Spatio-Temporal Neural Network for Fitting and Forecasting COVID-19
This work addresses forecasting the COVID-19 pandemic for public health planning, but it is incremental as it builds on existing neural network architectures.
The authors tackled the problem of forecasting the global spread of COVID-19 in 2020 by developing a Spatio-Temporal Neural Network (STNN) and its variants, which outperformed classical epidemic prediction models by providing more accurate fitting and prediction.
We established a Spatio-Temporal Neural Network, namely STNN, to forecast the spread of the coronavirus COVID-19 outbreak worldwide in 2020. The basic structure of STNN is similar to the Recurrent Neural Network (RNN) incorporating with not only temporal data but also spatial features. Two improved STNN architectures, namely the STNN with Augmented Spatial States (STNN-A) and the STNN with Input Gate (STNN-I), are proposed, which ensure more predictability and flexibility. STNN and its variants can be trained using Stochastic Gradient Descent (SGD) algorithm and its improved variants (e.g., Adam, AdaGrad and RMSProp). Our STNN models are compared with several classical epidemic prediction models, including the fully-connected neural network (BPNN), and the recurrent neural network (RNN), the classical curve fitting models, as well as the SEIR dynamical system model. Numerical simulations demonstrate that STNN models outperform many others by providing more accurate fitting and prediction, and by handling both spatial and temporal data.