A work of Mark Bartos made for the Year 2 class of Project Socially Aware Computing.
Goal Simulate COVID-19 transmission and investigate how early application of preventative measures (mask-wearing, staying at home, vaccination) impacts the virus spread.
Research Question How does the combination of different strategies and their early applicability impact the spread of the SARS-CoV-2 virus in a simulated population?
Experiments The model will be run 40 times with different combinations of prevention strategies. Results will be analyzed to determine the most effective measures.
Conclusion Combining preventative strategies is the most effective way to slow down the spread of COVID-19. Future work could explore multiple waves of infections and the impact of delayed measures.
💡 The code-base and research paper is made avaliable for grading purposes.
This NetLogo simulation model studies COVID-19 spread and prevention strategies. It tracks a population of turtles representing individuals, their infection status, and immunity.
Global Variables:
- countNonInfected: Count of non-infected turtles.
- countInfected: Count of infected turtles.
- countImmune: Count of immune turtles.
Procedures and Functions:
- setup: Initializes the simulation with infected and non-infected turtles.
- go: Main simulation loop that handles infection, recovery, and movement of turtles.
- infect: Handles turtle infection based on policies (mask and vaccination).
- recover: Manages turtle recovery process.
- percNonInfected, percInfected, and percImmune: Report the percentage of non-infected, infected, and immune turtles.
Simulation Behavior:
- Runs until no infected turtles remain.
- Turtles move randomly with different distances depending on quarantine policy.
- Infection depends on vaccination and mask policies.
Data Collection:
- Tracks percentages of non-infected, infected, and immune turtles over time.
⚫️ Black line: Non-Infected
🔴 Red line: Infected
🟢 Green line: Recovered
