  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.
