Gerami Seresht Nima
Department of Mechanical and Construction Engineering, Northumbria University, Newcastle Upon Tyne NE1 8ST, United Kingdom.
Autom Constr. 2022 Aug;140:104315. doi: 10.1016/j.autcon.2022.104315. Epub 2022 May 11.
To recover from the adverse impacts of COVID-19 on construction and to avoid further losses to the industry in future pandemics, the resilience of construction industry needs to be enhanced against infectious diseases. Currently, there is a gap for modelling frameworks to simulate the spread of infectious diseases in construction projects at micro-level and to test interventions' effectiveness for data-informed decision-making. Here, this gap is addressed by developing a simulation framework using stochastic agent-based modelling, which enables construction researchers and practitioners to simulate and limit the spread of infectious diseases in construction projects. This is specifically important, since the results of a building project case-study reveals that, in comparison to the general population, infectious diseases may spread faster among construction workers and fatalities can be significantly higher. The proposed framework motivates future research on micro-level modelling of infectious diseases and efforts for intervening the spread of diseases in construction projects.
为了从新冠疫情对建筑业的不利影响中恢复过来,并避免未来疫情对该行业造成进一步损失,建筑业需要增强抵御传染病的能力。目前,在微观层面模拟传染病在建设项目中的传播以及测试干预措施有效性以进行数据驱动决策的建模框架存在空白。在此,通过使用基于随机代理的建模开发一个模拟框架来填补这一空白,这使建筑研究人员和从业人员能够模拟并限制传染病在建设项目中的传播。这一点尤为重要,因为一个建筑项目案例研究的结果显示,与普通人群相比,传染病在建筑工人中可能传播得更快,且死亡人数可能显著更高。所提出的框架推动了未来关于传染病微观层面建模的研究以及在建设项目中干预疾病传播的努力。