Model-M

Model-M simulates COVID-19 transmission dynamics within a middle-sized municipality by representing 56,000 inhabitants and over 2.7 million social contacts in an agent-based, multi-layered social network.

Key Features:

• Agent-based simulation: Models individual-level interactions and transmission events among simulated agents.
• Population representation: Encodes a population of 56,000 inhabitants for municipality-scale analyses.
• Multi-layered social network: Incorporates over 2.7 million social contacts organized across multiple social layers.
• Extended SEIR model: Uses an extended Susceptible-Exposed-Infectious-Recovered framework as the core epidemiological model.
• Calibration: Calibration of model parameters is performed using real epidemiological data from the Czech Republic.
• Non-pharmaceutical interventions (individual-level): Simulates physical distancing, testing, contact tracing, isolation, and quarantine at the individual level.
• Non-pharmaceutical interventions (governmental-level): Simulates restrictions on social contacts in environments such as schools, restaurants, and other communal spaces.
• Epidemic indicator projections: Generates detailed projections of key epidemic indicators under varied intervention scenarios.
• Implementation: Implemented in Python for computational execution.

Scientific Applications:

• Intervention evaluation: Assess the effects of individual-level and governmental-level non-pharmaceutical interventions on transmission dynamics.
• Epidemic projection: Project key epidemic indicators under alternative intervention scenarios for municipality-scale planning.
• Transmission dynamics analysis: Explore how multi-layered social contacts drive COVID-19 spread in a middle-sized municipal population.

Methodology:

Agent-based simulations run on a multi-layered social contact network (56,000 agents, >2.7 million contacts) using an extended SEIR model calibrated to Czech Republic epidemiological data; implemented in Python.