chaffim/covid19_spread

Modeling the spread of COVID-19

Neural Relational Autoregression
for High-Resolution COVID-19 Forecasting

This library provides code to forecast COVID-19 cases. Details regarding the methods can be found in our paper

Install Dependencies

make env  # in the covid19_spread folder
conda activate covid19_spread

Install library/scripts

pip install --editable .

Preparing Data

Data for each region lives in covid19_spread/data/<region>. Each dataset is built using the prepare-data command the gets installed by the above pip install. For example, to prepare the U.S. data, simply run:

prepare-data us --with-features

The --with-features flag indicates to also assemble the covariate time feature data

Train bAR Model on US data

To train b-AR using the cross validation pipeline on the latest data python cv.py cfg/us.yml bar.

To execute jobs in parallel on a SLURM cluster, run add the -remote flag, i.e., python cv.py cfg/us.yml bar -remote -array-parallelism 20 runs cross-valiation on 20 SLURM nodes in parallel.

To modify the model parameters, change the corresponding field in cv/us.yml.

Back-Test bAR Model on past dates

To train b-AR using the cross validation pipeline on past dates run python cv.py backfill cfg/us.yml bar. The -remote flag executes the backfill again on a SLURM cluster.

Dates are specified using the command line -dates flag. For example:

python cv.py backfill cfg/us.yml bar -dates 2020-11-01 -dates 2020-12-01

Will run a backfill for both 11/01/2020 and 12/01/2020

References

If you are using this code in your work, please cite

@techreport{le2020covid19,
     title = {{Neural Relational Autoregression for High-Resolution COVID-19 Forecasting}},
     author = {Matthew Le and Mark Ibrahim and Levent Sagun and Timothee Lacroix and Maximilian Nickel},
     url = {https://ai.facebook.com/research/publications/neural-relational-autoregression-for-high-resolution-covid-19-forecasting/},
     month = {10},
     Date-Added = {2020-10-01},
     year = {2020}
}

Forecasts

We provide continuous forecasts for all counties in the United States via the Humanitarian Data Exchange. Past forecasts of our model are also available through the COVID-19 Forecast Hub.

Tests

To run tests:

python -m pytest tests/

To exclude integration tests:

python -m pytest tests/ -v -m "not integration" 

Lint

We have Circle CI setup which will run our test suite as well as run the black linter.
Please ensure that all code pushed passes the lint check. You can install a Git pre-commit hook with:

pre-commit install

License

This work is licensed under CC BY-NC. See LICENSE for details