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http://repositorio.ufla.br/jspui/handle/1/43480
metadata.artigo.dc.title: | Modeling the spread of COVID-19 on construction workers: an agent-based approach |
metadata.artigo.dc.creator: | Araya, Felipe |
metadata.artigo.dc.subject: | Construction COVID-19 Agent-based modeling |
metadata.artigo.dc.publisher: | Elsevier |
metadata.artigo.dc.date.issued: | Jan-2021 |
metadata.artigo.dc.identifier.citation: | ARAYA, F. Modeling the spread of COVID-19 on construction workers: an agent-based approach. Safety Science, [S.l.], v. 133, Jan. 2021. |
metadata.artigo.dc.description.abstract: | As the spread of COVID-19 has continued since December 2019, stay at home orders around the globe have changed how we live our lives, mostly from physical to virtual interactions, such as going to college and doing our jobs; however, some activities are basically impossible to perform virtually, such as construction activities. Thus, the construction sector has been highly disrupted by the current pandemic. The construction sector represents a key component of countries’ economies—it is approximately 13% of global GDP—as such, having the availability to perform construction activities with a minimum spread of COVID-19 may help to the financial response to the pandemic. Given this context, this study aims to understand the potential impact of COVID-19 on construction workers using an agent-based modeling approach. Activities are classified as being of low-medium–high risk for workers, and the spread of COVID-19 is simulated among construction workers in a project. This study found that the workforce from a construction project may be reduced by 30% to 90% due to the spread of COVID-19. Understanding how COVID-19 may spread among construction workers may assist construction project managers in creating adequate conditions for workers to perform their job, minimizing the chances of getting infected with COVID-19. |
metadata.artigo.dc.identifier.uri: | https://www.sciencedirect.com/science/article/pii/S0925753520304197 http://repositorio.ufla.br/jspui/handle/1/43480 |
metadata.artigo.dc.language: | en_US |
Appears in Collections: | FCS - Artigos sobre Coronavirus Disease 2019 (COVID-19) |
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