Description

This project aims to use machine learning to deduce the deterministic form of the classic susceptible-infected-removed (SIR) epidemic model, defined by a set of ordinary differential equations, from a large number of synthetic epidemics, each simulated at a different SIR parameter point using the stochastic version of the SIR model.

Duration

Total project length: 175 hours

Task ideas

• Starting with the stochastic equations, simulate an Epidemic based on the SIR model.
• Train a ML model to predict the mean counts S, I, R
• Use auto-differentiation and symbolic methods to approximate S(t), I(t), R(t)

Expected results

• Simulated SIR epidemic model
• Trained machine learning model to predict the mean counts
• Trained symbolic ML model to approximate the output

Requirements

Python, Pytorch or Tensorflow, upper-level mathematics such as differential equations and linear algebra preferred

Project difficulty level

Medium

Mentors

• Harrison Prosper (Florida State University)
• Olivia Prosper (University of Tennessee)
• Sergei Gleyzer (University of Alabama)

Please DO NOT contact mentors directly by email. Instead, please email human-ai@cern.ch with Project Title and include your CV and test results. The mentors will then get in touch with you.

Corresponding Project

• SIRA

Participating Organizations

• FSU
• Tennessee

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