Implementation of the paper "Differentiable Agent-based Epidemiology."
Authors: Ayush Chopra*, Alexander Rodríguez*, Jayakumar Subramanian, Arnau Quera-Bofarull, Balaji Krishnamurthy, B. Aditya Prakash, Ramesh Raskar
*Equal contribution
Venue: AAMAS 2023 and preliminary version in AI4ABM Workshop @ ICML 2022 (best paper award)
Paper + appendix: http://arxiv.org/abs/2207.09714
Use the package manager conda to install required Python dependencies. Note: We used Python 3.7.
conda env create -f enviroment.yml
The following command will train and predict for all regions from epidemic week 202036 (GPU):
python -u main.py -st MA -j -d 0 1 2 3 -ew 202036 --seed 1234 -m GradABM-time-varying -di COVID
where -st
is the US state (joint model for counties in the state ), -j
is joint training, -d
are the GPU devices to be used, -ew
is the epidemic week and di
is disease (either COVID or Flu).
For running this in multiple weeks, see examples in Scripts/run.sh
.
For CPU, you want to use:
python -u main.py -st MA -j -d cpu -ew 202036 --seed 1234 -m GradABM-time-varying -di COVID
If you have any questions about the code, please contact Alexander Rodriguez at arodriguezc[at]gatech[dot]edu and Ayush Chopra ayushc[at]mit[dot]edu
If you find our work useful, please cite our work:
- Ayush Chopra, Alexander Rodríguez, Jayakumar Subramanian, Arnau Quera-Bofarull, Balaji Krishnamurthy, B. Aditya Prakash, Ramesh Raskar. 2023. Differentiable Agent-based Epidemiology. In International Conference on Autonomous Agents and Multiagent Systems.
@inproceedings{chopra2023differentiable,
title={Differentiable Agent-based Epidemiology},
author={Chopra, Ayush and Rodr'\iguez, Alexander and Subramanian, Jayakumar and Quera-Bofarull, Arnau and Krishnamurthy, Balaji and Prakash, B Aditya and Raskar, Ramesh},
booktitle={International Conference on Autonomous Agents and Multiagent Systems},
year={2023}
}