  Non-pharmacologic interventions (NPIs) are one method to mitigate the spread
and effects of the COVID-19 pandemic in the United States. NPIs promote
protective actions to reduce exposure risk and can reduce mobility patterns
within communities. Growing research literature suggests that socially
vulnerable populations are disproportionately impacted with higher infection
and higher fatality rates of COVID-19, though there is limited understanding of
the underlying mechanisms to this health disparity. Thus, the research examines
two distinct and complimentary datasets at a granular scale for five urban
locations. Through statistical and spatial analyses, the research extensively
investigates the exposure risk reduction of socially vulnerable populations due
to NPIs. The mobility dataset tracks population movement across ZIP codes; it
is used for an origin-destination network analysis. The population activity
dataset is based on the number of visits from census block groups (CBG) to
points of interest (POIs), such as grocery stores, restaurants, education
centers, and medical facilities; it is used for network analysis of
population-facilities interactions. The mobility dataset showed that, after the
implementation of NPIs, socially vulnerable populations engaged in increased
mobility in the form of inflow between ZIP code areas. Similarly, population
activity analysis showed an increased exposure risk for socially vulnerable
populations based on a greater number of inflow visits of CBGs to POIs, which
increases the risk of contact at POIs, and a greater number of outflow visits
from POIs to home CBGs, which increases risk of transmission within CBGs. These
findings can assist emergency planners and public health officials in
comprehending how different groups are able to implement protective actions and
can inform more equitable and data-driven NPI policies for future epidemics.
