Infectious disease modelling aimed at demonstrating infection patterns for learning purposes, especially with COVID, can be overly simplistic in their modeling of how people in a small social network (like a college campus) interact. Using a small-world network model based on hyperbolic random graphs, we aimed to show how realistic social networks (<6 degrees of separation) change how a disease can spread.

What it does

Models the spread of an infectious disease among a realistic social network.

How we built it

Java back-end application and GUI. Used jgrapht as initial reference to design graph Object structure.

Challenges we ran into

Deciding on platform to use (especially when it came to UI), complexity of hyperbolic network topic.

Accomplishments that we're proud of

Functional model visually similar to the paper, intuitive UI.

What we learned

Hyperbolic geometry and networks, disease spread modelling.

What's next for Small-world COVID

  • Relating vertices to individuals for more realistic simulations
  • Setting individual vertex parameters (e.g. social distancing, mask wearing) instead of all vertices sharing parameters.

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