Enhancing resilience in construction against infectious diseases using stochastic multi-agent approach

Area of Science:

• Epidemiology
• Construction Management
• Computational Modeling

Background:

• The COVID-19 pandemic highlighted vulnerabilities in the construction industry.
• A lack of micro-level simulation frameworks hinders effective pandemic response in construction projects.
• Construction workers face higher risks of infectious disease spread and fatalities.

Purpose of the Study:

• To develop a simulation framework for modeling infectious disease spread in construction projects.
• To enable data-informed decision-making for interventions against infectious diseases in construction.
• To enhance the resilience of the construction industry against future pandemics.

Main Methods:

• Stochastic agent-based modeling (ABM) was employed to simulate disease transmission dynamics.
• The framework allows for the testing of various intervention strategies at a micro-level.
• A case study of a building project was used to validate the model's findings.

Main Results:

• Infectious diseases can spread more rapidly among construction workers compared to the general population.
• The simulation indicated potentially significantly higher fatality rates within construction project settings.
• The developed framework effectively simulates disease spread and intervention impacts.

Conclusions:

• The proposed agent-based modeling framework addresses the gap in micro-level infectious disease simulation for construction.
• The findings underscore the heightened risk of infectious diseases in construction projects, necessitating targeted interventions.
• This research encourages further investigation into micro-level disease modeling and intervention strategies within the construction sector.