COVID-19 Simulation using Markov Chains – Academic Assignment
Simulated the spread of COVID-19 across countries and age groups using a Markov chain model, visualizing infection dynamics through demographic-based data analysis.
Technologies Used
Overview This academic project simulates the progression of the COVID-19 pandemic using a Markov chain-based probabilistic model. Developed as part of coursework at Blekinge Institute of Technology, the project models infection dynamics across countries and age groups, providing time-series visualizations to observe state transitions such as infection, recovery, immunity, and death.
Key Features
Simulates COVID-19 spread using a Markov chain for each individual in a population
Models transitions between health states: Healthy → Infected → Immune or Deceased
Demographic-specific probabilities for different age groups
Generates population samples and daily state distributions for selected countries
Visualizes the progression of infection using custom time-series plots
Technical Highlights
Time-stepped simulation of infection spread using country-specific population data
Modular code structure for extensibility with custom scenarios and input datasets
Matplotlib-based visualizations for per-country infection curves and outcomes
Pandas used for efficient data aggregation and transformation across timeframes
Use Case & Educational Impact This project provides a sandbox for exploring infectious disease dynamics and evaluating how demographic and probabilistic factors influence pandemic outcomes. It is suitable for academic learning, simulation modeling, or as a foundational tool for more advanced epidemiological studies.