COVID-ABS

COVID-ABS simulates COVID-19 pandemic dynamics using an agent-based SEIR framework to evaluate health and economic impacts of social-distancing scenarios.

Key Features:

• Agent-Based Modeling: Models interactions among agents representing individuals, businesses, and government entities to capture heterogeneous behaviors and policy effects.
• SEIR Framework: Implements a Susceptible-Exposed-Infected-Recovered (SEIR) compartmental structure to track disease progression at the individual-agent level.
• Scenario Analysis: Evaluates seven social-distancing scenarios: Do nothing; Lockdown; Conditional lockdown; Vertical isolation; Partial isolation; Use of face masks; Combined use of face masks and 50% adherence to social isolation.
• Health and Economic Impact Assessment: Quantifies epidemiological outcomes (e.g., number of deaths) and economic effects to assess trade-offs between public health benefits and economic costs.
• Implementation and Extensibility: Implemented in Python and parameterized so input parameters can be modified to adapt the model to different societal contexts.

Scientific Applications:

• Policy Evaluation: Compares intervention strategies and their epidemiological and economic consequences to inform public-health policy choices.
• Strategic Planning: Projects epidemic trajectories under alternative social-distancing scenarios to support planning of interventions.
• Regional and Population Adaptation: Allows adaptation via parameter changes to explore outcomes for different regions and population structures.

Methodology:

Agent-based simulations integrate a SEIR compartmental model with agents representing individuals, businesses, and government entities, evaluating seven predefined social-distancing scenarios; implementation is in Python.