Fig 2. Overview of our model.

The model includes sequence-to-sequence combinations of a diffusion GCN and gated recurrent unit (GRU) with uncertainty estimation. We feed the historical time series of patient numbers into the encoder. Next, we use its final states to initialize the decoder. The decoder generates a prediction from previous ground-truths or the values predicted by the model using scheduled sampling. Additionally, our model applies the predicted values to our uncertainty estimation method and then outputs the prediction interval.