School Cafeteria Disease Spread Simulation

ISEF Category: Translational Medical Science

Subcategory: Disease Prevention  ·  Difficulty: Intermediate  ·  Setup: Home Setup  ·  Time: 1 to 2 Months

The Hook

A lunchroom can act like a tiny city. One crowded path can send people past many classmates in minutes. That makes cafeteria design a real health problem, not just a traffic problem. You can model it with code and turn layout choices into measurable risk.

What Is It?

This project uses an agent-based model, which means you build many simple digital people and let them move through a lunchroom. Each agent follows rules, like choosing a seat, walking to a table, or passing other agents in the serving line. You then watch how often agents come close enough for transmission risk to rise. Think of it like a video game where the goal is not points, but safer design.

You can test seating charts, aisle widths, entry points, and lunch line routes. You can also compare different assumptions about how often people move, talk, or sit near the same classmates. Because you calibrate the model with published flu or RSV R0 values, your simulation stays tied to real disease spread data. R0, pronounced R-naught, is a rough estimate of how many new infections one infected person can cause in a susceptible group.

Why This Is a Good Topic

This is a strong science fair topic because you can test many layout changes without needing a wet lab or human subjects. You get a real public health connection, since schools care about reducing respiratory disease spread with cheap changes. The project also teaches coding, modeling, data analysis, and how to compare simulation outputs to published epidemiology values. That mix can make your work look thoughtful and original.

Research Questions

• How does cafeteria seating density change the simulated number of close contacts per lunch period?
• What is the effect of one-way traffic flow on agent mixing near the serving line?
• Does staggered seating reduce repeated close contacts more than fixed cohort seating?
• To what extent do wider aisles lower transmission-risk encounters in the simulation?
• Which cafeteria layout produces the lowest estimated spread risk when calibrated to published flu R0 values?
• How does adding separate entry and exit paths change peak crowding near tables?

Basic Materials

• Laptop or desktop computer with internet access.
• Free Mesa or NetLogo software.
• Spreadsheet software such as Google Sheets or Excel for organizing outputs.
• PubMed access for finding flu and RSV studies.
• CDC or NIH reports on flu, RSV, and school transmission.
• Notebook or digital doc for assumptions, variables, and model rules.

Advanced Materials

• Laptop or desktop computer with internet access.
• Mesa Python package or NetLogo with BehaviorSpace.
• Python with pandas, matplotlib, and scipy for analysis.
• ImageJ or similar tool if you extract cafeteria layout dimensions from floor plan images.
• Published epidemiology papers with R0 estimates for flu and RSV.
• School floor plan data or measured cafeteria dimensions.
• Version control tool such as Git for tracking model changes.

Software & Tools

• Mesa: Builds agent-based simulations in Python and lets you define movement, contact rules, and outcomes.
• NetLogo: Lets you prototype agent behavior quickly and compare cafeteria layouts with repeatable runs.
• Python: Handles data cleaning, simulation output analysis, and plotting.
• Google Sheets: Helps you log assumptions, compare scenarios, and summarize results.
• PubMed: Helps you find review articles and original studies on flu, RSV, and transmission estimates.

Experiment Steps

1. Define the transmission question you want your model to answer, and choose one cafeteria feature to vary first.
2. Map the cafeteria as a simplified grid or floor plan, then decide how agents enter, move, sit, and leave.
3. Set contact rules that turn distance, crowding, or time near others into a risk score.
4. Calibrate the model with published flu or RSV R0 values so the output matches known disease behavior.
5. Run several layout scenarios with the same assumptions, then compare spread risk, crowding, and repeated contacts.
6. Check which assumptions change the results most, and use that sensitivity test to strengthen your conclusion.

Common Pitfalls

• Building a model with too many movement rules, which makes it hard to tell why one layout works better than another.
• Calibrating to an R0 value without checking whether the study population matches school-age students.
• Using a cafeteria map that is too abstract, which can hide traffic bottlenecks near the serving line and exits.
• Comparing layouts with different agent counts, which confounds density with design.
• Treating simulation output as exact infection counts instead of a relative risk estimate.

What Makes This Competitive

A strong version of this project goes beyond one simple layout comparison. You could test several real cafeteria designs, then use sensitivity analysis to show which design features matter most. You could also compare flu and RSV assumptions, since they do not spread the same way. Clear validation, careful controls, and honest limits will make the work look much stronger.

Project Variations

• Model different lunch schedules, such as staggered grade release versus one common lunch period.
• Compare cafeteria layouts for elementary, middle, and high school traffic patterns.
• Test whether masking assumptions or shorter dwell times change the ranking of seating designs.

Learn More

• NetLogo Models Library: Search for agent-based models and school or crowd movement examples in the NetLogo library and documentation.
• Mesa Documentation: Read the official Mesa docs for building agent-based models in Python.
• CDC Flu Information: Search the CDC site for flu transmission, school guidance, and seasonal data.
• NIH PubMed: Search for review articles on RSV transmission, influenza R0, and school-based spread.
• NOAA or USGS not needed here, so focus on public health sources, epidemiology papers, and university course notes on agent-based modeling.
• MIT OpenCourseWare: Search for course notes on computational modeling, simulation, or network science.