/STAN

STAN: spatio-temporal attention network for pandemic prediction using real-world evidence

Primary LanguageJupyter Notebook

STAN

The source code for STAN: Spatio-Temporal Attention Network for Pandemic Prediction Using Real World Evidence

Requirements

  • Install python, pytorch. We use Python 3.7.3, Pytorch 1.1.
  • Install dgl (if you use CUDA, please install cuda version of dgl), epiweeks, haversine.
  • If you plan to use GPU computation, install CUDA.

Train STAN with public JHU data

We provide a sample code in train_stan.ipynb jupyter notebook file to train STAN with publicly available COVID statistics. The data will be downloaded from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19).

In the notebook file, you can specify the date range in

GenerateTrainingData().download_jhu_data('2020-05-01', '2020-12-01')

You can modify the detailed data settings. valid_window and test_window indicates the date range used from validation and testing. history_window, pred_window and slide_step indicate sliding window settings for data inputs and prediction outputs.

For hyperparameters of the STAN model, gru_dim indicates the GRU hidden dimension. num_heads indicates the number of graph attention heads. hidden_dim indicates the hidden dimension of the GAT layer.

Once finished training, you can get the estimated $\beta$ and $\gamma$ of the SIR equation used in our model by using model.alpha_scaled and model.beta_scaled.