6 datasets found
  1. d

    The Marshall Project: COVID Cases in Prisons

    • data.world
    csv, zip
    Updated Apr 6, 2023
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    The Associated Press (2023). The Marshall Project: COVID Cases in Prisons [Dataset]. https://data.world/associatedpress/marshall-project-covid-cases-in-prisons
    Explore at:
    csv, zipAvailable download formats
    Dataset updated
    Apr 6, 2023
    Authors
    The Associated Press
    Time period covered
    Jul 31, 2019 - Aug 1, 2021
    Description

    Overview

    The Marshall Project, the nonprofit investigative newsroom dedicated to the U.S. criminal justice system, has partnered with The Associated Press to compile data on the prevalence of COVID-19 infection in prisons across the country. The Associated Press is sharing this data as the most comprehensive current national source of COVID-19 outbreaks in state and federal prisons.

    Lawyers, criminal justice reform advocates and families of the incarcerated have worried about what was happening in prisons across the nation as coronavirus began to take hold in the communities outside. Data collected by The Marshall Project and AP shows that hundreds of thousands of prisoners, workers, correctional officers and staff have caught the illness as prisons became the center of some of the country’s largest outbreaks. And thousands of people — most of them incarcerated — have died.

    In December, as COVID-19 cases spiked across the U.S., the news organizations also shared cumulative rates of infection among prison populations, to better gauge the total effects of the pandemic on prison populations. The analysis found that by mid-December, one in five state and federal prisoners in the United States had tested positive for the coronavirus -- a rate more than four times higher than the general population.

    This data, which is updated weekly, is an effort to track how those people have been affected and where the crisis has hit the hardest.

    Methodology and Caveats

    The data tracks the number of COVID-19 tests administered to people incarcerated in all state and federal prisons, as well as the staff in those facilities. It is collected on a weekly basis by Marshall Project and AP reporters who contact each prison agency directly and verify published figures with officials.

    Each week, the reporters ask every prison agency for the total number of coronavirus tests administered to its staff members and prisoners, the cumulative number who tested positive among staff and prisoners, and the numbers of deaths for each group.

    The time series data is aggregated to the system level; there is one record for each prison agency on each date of collection. Not all departments could provide data for the exact date requested, and the data indicates the date for the figures.

    To estimate the rate of infection among prisoners, we collected population data for each prison system before the pandemic, roughly in mid-March, in April, June, July, August, September and October. Beginning the week of July 28, we updated all prisoner population numbers, reflecting the number of incarcerated adults in state or federal prisons. Prior to that, population figures may have included additional populations, such as prisoners housed in other facilities, which were not captured in our COVID-19 data. In states with unified prison and jail systems, we include both detainees awaiting trial and sentenced prisoners.

    To estimate the rate of infection among prison employees, we collected staffing numbers for each system. Where current data was not publicly available, we acquired other numbers through our reporting, including calling agencies or from state budget documents. In six states, we were unable to find recent staffing figures: Alaska, Hawaii, Kentucky, Maryland, Montana, Utah.

    To calculate the cumulative COVID-19 impact on prisoner and prison worker populations, we aggregated prisoner and staff COVID case and death data up through Dec. 15. Because population snapshots do not account for movement in and out of prisons since March, and because many systems have significantly slowed the number of new people being sent to prison, it’s difficult to estimate the total number of people who have been held in a state system since March. To be conservative, we calculated our rates of infection using the largest prisoner population snapshots we had during this time period.

    As with all COVID-19 data, our understanding of the spread and impact of the virus is limited by the availability of testing. Epidemiology and public health experts say that aside from a few states that have recently begun aggressively testing in prisons, it is likely that there are more cases of COVID-19 circulating undetected in facilities. Sixteen prison systems, including the Federal Bureau of Prisons, would not release information about how many prisoners they are testing.

    Corrections departments in Indiana, Kansas, Montana, North Dakota and Wisconsin report coronavirus testing and case data for juvenile facilities; West Virginia reports figures for juvenile facilities and jails. For consistency of comparison with other state prison systems, we removed those facilities from our data that had been included prior to July 28. For these states we have also removed staff data. Similarly, Pennsylvania’s coronavirus data includes testing and cases for those who have been released on parole. We removed these tests and cases for prisoners from the data prior to July 28. The staff cases remain.

    About the Data

    There are four tables in this data:

    • covid_prison_cases.csv contains weekly time series data on tests, infections and deaths in prisons. The first dates in the table are on March 26. Any questions that a prison agency could not or would not answer are left blank.

    • prison_populations.csv contains snapshots of the population of people incarcerated in each of these prison systems for whom data on COVID testing and cases are available. This varies by state and may not always be the entire number of people incarcerated in each system. In some states, it may include other populations, such as those on parole or held in state-run jails. This data is primarily for use in calculating rates of testing and infection, and we would not recommend using these numbers to compare the change in how many people are being held in each prison system.

    • staff_populations.csv contains a one-time, recent snapshot of the headcount of workers for each prison agency, collected as close to April 15 as possible.

    • covid_prison_rates.csv contains the rates of cases and deaths for prisoners. There is one row for every state and federal prison system and an additional row with the National totals.

    Queries

    The Associated Press and The Marshall Project have created several queries to help you use this data:

    Get your state's prison COVID data: Provides each week's data from just your state and calculates a cases-per-100000-prisoners rate, a deaths-per-100000-prisoners rate, a cases-per-100000-workers rate and a deaths-per-100000-workers rate here

    Rank all systems' most recent data by cases per 100,000 prisoners here

    Find what percentage of your state's total cases and deaths -- as reported by Johns Hopkins University -- occurred within the prison system here

    Attribution

    In stories, attribute this data to: “According to an analysis of state prison cases by The Marshall Project, a nonprofit investigative newsroom dedicated to the U.S. criminal justice system, and The Associated Press.”

    Contributors

    Many reporters and editors at The Marshall Project and The Associated Press contributed to this data, including: Katie Park, Tom Meagher, Weihua Li, Gabe Isman, Cary Aspinwall, Keri Blakinger, Jake Bleiberg, Andrew R. Calderón, Maurice Chammah, Andrew DeMillo, Eli Hager, Jamiles Lartey, Claudia Lauer, Nicole Lewis, Humera Lodhi, Colleen Long, Joseph Neff, Michelle Pitcher, Alysia Santo, Beth Schwartzapfel, Damini Sharma, Colleen Slevin, Christie Thompson, Abbie VanSickle, Adria Watson, Andrew Welsh-Huggins.

    Questions

    If you have questions about the data, please email The Marshall Project at info+covidtracker@themarshallproject.org or file a Github issue.

    To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

  2. n

    Coronavirus (Covid-19) Data in the United States

    • nytimes.com
    • openicpsr.org
    • +2more
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    New York Times, Coronavirus (Covid-19) Data in the United States [Dataset]. https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html
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    Dataset provided by
    New York Times
    Description

    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.

  3. f

    Calf health information.

    • plos.figshare.com
    xlsx
    Updated Nov 14, 2024
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    Cian Reid; John Donlon; Aude Remot; Emer Kennedy; Giovanna De Matteis; Cliona O’Farrelly; Conor McAloon; Kieran G. Meade (2024). Calf health information. [Dataset]. http://doi.org/10.1371/journal.pone.0309964.s004
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Nov 14, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Cian Reid; John Donlon; Aude Remot; Emer Kennedy; Giovanna De Matteis; Cliona O’Farrelly; Conor McAloon; Kieran G. Meade
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Bovine respiratory disease (BRD) is a leading cause of mortality and compromised welfare in bovines. It is a polymicrobial syndrome resulting from a complex interplay of viral and bacterial pathogens with environmental factors. Despite the availability of vaccines, incidence and severity in young calves remains unabated. A more precise analysis of host innate immune responses during infection will identify improved diagnostic and prognostic biomarkers for early intervention and targeted treatments to prevent severe disease and loss of production efficiency. Here, we investigate hematological and innate immune responses using standardized ex-vivo whole blood assays in calves diagnosed with BRD. A total of 65 calves were recruited for this study, all between 2–8 weeks of age with 28 diagnosed with BRD by a thoracic ultrasonography score (TUS) and 19 by Wisconsin health score (WHS) and all data compared to 22 healthy controls from the same 9 study farms. Haematology revealed circulating immune cell populations were similar in both TUS positive and WHS positive calves compared to healthy controls. Gene expression analysis of 48 innate immune signalling genes in whole blood stimulated with TLR ligands was completed in a subset of calves. TLR1/2 stimulation with Pam3CSK4 showed a decreased pattern of expression in IL-1 and inflammasome related genes in addition to chemokine genes in calves with BRD. In response to TLR ligands LPS, Pam3CSK4 and R848, protein analysis of supernatant collected from all calves with BRD revealed significantly increased IL-6, but not IL-1β or IL-8, compared to healthy controls. This hyper-induction of IL-6 was observed most significantly in response to TLR1/2 stimulation in TUS positive calves. ROC analysis identified this induced IL-6 response to TLR1/2 stimulation as a potential diagnostic for BRD with a 74% true positive and 5% false positive detection rate for an IL-6 concentration >1780pg/mL. Overall, these results show altered immune responses specifically upon TLR1/2 activation is associated with BRD pathology which may contribute to disease progression. We have also identified induced IL-6 as a potentially informative biomarker for improved early intervention strategies for BRD.

  4. The number of included patients and confirmed cases.

    • plos.figshare.com
    xls
    Updated May 16, 2025
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    Joanna Zajkowska; Elżbieta Waluk; Renata Świerzbińska; Justyna Dunaj; Olga Zajkowska; Dominik Wawrzuta; Jolanta Niścigorska-Olsen; Marek Matukiewicz; Barbara Oczko-Grzesik; Daniel Veltze; Katarzyna Bernacka-Andrzejewska; Katarzyna Burchart-Adamczyk; Ewa Dutkiewicz; Jadwiga Maciukajć; Krystyna Konieczny; Danuta Malcher-Bober; Dorota Dybowska; Małgorzata Hapyn-Rocha; Monika Marsik-Styrkosz; Grzegorz Kmak; Monika Bociąga-Jasik; Magdalena Byś-Chrzanowska; Iwona Paradowska-Stankiewicz (2025). The number of included patients and confirmed cases. [Dataset]. http://doi.org/10.1371/journal.pone.0323022.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 16, 2025
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Joanna Zajkowska; Elżbieta Waluk; Renata Świerzbińska; Justyna Dunaj; Olga Zajkowska; Dominik Wawrzuta; Jolanta Niścigorska-Olsen; Marek Matukiewicz; Barbara Oczko-Grzesik; Daniel Veltze; Katarzyna Bernacka-Andrzejewska; Katarzyna Burchart-Adamczyk; Ewa Dutkiewicz; Jadwiga Maciukajć; Krystyna Konieczny; Danuta Malcher-Bober; Dorota Dybowska; Małgorzata Hapyn-Rocha; Monika Marsik-Styrkosz; Grzegorz Kmak; Monika Bociąga-Jasik; Magdalena Byś-Chrzanowska; Iwona Paradowska-Stankiewicz
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The number of included patients and confirmed cases.

  5. Mortality rates for hospitalized COVID–19 patients during the first three...

    • plos.figshare.com
    xls
    Updated Jun 13, 2023
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    Michael C. Fiore; Stevens S. Smith; Robert T. Adsit; Daniel M. Bolt; Karen L. Conner; Steven L. Bernstein; Oliver D. Eng; David Lazuk; Alec Gonzalez; Douglas E. Jorenby; Heather D’Angelo; Julie A. Kirsch; Brian Williams; Margaret B. Nolan; Todd Hayes-Birchler; Sean Kent; Hanna Kim; Thomas M. Piasecki; Wendy S. Slutske; Stan Lubanski; Menggang Yu; Youmi Suk; Yuxin Cai; Nitu Kashyap; Jomol P. Mathew; Gabriel McMahan; Betsy Rolland; Hilary A. Tindle; Graham W. Warren; Lawrence C. An; Andrew D. Boyd; Darlene H. Brunzell; Victor Carrillo; Li-Shiun Chen; James M. Davis; Deepika Dilip; Edward F. Ellerbeck; Eduardo Iturrate; Thulasee Jose; Niharika Khanna; Andrea King; Elizabeth Klass; Michael Newman; Kimberly A. Shoenbill; Elisa Tong; Janice Y. Tsoh; Karen M. Wilson; Wendy E. Theobald; Timothy B. Baker (2023). Mortality rates for hospitalized COVID–19 patients during the first three and final three months of data collection. [Dataset]. http://doi.org/10.1371/journal.pone.0274571.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 13, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Michael C. Fiore; Stevens S. Smith; Robert T. Adsit; Daniel M. Bolt; Karen L. Conner; Steven L. Bernstein; Oliver D. Eng; David Lazuk; Alec Gonzalez; Douglas E. Jorenby; Heather D’Angelo; Julie A. Kirsch; Brian Williams; Margaret B. Nolan; Todd Hayes-Birchler; Sean Kent; Hanna Kim; Thomas M. Piasecki; Wendy S. Slutske; Stan Lubanski; Menggang Yu; Youmi Suk; Yuxin Cai; Nitu Kashyap; Jomol P. Mathew; Gabriel McMahan; Betsy Rolland; Hilary A. Tindle; Graham W. Warren; Lawrence C. An; Andrew D. Boyd; Darlene H. Brunzell; Victor Carrillo; Li-Shiun Chen; James M. Davis; Deepika Dilip; Edward F. Ellerbeck; Eduardo Iturrate; Thulasee Jose; Niharika Khanna; Andrea King; Elizabeth Klass; Michael Newman; Kimberly A. Shoenbill; Elisa Tong; Janice Y. Tsoh; Karen M. Wilson; Wendy E. Theobald; Timothy B. Baker
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Mortality rates for hospitalized COVID–19 patients during the first three and final three months of data collection.

  6. C

    Connected Medical Technology Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated May 19, 2025
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    Data Insights Market (2025). Connected Medical Technology Report [Dataset]. https://www.datainsightsmarket.com/reports/connected-medical-technology-1400716
    Explore at:
    pdf, ppt, docAvailable download formats
    Dataset updated
    May 19, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The Connected Medical Technology (CMT) market is experiencing robust growth, driven by the increasing adoption of remote patient monitoring (RPM), the rise of telehealth, and the demand for improved healthcare efficiency and patient outcomes. The market, estimated at $50 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $150 billion by 2033. This expansion is fueled by several key factors. Firstly, technological advancements in areas like wireless connectivity (BLE, Wi-Fi, Cellular) and miniaturized sensors are enabling the development of smaller, more sophisticated, and user-friendly medical devices. Secondly, increasing healthcare costs and the need for cost-effective solutions are driving the adoption of CMT solutions that reduce hospital readmissions and improve patient management. Finally, government initiatives promoting digital health and telehealth are further accelerating market growth across regions. The Hospital segment currently holds the largest market share, followed by Clinic and Home Care, indicating a shift towards decentralized and proactive healthcare delivery. Different connectivity technologies cater to varied needs; BLE and Wi-Fi are prevalent in home care and smaller clinics due to ease of implementation and cost-effectiveness, whereas cellular and satellite technologies are crucial for remote monitoring in geographically challenging areas. Leading players like Nonin Medical, Philips, and Medtronic are actively investing in R&D and strategic partnerships to solidify their market positions. The market’s growth is not without challenges. Data security and privacy concerns remain significant hurdles, requiring robust cybersecurity measures and adherence to stringent data protection regulations. Interoperability issues among different devices and systems also pose a challenge, hindering seamless data exchange and comprehensive patient monitoring. Furthermore, the high initial investment costs associated with implementing CMT infrastructure can be a barrier for smaller healthcare providers in developing economies. Despite these restraints, the long-term growth outlook for the CMT market remains positive. The increasing availability of affordable devices, improving connectivity infrastructure, and a growing awareness of the benefits of remote patient monitoring are expected to overcome these challenges and propel further market expansion throughout the forecast period. Regional analysis shows North America and Europe holding significant market shares initially, but rapid growth is expected in the Asia-Pacific region driven by rising healthcare expenditure and increasing smartphone penetration.

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    Learn how you can add new datasets to our index.

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The Associated Press (2023). The Marshall Project: COVID Cases in Prisons [Dataset]. https://data.world/associatedpress/marshall-project-covid-cases-in-prisons

The Marshall Project: COVID Cases in Prisons

The Marshall Project is compiling data on the prevalence of COVID-19 infection in prisons across the country

Explore at:
2 scholarly articles cite this dataset (View in Google Scholar)
csv, zipAvailable download formats
Dataset updated
Apr 6, 2023
Authors
The Associated Press
Time period covered
Jul 31, 2019 - Aug 1, 2021
Description

Overview

The Marshall Project, the nonprofit investigative newsroom dedicated to the U.S. criminal justice system, has partnered with The Associated Press to compile data on the prevalence of COVID-19 infection in prisons across the country. The Associated Press is sharing this data as the most comprehensive current national source of COVID-19 outbreaks in state and federal prisons.

Lawyers, criminal justice reform advocates and families of the incarcerated have worried about what was happening in prisons across the nation as coronavirus began to take hold in the communities outside. Data collected by The Marshall Project and AP shows that hundreds of thousands of prisoners, workers, correctional officers and staff have caught the illness as prisons became the center of some of the country’s largest outbreaks. And thousands of people — most of them incarcerated — have died.

In December, as COVID-19 cases spiked across the U.S., the news organizations also shared cumulative rates of infection among prison populations, to better gauge the total effects of the pandemic on prison populations. The analysis found that by mid-December, one in five state and federal prisoners in the United States had tested positive for the coronavirus -- a rate more than four times higher than the general population.

This data, which is updated weekly, is an effort to track how those people have been affected and where the crisis has hit the hardest.

Methodology and Caveats

The data tracks the number of COVID-19 tests administered to people incarcerated in all state and federal prisons, as well as the staff in those facilities. It is collected on a weekly basis by Marshall Project and AP reporters who contact each prison agency directly and verify published figures with officials.

Each week, the reporters ask every prison agency for the total number of coronavirus tests administered to its staff members and prisoners, the cumulative number who tested positive among staff and prisoners, and the numbers of deaths for each group.

The time series data is aggregated to the system level; there is one record for each prison agency on each date of collection. Not all departments could provide data for the exact date requested, and the data indicates the date for the figures.

To estimate the rate of infection among prisoners, we collected population data for each prison system before the pandemic, roughly in mid-March, in April, June, July, August, September and October. Beginning the week of July 28, we updated all prisoner population numbers, reflecting the number of incarcerated adults in state or federal prisons. Prior to that, population figures may have included additional populations, such as prisoners housed in other facilities, which were not captured in our COVID-19 data. In states with unified prison and jail systems, we include both detainees awaiting trial and sentenced prisoners.

To estimate the rate of infection among prison employees, we collected staffing numbers for each system. Where current data was not publicly available, we acquired other numbers through our reporting, including calling agencies or from state budget documents. In six states, we were unable to find recent staffing figures: Alaska, Hawaii, Kentucky, Maryland, Montana, Utah.

To calculate the cumulative COVID-19 impact on prisoner and prison worker populations, we aggregated prisoner and staff COVID case and death data up through Dec. 15. Because population snapshots do not account for movement in and out of prisons since March, and because many systems have significantly slowed the number of new people being sent to prison, it’s difficult to estimate the total number of people who have been held in a state system since March. To be conservative, we calculated our rates of infection using the largest prisoner population snapshots we had during this time period.

As with all COVID-19 data, our understanding of the spread and impact of the virus is limited by the availability of testing. Epidemiology and public health experts say that aside from a few states that have recently begun aggressively testing in prisons, it is likely that there are more cases of COVID-19 circulating undetected in facilities. Sixteen prison systems, including the Federal Bureau of Prisons, would not release information about how many prisoners they are testing.

Corrections departments in Indiana, Kansas, Montana, North Dakota and Wisconsin report coronavirus testing and case data for juvenile facilities; West Virginia reports figures for juvenile facilities and jails. For consistency of comparison with other state prison systems, we removed those facilities from our data that had been included prior to July 28. For these states we have also removed staff data. Similarly, Pennsylvania’s coronavirus data includes testing and cases for those who have been released on parole. We removed these tests and cases for prisoners from the data prior to July 28. The staff cases remain.

About the Data

There are four tables in this data:

  • covid_prison_cases.csv contains weekly time series data on tests, infections and deaths in prisons. The first dates in the table are on March 26. Any questions that a prison agency could not or would not answer are left blank.

  • prison_populations.csv contains snapshots of the population of people incarcerated in each of these prison systems for whom data on COVID testing and cases are available. This varies by state and may not always be the entire number of people incarcerated in each system. In some states, it may include other populations, such as those on parole or held in state-run jails. This data is primarily for use in calculating rates of testing and infection, and we would not recommend using these numbers to compare the change in how many people are being held in each prison system.

  • staff_populations.csv contains a one-time, recent snapshot of the headcount of workers for each prison agency, collected as close to April 15 as possible.

  • covid_prison_rates.csv contains the rates of cases and deaths for prisoners. There is one row for every state and federal prison system and an additional row with the National totals.

Queries

The Associated Press and The Marshall Project have created several queries to help you use this data:

Get your state's prison COVID data: Provides each week's data from just your state and calculates a cases-per-100000-prisoners rate, a deaths-per-100000-prisoners rate, a cases-per-100000-workers rate and a deaths-per-100000-workers rate here

Rank all systems' most recent data by cases per 100,000 prisoners here

Find what percentage of your state's total cases and deaths -- as reported by Johns Hopkins University -- occurred within the prison system here

Attribution

In stories, attribute this data to: “According to an analysis of state prison cases by The Marshall Project, a nonprofit investigative newsroom dedicated to the U.S. criminal justice system, and The Associated Press.”

Contributors

Many reporters and editors at The Marshall Project and The Associated Press contributed to this data, including: Katie Park, Tom Meagher, Weihua Li, Gabe Isman, Cary Aspinwall, Keri Blakinger, Jake Bleiberg, Andrew R. Calderón, Maurice Chammah, Andrew DeMillo, Eli Hager, Jamiles Lartey, Claudia Lauer, Nicole Lewis, Humera Lodhi, Colleen Long, Joseph Neff, Michelle Pitcher, Alysia Santo, Beth Schwartzapfel, Damini Sharma, Colleen Slevin, Christie Thompson, Abbie VanSickle, Adria Watson, Andrew Welsh-Huggins.

Questions

If you have questions about the data, please email The Marshall Project at info+covidtracker@themarshallproject.org or file a Github issue.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

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