Background:

Take a simple undirected graph where nodes represent individuals and edges represent the contacts between them. On this graph we consider a SI (susceptible-infected) epidemic process. I.e., each node can be in a susceptible or infected state and nodes transition from the susceptible into the infected state if they get infected by a neighbour.

The goal of this project is to create a simulator of this process depending on the underlying structure and compare the infection model to more realistic real-world simulators (e.g. https://www.nature.com/articles/s41591-020-1036-8.pdf).

Suggested steps:

• Implement a simulator for the spreading process on a synthetic graph model

• Test implementation on real world graph

• Adapt published Covid simulator to problem parameters

• Compare published simulator to theoretical simulator

Requirements:

• Familiarity with random graph models, probability, sampling techniques

• Strong programming skills & good code organisation/documentation/maintainability

• Language: python or take this project as a chance to learn a new language

• For published simulators, you will need to familiarise yourself with R