Parallel Simulation Decision-Making Method for a Response to Unconventional Public Health Emergencies Based on the Scenario–Response Paradigm and Discrete Event System Theory

This study presents a simulation-based decision tool for managing complex public health emergencies when little is known at the start. Instead of relying only on past data, the method builds a dynamic model of the emergency as it unfolds, updates it with real-time information, and tests response options in parallel. Using SARS as an example, the model identified bottlenecks in diagnosis and patient flow, then showed how adding key response units could sharply reduce waits and overcrowding. The main value for practice is speed: responders can use evolving scenario models to spot weak points early and adjust staffing, space, and process decisions before systems fail.