Capacity assessment using stochastic simulation.
The project aims to develop a method for modeling a health care region and optimizing disaster management capacity to respond to a mass casualty incident.
A novel simulation-based computer software system has been developed. The prototype system tracks each simulated patient and calculates preventable deaths depending on the health care organization’s capacity.
The system employs stochastic hybrid discrete-events and dynamic simulation to mimic realistic conditions such as time of day, waiting room crowding and weather conditions. The development of the system will now turn to validation against historic mass casualty incidents, implementation of modules for traffic modeling, and machine learning algorithms for developing agent-based decisions and strategies.
Contact: Erik Prytz