@wjw.sz.gov.cn.
Qiuying LvSchool of Public Health, Southern Medical University, Guangzhou, 510515, China. sandylv1980@126.com.

Funding

Guangdong Provincial Medical Science and Technology Research Fund A2025272Shenzhen Science and Technology Innovation Commission JCYJ20210324115411030Shenzhen Science and Technology Program SYSPG20241211173921049
6 · The paper itself

Abstract

backgroundClose social interactions are fundamental drivers of infectious disease transmission in early childhood education settings. However, high-resolution empirical contact data from these environments remain scarce, limiting our understanding of transmission dynamics and prevention strategies.

methodsWe deployed name-tag devices using Ultra-Wide Band (UWB) technology in a public kindergarten in Shenzhen, China. We recorded close-proximity contact events (≤ 2 m) during a typical school day. Contact events were classified into four risk categories based on duration and proximity. We then conducted a joint analysis of temporally segmented, risk-stratified networks alongside six months of infectious-disease-related absenteeism records.

resultsThe study achieved an 88.68% participation rate (282 of 318 children) and full participation of 30 teachers, resulting in 389,617 contact events. On average, each child interacted with 57.91 distinct individuals daily. Outdoor activities involved fewer and lower-risk contacts. In contrast, nap time-a sedentary activity-accounted for the highest proportion of high-risk contact time (14.38%). While children with and without absenteeism records had similar total contact numbers and durations over the full day, those in the absentee group engaged in more high-risk contacts. Specifically, they had longer and more frequent interactions across the medium-low, medium-high, and high risk layers. This disparity diminished when low risk contacts were included. At the class level, absenteeism rates were correlated with the density of medium-high (r = 0.833, p = 0.005) and high risk (r = 0.733, p = 0.025) contact layers.

conclusionsOur findings reveal substantial structural and contextual heterogeneity within kindergarten contact networks and provide quantitative evidence associating contact patterns with health outcomes. High-resolution proximity data offer valuable insights into transmission risk and inform the design of targeted prevention and risk management strategies in early childhood education settings.

Indexed as

AbsenteeismCommunicable DiseasesContact TracingChildChild, PreschoolChinaFemaleHumansMaleSchoolsContact networksEarly childhood education settingsRisk stratificationUltra-wide band proximity sensing

Identifiers

PMID42432522
PMCPMC13640370

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