  The rapid spread of the novel coronavirus (COVID-19) has severely impacted
almost all countries around the world. It not only has caused a tremendous
burden on health-care providers to bear, but it has also brought severe impacts
on the economy and social life. The presence of reliable data and the results
of in-depth statistical analyses provide researchers and policymakers with
invaluable information to understand this pandemic and its growth pattern more
clearly. This paper combines and processes an extensive collection of publicly
available datasets to provide a unified information source for representing
geographical regions with regards to their pandemic-related behavior. The
features are grouped into various categories to account for their impact based
on the higher-level concepts associated with them. This work uses several
correlation analysis techniques to observe value and order relationships
between features, feature groups, and COVID-19 occurrences. Dimensionality
reduction techniques and projection methodologies are used to elaborate on
individual and group importance of these representative features. A specific
RNN-based inference pipeline called DoubleWindowLSTM-CP is proposed in this
work for predictive event modeling. It utilizes sequential patterns and enables
concise record representation while using but a minimal amount of historical
data. The quantitative results of our statistical analytics indicated critical
patterns reflecting on many of the expected collective behavior and their
associated outcomes. Predictive modeling with DoubleWindowLSTM-CP instance
exhibits efficient performance in quantitative and qualitative assessments
while reducing the need for extended and reliable historical information on the
pandemic.
