Implementation of the paper "EINNs: Epidemiologically-informed Neural Networks" published in AAAI 2023.
Authors: Alexander Rodríguez, Jiaming Cui, Naren Ramakrishnan, Bijaya Adhikari, B. Aditya Prakash
Paper + appendix: https://arxiv.org/abs/2202.10446
Use the package manager conda to install required Python dependencies. Note: We used Python 3.7.
conda env create -f requirements.yml
The following command will train and predict for all regions from epidemic week 202036 to 202109:
python main.py --region AL --dev cpu --exp 400 --start_ew 202036 --end_ew 202109 --step 2
More examples can be found in run.sh
.
You can set up your own model hyperparameter values (e.g. learning rate, loss weights) in the file ./setup/EINN-params.json
.
If you have any questions about the code, please contact Alexander Rodriguez at arodriguezc[at]gatech[dot]edu and/or B. Aditya Prakash badityap[at]cc[dot]gatech[dot]edu
If you find our work useful, please cite our work:
- Alexander Rodríguez, Jiaming Cui, Naren Ramakrishnan, Bijaya Adhikari, B. Aditya Prakash. 2023. EINNs: Epidemiologically-informed Neural Networks. In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 37.
@inproceedings{rodriguez2022einns,
title={EINNs: Epidemiologically-Informed Neural Networks},
author={Rodr'\iguez, Alexander and Cui, Jiaming and Ramakrishnan, Naren and Adhikari, Bijaya and Prakash, B. Aditya},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={37},
year={2023}
}