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An agent-based simulation model integrates multiple datasets to generate artificial populations for studying self-protective behaviors during the COVID-19 pandemic. The 9.5 KB dataset, authored by Geonsik Yu and last updated in May 2026, was calibrated against observed patterns and includes simulation experiments under varying conditions like non-pharmaceutical interventions and vaccine policies. Results suggest decision-making patterns vary across demographic groups and interactions between behaviors and external influences affect disease dynamics.
Data is in XLS (Excel) format, requiring compatible software to open. The 9.5 KB size indicates a very limited scope, likely containing summary or parameter data rather than extensive simulation outputs.