How vorlon83/extending-multilayerabm-covid-19-patras works
This is a Python/Mesa research package for simulating COVID-19 in a synthetic Patras population and producing paper results. The main workflow runs intervention scenarios through the SEIRD model, whose agents interact across seven social layers; repeated runs are summarized and visualized. Separate scripts provide audits and exploratory analyses, including transmission-tree measures, hospitalization, network statistics, and layer ablations. The graph follows documented and indexed relationships; unsampled script internals are not assumed to call one another.
Main entry points: model.py SEIRD model · run_simulation.py Simulation CLI · synthetic_population.py Synthetic population · run_experiments.py Scenario experiments
Components 16
Population and simulation
- Simulation CLI
run_simulation.py - SEIRD model
model.py - Synthetic population
synthetic_population.py - Person agent
agent.py
Paper experiments
- Scenario experiments
run_experiments.py - Church sensitivity
church_sensitivity.py - Layer ablation
ablation_study.py - Age-weighted church
church_weighted_comparison.py
Analyses and checks
- Transmission analysis
transmission_analysis.py - Hospitalization analysis
hospitalization_analysis.py - Network statistics
network_stats.py - Extra replications
extra_replications.py - Simulation audit
audit.py
Results and figures
- Publication figures
nice_figures.py - Archived results
results
Other components
- Researcher
Connections 19
- Researcher → Simulation CLI: runs scenarios
README.md - Simulation CLI → SEIRD model: runs model
run_simulation.py - SEIRD model → Synthetic population: generates population
model.py - SEIRD model → Person agent: creates agents
model.py - Person agent → SEIRD model: reads model state
agent.py - Scenario experiments → Simulation CLI: runs scenarios
run_experiments.py - Scenario experiments → Archived results: writes summaries
README.md - Publication figures → Simulation CLI: averages runs
nice_figures.py - Publication figures → Archived results: writes figures
README.md - Church sensitivity → Simulation CLI: runs scenarios
church_sensitivity.py - Layer ablation → SEIRD model: runs variants
ablation_study.py - Age-weighted church → Synthetic population: compares assignments
church_weighted_comparison.py - Age-weighted church → SEIRD model: runs comparison
church_weighted_comparison.py - Transmission analysis → SEIRD model: analyzes transmissions
transmission_analysis.py - Hospitalization analysis → Simulation CLI: analyzes runs
hospitalization_analysis.py - Network statistics → Synthetic population: analyzes layers
network_stats.py - Extra replications → Simulation CLI: runs scenarios
extra_replications.py - Simulation audit → Simulation CLI: checks runs
audit.py - Simulation audit → SEIRD model: checks model
audit.py