This data set contains Wisconsin COVID-19 case, death, hospitalization, test data and population information by county boundary. All data are laboratory-confirmed cases of COVID-19 that are frozen once a day to verify and ensure that we are reporting accurate information. These numbers are the official state numbers, though counties may report their own totals independent of Department of Health Services (combining the DHS and local totals may result in inaccurate totals). Deaths are reported by health care providers, medical examiners/coroners, and recorded by local health departments in order to be counted by the state DHS. Detailed data descriptions can be found within the COVID-19 Public Use Data Definitions document: https://www.dhs.wisconsin.gov/publications/p02677.pdf.

This data set contains Wisconsin COVID-19 data by county boundary. Data is updated at 2:00PM CDT daily.Detailed data descriptions can be found within the COVID-19 Public Use Data Definitions document.All data are laboratory-confirmed cases of COVID-19 that we freeze once a day to verify and ensure that we are reporting accurate information. These numbers are the official state numbers, though counties may report their own totals independent of DHS. Combining the DHS and local totals may result in inaccurate totals.Data included in these tables are subject to change. As individual cases are investigated by public health, there may be corrections to the status and details of cases that result in changes to this information.Deaths must be reported by health care providers, medical examiners/coroners, and recorded by local health departments in order to be counted.Starting on March 30, 2020, the number of people with negative test results was changed to include only Wisconsin residents. The number of people with negative test results includes only Wisconsin residents who had their results reported electronically to DHS. As a result, this number underestimates the total number of Wisconsin residents with negative test results."-999" values represent fewer than 5 cases, including 0 cases.For more information on the COVID-19 outbreak please visit https://www.dhs.wisconsin.gov/outbreaks/index.htm.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

Over the past thirty years, disaster scholars have highlighted that communities with stronger social infrastructure - including social ties that enable trust, mutual aid, and collective action - tend to respond to and recover better from crisis. However, comprehensive measurements of social capital across communities have been rare. This study adapts Kyne and Aldrich’s (2019) county-level social capital index to the census-tract level, generating social capital indices from 2011 to 2018 at the census-tract, zipcode, and county subdivision levels. To demonstrate their usefulness to disaster planners, public health experts, and local officials, we paired these with the CDC’s Social Vulnerability Index to predict the incidence of COVID-19 in case studies in Massachusetts, Wisconsin, Illinois, and New York City. We found that social capital and social vulnerability predicted as much as 95% of the variation in COVID outbreaks, highlighting their power as diagnostic and predictive tools for combating the spread of COVID.