TY - CHAP
AU - Cao, Ruifeng
AU - Cheung, Clara
AU - Yunusa-Kaltungo, Akilu
AU - Manu, Patrick
AU - Liu, Chunxue
AU - Qiao, Qingyao
PY - 2023
DA - September
TI - The transmission of COVID-19 in construction: a systematic review of findings from statistical and modelling techniques
EP - 27
DO - https://doi.org/10.1201/9781003278368-3
AB - The COVID-19 outbreak experienced in late 2019 continues to impact work patterns significantly. The construction sector has been severely affected because of the labour-intensiveness of typical construction sites. Recent studies, particularly in epidemiology, have shown how different modelling techniques can predict the spread of COVID-19. However, the approach for selecting the most appropriate modelling method for workers’ health and safety in the construction industry against COVID-19 is still unclear. This study aims to develop an awareness of how the construction industry could use the simulation and modelling techniques to manage the transmission risks at construction sites. A systematic literature review based on the PRISMA guidelines has been conducted focusing on modelling applications in the spread of COVID-19 in construction, with specific interest in the modelling purposes and limitations to guide researchers to select appropriate modelling techniques. Research identified five commonly used simulation and modelling techniques in construction industry for COVID-19 transmission: 1) SIR/SEIR modelling, 2) Agent-based modelling, 3) Statistical modelling, 4) Building information modelling, and 5) Artificial Intelligence approach. Nineteen relevant articles were included in the detailed review based on criteria, and a comprehensive comparison of these five modelling techniques was produced. The review reveals that each of these techniques have their unique characteristics/strengths that make them useful for modelling COVID-19 transmission risk in construction under various scenarios or stages of the pandemic.
PB - Taylor and Francis
UR - https://uws.pure.elsevier.com/en/publications/98690468-a8db-4d09-a9f6-413fcfbd91a4
KW - Coronavirus (COVID-19)
KW - Work and workforce
ER