Multi-hazard hospital evacuation planning during disease outbreaks using agent-based modeling.

Area of Science:

• Disaster Risk Management
• Computational Modeling
• Public Health Preparedness

Background:

• Simultaneous natural and man-made hazards require integrated risk management.
• The COVID-19 pandemic complicates regular hospital operations and emergency planning.
• Patient evacuation, especially for those with mobility disabilities, is complex and requires robust protocols.

Purpose of the Study:

• To adapt emergency evacuation plans for multi-hazard scenarios, specifically during the COVID-19 pandemic.
• To develop a non-intensive care unit (ICU) patient classification framework based on mobility data.
• To simulate and analyze emergency evacuation strategies in a hospital setting.

Main Methods:

• Development of a non-ICU patient classification framework using mobility data.
• Agent-based modeling to simulate emergency evacuation from a hospital emergency department.
• Scenario analysis including a fire emergency during the COVID-19 pandemic.

Main Results:

• A larger nursing team reduced median evacuation time by 36% and the upper bound by 33%.
• A dedicated exit for COVID-19 patients had a less significant impact on median evacuation time but reduced the upper bound by over 50%.
• Computational models offer insights into optimizing evacuation strategies and resource allocation.

Conclusions:

• Adapting emergency plans for multi-hazard scenarios, including infectious disease outbreaks, is essential for hospital resilience.
• Patient classification and simulation modeling are valuable tools for improving emergency preparedness and evacuation efficiency.
• Resource allocation, such as staffing levels and exit strategies, significantly impacts evacuation outcomes.