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Author:

Guo, Yong (Guo, Yong.) | Dou, Zhiyang (Dou, Zhiyang.) | Zhang, Nan (Zhang, Nan.) (Scholars:张楠) | Liu, Xiyue (Liu, Xiyue.) | Su, Boni (Su, Boni.) | Li, Yuguo (Li, Yuguo.) | Zhang, Yinping (Zhang, Yinping.) | Bovell-Benjamin, Adelia (Bovell-Benjamin, Adelia.)

Indexed by:

Scopus

Abstract:

Classrooms are high-risk indoor environments, so analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in classrooms is important for determining optimal interventions. Due to the absence of human behavior data, it is challenging to accurately determine virus exposure in classrooms. A wearable device for close contact behavior detection was developed, and we recorded >250,000 data points of close contact behaviors of students from grades 1 to 12. Combined with a survey on students' behaviors, we analyzed virus transmission in classrooms. Close contact rates for students were 37 +/- 11% during classes and 48 +/- 13% during breaks. Students in lower grades had higher close contact rates and virus transmission potential. The long-range airborne transmission route is dominant, accounting for 90 +/- 3.6% and 75 +/- 7.7% with and without mask wearing, respectively. During breaks, the short-range airborne route became more important, contributing 48 +/- 3.1% in grades 1 to 9 (without wearing masks). Ventilation alone cannot always meet the demands of COVID-19 control; 30 m(3)/h/person is suggested as the threshold outdoor air ventilation rate in a classroom. This study provides scientific support for COVID-19 prevention and control in classrooms, and our proposed human behavior detection and analysis methods offer a powerful tool to understand virus transmission characteristics and can be employed in various indoor environments.

Keyword:

COVID-19 ventilation children health close contact behavior school pandemic prevention

Author Community:

  • [ 1 ] [Guo, Yong]Tsinghua Univ, Dept Bldg Sci, Beijing 100084, Peoples R China
  • [ 2 ] [Bovell-Benjamin, Adelia]Tsinghua Univ, Dept Bldg Sci, Beijing 100084, Peoples R China
  • [ 3 ] [Guo, Yong]Beijing Key Lab Indoor Air Qual Evaluat & Control, Beijing 100084, Peoples R China
  • [ 4 ] [Bovell-Benjamin, Adelia]Beijing Key Lab Indoor Air Qual Evaluat & Control, Beijing 100084, Peoples R China
  • [ 5 ] [Dou, Zhiyang]Univ Hong Kong, Dept Comp Sci, Beijing 999077, Peoples R China
  • [ 6 ] [Zhang, Nan]Beijing Univ Technol, Beijing Key Lab Green Built Environm & Energy Effi, Beijing 100124, Peoples R China
  • [ 7 ] [Liu, Xiyue]Beijing Univ Technol, Beijing Key Lab Green Built Environm & Energy Effi, Beijing 100124, Peoples R China
  • [ 8 ] [Su, Boni]Clean Energy Res Inst, China Elect Power Planning & Engn Inst, Beijing 100120, Peoples R China
  • [ 9 ] [Li, Yuguo]Univ Hong Kong, Dept Mech Engn, Hong Kong 999077, Peoples R China

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Source :

PNAS NEXUS

Year: 2023

Issue: 5

Volume: 2

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 19

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 7

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