  We present an interpretable high-resolution spatio-temporal model to estimate
COVID-19 deaths together with confirmed cases one-week ahead of the current
time, at the county-level and weekly aggregated, in the United States. A
notable feature of our spatio-temporal model is that it considers the (a)
temporal auto- and pairwise correlation of the two local time series (confirmed
cases and death of the COVID-19), (b) dynamics between locations (propagation
between counties), and (c) covariates such as local within-community mobility
and social demographic factors. The within-community mobility and demographic
factors, such as total population and the proportion of the elderly, are
included as important predictors since they are hypothesized to be important in
determining the dynamics of COVID-19. To reduce the model's
high-dimensionality, we impose sparsity structures as constraints and emphasize
the impact of the top ten metropolitan areas in the nation, which we refer (and
treat within our models) as hubs in spreading the disease. Our retrospective
out-of-sample county-level predictions were able to forecast the subsequently
observed COVID-19 activity accurately. The proposed multi-variate predictive
models were designed to be highly interpretable, with clear identification and
quantification of the most important factors that determine the dynamics of
COVID-19. Ongoing work involves incorporating more covariates, such as
education and income, to improve prediction accuracy and model
interpretability.
