Python Ireland November 2025
Modelling the COVID Pandemic in Ireland with Python

Summary
The presentation will introduce common differential models for epidemic models, focusing on their estimation using Bayesian techniques in Python.

The target audience includes individuals with some Python proficiency and an interest in epidemic modeling but limited prior experience.

Python Tools Three main libraries were used

• PyMC for estimation. These models are in the PYMC directory.
  
• CmdPyStan for estimates as an interface to the STAN language. These models are in the STAN directory, with STAN files in the STAN/STAN subdirectory and the notebook in Google Colab
  
• ArviZ for diagnostics
  

Data
COVID-19 data examples include:

• Influenza infection in a school in England, a common case-study for epidemic models
• A simulated SIR model, which is then re-estimated.
• Irish case data from February to November 2020 used in modelling by the Irish Epidemiological Modelling Advisory Group (IEMAG) models used by NPHET and the Irish government

Files

• Presentation files are given in Keynote, Powerpoint and PDF
  
• PYMC directory holds PyMC notebooks 1 to 4 are in IPYNB and static copies in HTML. Created and run in Conda on Windows 11 workstation.
  
• STAN directory holds STAN models. cmdpystan notebooks are in IPYNB and static copies also. Created and run in Windows Subsystem for Linux on Windows 11 workstation.
  
• Datasets are in the Notebook directories too
  
• Important papers used for data and methodology guidance are included in the directory Papers
  

Contact
Peter Nolan
databeaker@gmail.com
https://github.com/dpnolan/pandemic

I'm a Dublin-raised and Europe-based risk and data analyst and communicator specialising in financial, economic analysis. Consultant business and risk analyst, product and programme manager.
https://www.linkedin.com/in/peterpatricknolan