  Understanding influencing factors is essential for the surveillance and
prevention of infectious diseases, and the factors are likely to vary spatially
and temporally as the disease progresses. Taking daily cases and deaths data
during the coronavirus disease 2019 (COVID-19) outbreak in the U.S. as a case
study, we develop a mobility-augmented geographically and temporally weighted
regression (M-GTWR) model to quantify the spatiotemporal impacts of
social-demographic factors and human activities on the COVID-19 dynamics.
Different from the base GTWR model, we incorporate a mobility-adjusted distance
weight matrix where travel mobility is used in addition to the spatial
adjacency to capture the correlations among local observations. The model
residuals suggest that the proposed model achieves a substantial improvement
over other benchmark methods in addressing the spatiotemporal nonstationarity.
Our results reveal that the impacts of social-demographic and human activity
variables present significant spatiotemporal heterogeneity. In particular, a 1%
increase in population density may lead to 0.63% and 0.71% more daily cases and
deaths, and a 1% increase in the mean commuting time may result in 0.22% and
0.95% increases in daily cases and deaths. Although increased human activities
will, in general, intensify the disease outbreak, we report that the effects of
grocery and pharmacy-related activities are insignificant in areas with high
population density. And activities at the workplace and public transit are
found to either increase or decrease the number of cases and deaths, depending
on particular locations. The results of our study establish a quantitative
framework for identifying influencing factors during a disease outbreak, and
the obtained insights may have significant implications in guiding the
policy-making against infectious diseases.
