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Infection Spread and High-Resolution Detection of Close Contact Behaviors.

Nan ZhangBoni SuPak-To ChanTe MiaoPeihua WangYuguo Li
Published in: International journal of environmental research and public health (2020)
Knowledge of human behaviors is important for improving indoor-environment design, building-energy efficiency, and productivity, and for studies of infection spread. However, such data are lacking. In this study, we designed a device for detecting and recording, second by second, the 3D indoor positioning and head and body motions of each graduate student in an office. From more than 400 person hours of data. Students spent 92.2%, 4.1%, 2.9%, and 0.8% of their time in their own office cubicles, other office cubicles, aisles, and areas near public facilities, respectively. They spent 9.7% of time in close contact, and each student averagely had 4.0 close contacts/h. Students spent long time on close contact in the office which may lead to high infection risk. The average interpersonal distance during close contact was 0.81 m. When sitting, students preferred small relative face orientation angle. Pairs of standing students preferred a face-to-face orientation during close contact which means this pattern had a lower infection risk via close contact. Probability of close contact decreased exponentially with the increasing distance between two students' cubicles. Data on human behaviour during close contact is helpful for infection risk analysis and infection control and prevention.
Keyphrases
  • high school
  • high resolution
  • endothelial cells
  • healthcare
  • big data
  • climate change
  • machine learning
  • risk assessment
  • data analysis
  • optical coherence tomography
  • drinking water
  • real time pcr