TITLE: A County-level Dataset for Informing the United States' Response to COVID-19 

ABSTRACT: As the coronavirus disease 2019 (COVID-19) continues to be a global pandemic,
policy makers have enacted and reversed non-pharmaceutical interventions with
various levels of restrictions to limit its spread. Data driven approaches that
analyze temporal characteristics of the pandemic and its dependence on regional
conditions might supply information to support the implementation of mitigation
and suppression strategies. To facilitate research in this direction on the
example of the United States, we present a machine-readable dataset that
aggregates relevant data from governmental, journalistic, and academic sources
on the U.S. county level. In addition to county-level time-series data from the
JHU CSSE COVID-19 Dashboard, our dataset contains more than 300 variables that
summarize population estimates, demographics, ethnicity, housing, education,
employment and income, climate, transit scores, and healthcare system-related
metrics. Furthermore, we present aggregated out-of-home activity information
for various points of interest for each county, including grocery stores and
hospitals, summarizing data from SafeGraph and Google mobility reports. We
compile information from IHME, state and county-level government, and
newspapers for dates of the enactment and reversal of non-pharmaceutical
interventions. By collecting these data, as well as providing tools to read
them, we hope to accelerate research that investigates how the disease spreads
and why spread may be different across regions. Our dataset and associated code
are available at github.com/JieYingWu/COVID-19_US_County-level_Summaries.
As the coronavirus disease 2019 (COVID-19) continues to be a global pandemic,
policy makers have enacted and reversed non-pharmaceutical interventions with
various levels of restrictions to limit its spread. Data driven approaches that
analyze temporal characteristics of the pandemic and its dependence on regional
conditions might supply information to support the implementation of mitigation
and suppression strategies. To facilitate research in this direction on the
example of the United States, we present a machine-readable dataset that
aggregates relevant data from governmental, journalistic, and academic sources
on the U.S. county level. In addition to county-level time-series data from the
JHU CSSE COVID-19 Dashboard, our dataset contains more than 300 variables that
summarize population estimates, demographics, ethnicity, housing, education,
employment and income, climate, transit scores, and healthcare system-related
metrics. Furthermore, we present aggregated out-of-home activity information
for various points of interest for each county, including grocery stores and
hospitals, summarizing data from SafeGraph and Google mobility reports. We
compile information from IHME, state and county-level government, and
newspapers for dates of the enactment and reversal of non-pharmaceutical
interventions. By collecting these data, as well as providing tools to read
them, we hope to accelerate research that investigates how the disease spreads
and why spread may be different across regions. Our dataset and associated code
are available at github.com/JieYingWu/COVID-19_US_County-level_Summaries.
A County-level Dataset for Informing the United States' Response to COVID-19
http://arxiv.org/abs/2004.00756v2