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[AAAI-21] Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19

Publication

Implementation of the paper "Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19" published in AAAI-21.

Authors: Alexander Rodríguez, Nikhil Muralidhar, Bijaya Adhikari, Anika Tabassum, Naren Ramakrishnan, B. Aditya Prakash

Venue: AAAI Conference on Artificial Intelligence (AAAI-21)

Pre-print: https://arxiv.org/abs/2009.11407

Appendix: LINK

Requirements

Use the package manager conda to install required Python dependencies. Note: We used Python 3.7.

conda env create -f requirements.yml

Training

The following command will train and predict for all regions from epidemic week 9 to 15:

python ./main.py --start_week 9 --end_week 15

You can set up your own model hyperparameter values (e.g. learning rate, loss weights) in the file ./experiment_setup/feature_module/model_specifications/global_recurrent_feature_model.json.

Evaluation

To evaluate the results, go to evaluate.py and change line 71 for the name of results file (saved in folder rmse_results). Then, run.

python ./evaluate.py

Contact:

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

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cali-net's Issues

question about the symbol in the paper

Hi, thank you for your interesting work.
Here I'm confused with the symbol in the paper.
source predictions (yˆs) and the ground truth (yˆt) of imitation loss from KD loss
but another y − yˆs is also the error between source predictions and ground truth

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