Code for "Improving Policy-Oriented Agent-Based Modeling with History Matching: A Case Study"

This repository includes the code for reproducing the results in the above-mentioned manuscript by O'Gara, Kerr, Klein, Binois, Garnett, and Hammond, now published in Epidemics:

@article{ogara2025improving,
  title={Improving Policy-Oriented Agent-Based Modeling with History Matching: A Case Study},
  author={O’Gara, David and Kerr, Cliff C and Klein, Daniel J and Binois, Micka{\"e}l and Garnett, Roman and Hammond, Ross A},
  journal={Epidemics},
  pages={100845},
  year={2025},
  publisher={Elsevier}
}

Organization

The repository is based off of the one in the original work, see here:

https://github.com/amath-idm/controlling-covid19-ttq

It is organized as follows:

• hetGPy-calibration contains the analysis code to calibrate the model as described in the manuscript.

It also relies on modules originally from Kerr et. al 2021, which are:

• fig1_calibration and fig5_projections are the main folders containing the code for reproducing each figure of the manuscript.
• inputs and outputs are folders containing the input data and the model-based outputs, respectively.

Note that these analyses create large data files, which cannot be uploaded to github. These data are archived via Zenodo: 10.5281/zenodo.14574663

Installation and usage

Use pip install -r requirements.txt to install dependencies. A Docker image (used for the simulations in the paper) is available here: https://hub.docker.com/r/dogara/covasim-py310

Running the History Matching Rounds

• See the run_HM_round.sh file in this directory for a sample of how to run the history matching rounds. The two inputs to the python script hetGPy-calibration/run_HM.py are:
  • r the round number
  • n whether to run and save new simulations (defaults to True)
• We recommend running the simulations on a computing cluster.