  In this paper, we propose a deep learning model to forecast the range of
increase in COVID-19 infected cases in future days and we present a novel
method to compute equidimensional representations of multivariate time series
and multivariate spatial time series data. Using this novel method, the
proposed model can both take in a large number of heterogeneous features, such
as census data, intra-county mobility, inter-county mobility, social distancing
data, past growth of infection, among others, and learn complex interactions
between these features. Using data collected from various sources, we estimate
the range of increase in infected cases seven days into the future for all U.S.
counties. In addition, we use the model to identify the most influential
features for prediction of the growth of infection. We also analyze pairs of
features and estimate the amount of observed second-order interaction between
them. Experiments show that the proposed model obtains satisfactory predictive
performance and fairly interpretable feature analysis results; hence, the
proposed model could complement the standard epidemiological models for
national-level surveillance of pandemics, such as COVID-19. The results and
findings obtained from the deep learning model could potentially inform
policymakers and researchers in devising effective mitigation and response
strategies. To fast-track further development and experimentation, the code
used to implement the proposed model has been made fully open source.
