  The outbreaks of Coronavirus Disease 2019 (COVID-19) have impacted the world
significantly. Modeling the trend of infection and real-time forecasting of
cases can help decision making and control of the disease spread. However,
data-driven methods such as recurrent neural networks (RNN) can perform poorly
due to limited daily samples in time. In this work, we develop an integrated
spatiotemporal model based on the epidemic differential equations (SIR) and
RNN. The former after simplification and discretization is a compact model of
temporal infection trend of a region while the latter models the effect of
nearest neighboring regions. The latter captures latent spatial information.
%that is not publicly reported. We trained and tested our model on COVID-19
data in Italy, and show that it out-performs existing temporal models (fully
connected NN, SIR, ARIMA) in 1-day, 3-day, and 1-week ahead forecasting
especially in the regime of limited training data.
