When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting

Paper Link: https://arxiv.org/abs/2106.03904

Setup

First install Anaconda. The dependencies are listed in environment.yml file. Make sure you make changes to version of cudatoolkit if applicable.

Then run the following commands:

conda env create --prefix ./envs/epifnp --file environment.yml
source activate ./envs/epifnp

Directory structure

-data
	- ILINet.csv -> wILI values for seasons 2003 to 2020 collected from flusight
- model_chkp -> stores intermediate model parameters while training
- models/fnpmodels.py -> implementation of EpiFNP modules
- plots -> plots of predictions
- saves -> saves predictions for models as pkl files
- train_ili.py -> training script for EpiFNP
- test_ili.py -> inference of trained model
- test_regress.py -> Autoregressive inference using a trained model

Training

Run:

python train_ili.py -y <test season> -w <week ahead> -a trans -n <experiment name> -e <max num. of epochs>

Or run run.py to run all experiments.

Prediction plots will be saved in plots/Test<experiment name>.png and model in model_chkp folder.

Inference

Run:

python test_ili.py -y <test season> -w <week ahead> -a trans -n <experiment name>

for normal inference.

Run:

python test_regress_ili.py -y <test season> -w <week ahead> -a trans -n <experiment name>

for auto-regressive inference. Note: Train and use a 1 week ahead model for AR inference.

The predictions and plots are saved in saves and plots respectively.