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

Test

Solve the evaluation task(s) for any of the other projects in the HumanAI umbrella organization. Please send us your CV and a link to all your completed work (github repo, Jupyter notebook + pdf of Jupyter notebook with output) to human-ai@cern.ch with Evaluation Test: SIRA in the title. In the email specify which evaluation test(s) you solved.

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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