Uncovering Construction Site-Specific Transmission Patterns of COVID-19: A Spatiotemporal Connectivity Analysis in Hong Kong

Ziyue Yuan, Shi Zhao, Shu Chien Hsu, Clara Man Cheung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

To adapt to the prolonged pandemic, the construction industry, which has a high vulnerability to coronavirus disease 2019 (COVID-19) infection, has sought more sector-specific and individual-level nonpharmaceutical interventions (NPIs). Understanding infection transmission patterns can determine what, when, and how NPIs should be implemented. This study examined infection transmission proceeding from construction sites using spatiotemporal analysis with COVID-19 case cluster data from construction sites in Hong Kong. The study revealed that COVID-19 transmission diffuses from the workplace to residential neighborhoods where infected construction workers live but not to the surroundings of infected construction sites. The average number of offspring cases infected by each seed case in the first to fifth transmission generations were 7.8, 26.1, 10.6, 3.6, and 1.3, respectively. Around 18% of cases were responsible for 79.6% of all COVID-19 transmission, driven mainly by workplace and household settings. The study found that closing a workplace within two working days after a primary case is identified can help reduce the attack rate by 5.33%. Encouraging household members of infected construction workers to follow quarantines can reduce offspring cases by 15.84% on average. A priori identification of superspreaders can help remove half of COVID-19 cases.

Original languageEnglish
Article number04022067
JournalJournal of Management in Engineering
Volume39
Issue number1
DOIs
Publication statusPublished - 1 Jan 2023

Keywords

  • Construction site
  • COVID-19
  • Hong Kong
  • Transmission pattern

ASJC Scopus subject areas

  • Building and Construction
  • Industrial relations
  • Engineering(all)
  • Strategy and Management
  • Management Science and Operations Research

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