Alternative Scenario Table: Agent-Based Simulation of COVID-19 Behavioral Responses

A 5.5 KB Excel file containing simulation results from an agent-based model of public health interventions during the COVID-19 pandemic. The dataset, authored by Geonsik Yu and last updated in May 2026, presents alternative scenarios exploring how individual behaviors like vaccination and mask-wearing interact with policy decisions. The model was calibrated against observed patterns and integrates multiple datasets to generate artificial populations for simulation.

Use Cases

• Calibrating agent-based models based on simulated behavioral responses to non-pharmaceutical interventions (NPIs).
• Analyzing the impact of heterogeneous public messaging on mask-wearing and vaccination uptake within a simulated population.
• Evaluating the effects of alternative vaccine eligibility policies on infection burdens among different demographic groups.
• Studying the interaction between individual risk perception and collective disease dynamics under varying intervention conditions.

Strengths

• Dataset is openly licensed under CC-BY-4.0, permitting broad reuse.
• Model was calibrated against selected observed patterns from the COVID-19 pandemic.
• Simulation experiments explore multiple factors including NPIs, messaging, and vaccine policies.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Findings are contingent on the specific model structure and assumptions used.

Provenance

Source

figshare

Collection Method

Generated from an agent-based simulation model that integrated multiple datasets to create artificial populations.

Time Range

Simulates scenarios related to the COVID-19 pandemic period.

Freshness

Last updated 2026-05-08 17:48:05; freshness should be verified.