VTES

VTES simulates the spread of infectious diseases within human populations using a stochastic, Python-based model to analyze transmission dynamics and intervention effects.

Key Features:

• Stochastic Simulation: Employs stochastic modeling techniques to replicate the random nature of viral transmission events among individuals.
• Reduced Representation Simulations: Focuses on a few critical variables such as infectivity and population density to reduce model complexity.
• 2D Individual Representation: Represents individuals as circles on a bounded 2D plane to visualize interactions that can lead to transmission.
• Parameter Modulation: Allows adjustment of variables including social distancing and masking to explore intervention effects.
• R0 Calculation: Includes functionality to calculate the basic reproduction number (R0) for simulated outbreaks.
• Python Implementation: Implemented in Python as the computational environment for simulation and analysis.

Scientific Applications:

• Modeling Disease Spread: Simulates and analyzes propagation of pathogens such as SARS-CoV-2 through populations considering infectivity and population density.
• Intervention Testing: Evaluates the potential effectiveness of interventions like social distancing and masking on outbreak dynamics.
• Educational Illustration: Illustrates how changes in key parameters influence epidemic outcomes for teaching epidemiology and transmission dynamics.

Methodology:

Simulates person-to-person interactions within a confined 2D space where each individual is represented as a circle; infections occur through contact between circles and outbreak progression is tracked over time.