COVID-19 in Metropolitan and Non-Metropolitan Counties
These slides compare COVID-19 coronavirus cases and deaths in rural and urban areas.
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These slides compare COVID-19 coronavirus cases and deaths in rural and urban areas.
This date note examines the share of non-elderly adults at higher risk of serious illness if infected with coronavirus by race/ethnicity and household income in 2018.
This blog looks at states that have moved to ease social distancing requirements, examining approaches taken as well as metrics for helping to assess readiness for reopening.
Provides a summary of health provisions in the fourth major legislative initiative to address COVID-19, the Paycheck Protection Program and Health Care Enhancement Act.
While to date big cities and major urban areas have seen the greatest number of coronavirus cases and deaths, this analysis finds the growth rate is now higher in rural areas.
This data note provides an accounting of publicly-available information on donor funding to date for the global coronavirus (COVID-19) response.
About four in ten adults (37.6%) ages 18 and older in the U.S. (92.6 million people) have a higher risk of developing serious illness if they become infected with the novel coronavirus, due to their older age (65 and older) or health condition. The share who have a higher risk varies across the country. An estimated 5.1 million of these adults are uninsured.
This explainer describes the National Disaster Medical System (NDMS), explores how it has been used in the past, and assesses how it is already being used or has been proposed to be used to fill gaps in the current response to the COVID-19 pandemic.
This post looks at potential benchmarks for estimating the number of coronavirus tests needed in the United States and compares them to current national, and state level, testing levels.
This blog gives a primer on epidemiological models for Covid-19 (coronavirus). It describes the uses and the types of models used, then lists a number of examples of different types of models and some key findings. The post also describes the limitations and assumptions related to these models, and how to use the information they provide more effectively.
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