12 datasets found
  1. d

    Detroit City COVID Confirmed Cases and Rates by ZIP Code

    • data.detroitmi.gov
    • detroitdata.org
    • +1more
    Updated Apr 5, 2021
    + more versions
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    City of Detroit (2021). Detroit City COVID Confirmed Cases and Rates by ZIP Code [Dataset]. https://data.detroitmi.gov/datasets/detroitmi::detroit-city-covid-confirmed-cases-and-rates-by-zip-code/about
    Explore at:
    Dataset updated
    Apr 5, 2021
    Dataset authored and provided by
    City of Detroit
    Area covered
    Detroit
    Description

    Detroit-specific ZIP code populations, along with their cumulative COVID case counts, deaths, and rates. Data provided by Detroit Health Department. The public-facing COVID Cases Dashboard is hosted at: detroitmi.gov/healthUPDATE* July 29 2021:The underlying calculation for disease date was updated to allow for individuals to appear on the curve in multiple locations if they experienced more than one case of COVID-19 that was at least 90 days apart.Geospatial information analysis was also improved and additional criterial for address clean up were implemented, which leads to more accurate case counts within Zip Codes. Some unverified addresses that may have appeared in previous Zip Code counts are now excluded.This change discourages direct comparison of dashboard visualizations and counts prior to the new calculation, and non-significant shifts in numbers will be noticed.Case numbers represent Detroit residents only. Some ZIP codes with very low case counts are excluded to protect privacy. Case counts are totals per ZIP code and are not adjusted for population. ZIP code totals are preliminary; addresses are updated as new information becomes available and counts are subject to change. Not all cases have an accurate location; only cases with a known ZIP code are represented. Where a ZIP code is split between cities, only the Detroit portion is shown (48203, 48211, 48212, 48236, 48239). The counts exclude cases among prisoners at the Wayne County Jail and known hospital or laboratory locations.ZIP_Code: The USPS ZIP postal code Clipped_ZIP_Population: The 2010 population of the ZIP code, clipped to include Detroit City residents only.ZIP_Case_Count: The current cumulative count of Confirmed COVID cases within the ZIP code, since the beginning of the pandemic. (Have a "Confimed" case status in MDSS)ZIP_Death_Count: The current cumulative count of Confirmed COVID cases within the ZIP code, since the beginning of the pandemic. (Have a "Confimed" case status in MDSS and are deceased)ZIP_Case_Rate: Rate of confirmed cases per 100 thousand residents in the ZIP code. For each zip, the rate was calculated by (C/P)*100000 C = the count of confirmed (MDSS case status = Confirmed) cases with a resident address in the ZIP code P = the population count of the ZIP codeZIP_Death_Rate: Rate of confirmed cases that were marked deceased, per 100 thousand residents in the ZIP code. For each zip, the rate was calculated by (D/P)*100000 D = the count of confirmed (MDSS case status = Confirmed) cases marked as deceased, with a resident address in the ZIP P = the population count of the ZIP code

  2. D

    COVID-19 cases and 2-1-1 food needs

    • detroitdata.org
    csv
    Updated Feb 16, 2024
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    Michigan Public (2024). COVID-19 cases and 2-1-1 food needs [Dataset]. https://detroitdata.org/dataset/covid-19-cases-and-2-1-1-food-needs
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    csv(1231)Available download formats
    Dataset updated
    Feb 16, 2024
    Dataset provided by
    Michigan Public
    License

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

    Description

    The single biggest need in the parts of Detroit that have been the hardest hit by the coronavirus is food. That's according to data available in a recently released COVID-19 Dashboard put together by Michigan 211.

  3. f

    Michigan COVID-19 Outbreaks by Type and Region 2020-2021

    • data.ferndalemi.gov
    • detroitdata.org
    • +1more
    Updated Mar 1, 2021
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    Data Driven Detroit (2021). Michigan COVID-19 Outbreaks by Type and Region 2020-2021 [Dataset]. https://data.ferndalemi.gov/documents/95c853a11c7242e9a15ca0f1f677f3f6
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    Dataset updated
    Mar 1, 2021
    Dataset authored and provided by
    Data Driven Detroit
    Area covered
    Michigan
    Description

    This dataset provides a single table of historical outbreak data as reported by public health departments to the Michigan Department of Health and Human Services from August 22, 2020 to February 11, 2021. Additional information about the dataset and more current data tables can be found here: https://www.michigan.gov/coronavirus/0,9753,7-406-98163_98173_102057---,00.html. Data is reported by Emergency Preparedness Region as well as the entire state. For more on Emergency Preparedness Regions: https://www.michigan.gov/mdhhs/0,5885,7-339-71548_54783_54826_56171-237197--,00.html. New outbreaks are those outbreaks that were first identified during the current reporting week. Ongoing outbreaks are those that had already been identified in previous weeks but have had at least one new associated case reported to the local health department in the last 2 weeks.Click here for metadata (descriptions of the fields)

  4. a

    COVID-19 Dashboard

    • analytics-detroitmi.hub.arcgis.com
    Updated Jan 1, 2020
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    City of Detroit (2020). COVID-19 Dashboard [Dataset]. https://analytics-detroitmi.hub.arcgis.com/datasets/covid-19-dashboard
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    Dataset updated
    Jan 1, 2020
    Dataset authored and provided by
    City of Detroit
    Description

    Detroit Health Departments COVID-19 Dashboard that tracks cases and deaths over time, demographics, testing, hospital capacity, zip code level information, nursing home cases and deaths, and vaccination breakdowns.

  5. COVID PLOS DATA.xlsx

    • figshare.com
    xlsx
    Updated Aug 29, 2021
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    Said El Zein (2021). COVID PLOS DATA.xlsx [Dataset]. http://doi.org/10.6084/m9.figshare.15085656.v3
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    xlsxAvailable download formats
    Dataset updated
    Aug 29, 2021
    Dataset provided by
    Figsharehttp://figshare.com/
    figshare
    Authors
    Said El Zein
    License

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

    Description

    Cycle threshold values of COVID PCR. Project reviewed and approved by Wayne State / DMC IRB

  6. Detroit Metro Area Communities Study (DMACS) Wave 14, Michigan, 2021

    • icpsr.umich.edu
    ascii, delimited, r +3
    Updated Mar 31, 2025
    + more versions
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    Gerber, Elisabeth; Morenoff, Jeffrey (2025). Detroit Metro Area Communities Study (DMACS) Wave 14, Michigan, 2021 [Dataset]. http://doi.org/10.3886/ICPSR38970.v1
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    r, ascii, stata, sas, spss, delimitedAvailable download formats
    Dataset updated
    Mar 31, 2025
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    Gerber, Elisabeth; Morenoff, Jeffrey
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/38970/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/38970/terms

    Area covered
    United States, Detroit, Michigan
    Description

    The Detroit Metro Area Communities Study (DMACS) is a panel survey of Detroit residents aged 18 and older. The original panel of respondents was drawn from an address-based probability sample of all occupied Detroit households in 2016 and has since been refreshed through additional address-based sampling annually. Between November 3 and December 15, 2021, 2,662 previously-enrolled panelists were invited to participate in a self-administered online or interviewer-administered telephone survey. A total of 1,900 Detroit residents completed the survey, yielding an overall response rate of 72% (using AAPOR Response Rate 1). Topics include experience with COVID-19; COVID-19 vaccine receipt, attitudes and trust; employment and economic precarity; neighborhood satisfaction; neighborhood change; as well as healthcare usage; the Child Tax Credit; and Digital Inclusion.

  7. D

    Coronavirus: Populations To Help

    • detroitdata.org
    Updated Apr 21, 2020
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    Data Driven Detroit (2020). Coronavirus: Populations To Help [Dataset]. https://detroitdata.org/dataset/coronavirus-populations-to-help
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    arcgis geoservices rest api, htmlAvailable download formats
    Dataset updated
    Apr 21, 2020
    Dataset provided by
    Data Driven Detroit
    Description

    {{description}}

  8. Data_Sheet_1_Vaccination Diffusion and Incentive: Empirical Analysis of the...

    • frontiersin.figshare.com
    pdf
    Updated May 30, 2023
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    Hwang Kim; Vithala R. Rao (2023). Data_Sheet_1_Vaccination Diffusion and Incentive: Empirical Analysis of the US State of Michigan.PDF [Dataset]. http://doi.org/10.3389/fpubh.2021.740367.s001
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    pdfAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    Frontiers Mediahttp://www.frontiersin.org/
    Authors
    Hwang Kim; Vithala R. Rao
    License

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

    Area covered
    Michigan, United States
    Description

    Vaccination is the only way to reach herd immunity and help people return to normal life. However, vaccination rollouts may not be as fast as expected in some regions due to individuals' vaccination hesitation. For this reason, in Detroit, Michigan, the city government has offered a $50 prepaid card to people who entice city residents to visit vaccination sites. This study examined vaccination rates in the US using Detroit, Michigan, as the setting. It sought to address two issues. First, we analyzed the vaccination diffusion process to predict whether any region would reach a vaccination completion level that ensures herd immunity. Second, we examined a natural experiment involving a vaccination incentive scheme in Detroit and discovered its causal inference. We collected weekly vaccination data and demographic Census data from the state of Michigan and employed the Bass model to study vaccination diffusion. Also, we used a synthetic control method to evaluate the causal inference of a vaccination incentive scheme utilized in Detroit. The results showed that many Michigan counties—as well as the city of Detroit—would not reach herd immunity given the progress of vaccination efforts. Also, we found that Detroit's incentive scheme indeed increased the weekly vaccination rate by 44.19% for the first dose (from 0.86 to 1.25%) but was ineffective in augmenting the rate of the second dose. The implications are valuable for policy makers to implement vaccination incentive schemes to boost vaccination rates in geographical areas where such rates remain inadequate for achieving herd immunity.

  9. Dataset from Will Hydroxychloroquine Impede or Prevent COVID-19: WHIP...

    • data-staging.niaid.nih.gov
    • data.niaid.nih.gov
    Updated Feb 22, 2025
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    Henry Ford Health System; William W. O'Neill; William W O'Neill, MD; Dee Dee Wang, MD (2025). Dataset from Will Hydroxychloroquine Impede or Prevent COVID-19: WHIP COVID-19 Study [Dataset]. http://doi.org/10.25934/00007320
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    Dataset updated
    Feb 22, 2025
    Dataset provided by
    Henry Ford Health
    Authors
    Henry Ford Health System; William W. O'Neill; William W O'Neill, MD; Dee Dee Wang, MD
    Area covered
    United States
    Variables measured
    COVID-19, Adverse Event, Seroconversion, Hydroxychloroquine, Serious Adverse Event, Therapeutic Drug Effect, Drug Interaction With Drug
    Description

    This is a prospective, multi-site study designed to evaluate whether the use of hydroxychloroquine in healthcare workers (HCW), Nursing Home Workers (NHW), first responders (FR), and Detroit Department of Transportation bus drivers (DDOT) in SE, Michigan, can prevent the acquisition, symptoms and clinical COVID-19 infection

    The primary objective of this study is to determine whether the use of daily or weekly oral hydroxychloroquine (HCQ) therapy will prevent SARS-CoV-2 infection and COVID-19 viremia and clinical COVID-19 infection healthcare workers (HCW) and first responders (FR) (EMS, Fire, Police, bus drivers) in Southeast Michigan.

    Preventing COVID-19 transmission to HCW, FR, and Detroit Department of Transportation (DDOT) bus drivers is a critical step in preserving the health care and first responder force, the prevention of COVID-19 transmission in health care facilities, with the potential to preserve thousands of lives in addition to sustaining health care systems and civil services both nationally and globally. If efficacious, further studies on the use of hydroxychloroquine to prevent COVID-19 in the general population could be undertaken, with a potential impact on hundreds of thousands of lives.

  10. Key informants by location, position and sex.

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Sarah R. Meyer; Ilana Seff; Alli Gillespie; Hannah Brumbaum; Najat Qushua; Lindsay Stark (2023). Key informants by location, position and sex. [Dataset]. http://doi.org/10.1371/journal.pone.0283599.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Sarah R. Meyer; Ilana Seff; Alli Gillespie; Hannah Brumbaum; Najat Qushua; Lindsay Stark
    License

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

    Description

    Adolescent resettled refugees across the United States have been significantly impacted by the COVID-19 pandemic, through socio-economic stressors in households, disproportionate morbidity and mortality in immigrant communities, and social isolation and loss of learning due to school closures and the shift to online learning. The Study of Adolescent Lives after Migration to America [SALaMA] investigates the mental health and wellbeing of adolescents who come from–or who have parents who came from–the Middle East and North Africa [MENA] region and settled in the U.S. There is a gap in understanding of the experiences during the pandemic of MENA-background adolescents in the U.S. The objective of this study was to describe the perspective of educators and other school-affiliated service providers on the impact of the COVID-19 pandemic on mental health and wellbeing of adolescent resettled refugees and access to and quality of education and support services for adolescent resettled refugees. The researchers collected data using in-depth interviews with key informants in Chicago, Illinois; Harrisonburg, Virginia; and Detroit Metropolitan Area [DMA], Michigan, Key informants were school administrators, managers of English language learning services and programs, teachers, therapists, staff of non-governmental organizations and/ or community-based organizations, and case workers. Data analysis was conducted utilizing directed content analysis to develop an initial codebook and identify key themes in the data. Findings revealed a number of pathways through which the pandemic impacted adolescent refugees and immigrants’ mental health and wellbeing, with online programming impacting students’ engagement, motivation and social isolation in terms of peer and provider relationships. Specific dynamics in refugee adolescents’ households increased stressors and reduced engagement through online learning, and access to space and resources needed to support learning during school closures were limited. Service providers emphasized multiple and overlapping impacts on service quality and access, resulting in reduced social supports and mental health prevention and response approaches. Due to the long-term impacts of school closures in the first two years of the pandemic, and ongoing disruption, these data both provide a snapshot of the impacts of the pandemic at a specific moment, as well as insights into ways forward in terms of adapting services and engaging students within restrictions and limitations due to the pandemic. These findings emphasize the need for educators and mental health service providers to rebuild and strengthen relationships with students and families. These findings indicate the need to consider, support and expand social support and mental health services, specifically for refugee adolescent students, in the context of learning and well-being during the COVID-19 pandemic.

  11. Primary codes and sub-codes.

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Sarah R. Meyer; Ilana Seff; Alli Gillespie; Hannah Brumbaum; Najat Qushua; Lindsay Stark (2023). Primary codes and sub-codes. [Dataset]. http://doi.org/10.1371/journal.pone.0283599.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Sarah R. Meyer; Ilana Seff; Alli Gillespie; Hannah Brumbaum; Najat Qushua; Lindsay Stark
    License

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

    Description

    Adolescent resettled refugees across the United States have been significantly impacted by the COVID-19 pandemic, through socio-economic stressors in households, disproportionate morbidity and mortality in immigrant communities, and social isolation and loss of learning due to school closures and the shift to online learning. The Study of Adolescent Lives after Migration to America [SALaMA] investigates the mental health and wellbeing of adolescents who come from–or who have parents who came from–the Middle East and North Africa [MENA] region and settled in the U.S. There is a gap in understanding of the experiences during the pandemic of MENA-background adolescents in the U.S. The objective of this study was to describe the perspective of educators and other school-affiliated service providers on the impact of the COVID-19 pandemic on mental health and wellbeing of adolescent resettled refugees and access to and quality of education and support services for adolescent resettled refugees. The researchers collected data using in-depth interviews with key informants in Chicago, Illinois; Harrisonburg, Virginia; and Detroit Metropolitan Area [DMA], Michigan, Key informants were school administrators, managers of English language learning services and programs, teachers, therapists, staff of non-governmental organizations and/ or community-based organizations, and case workers. Data analysis was conducted utilizing directed content analysis to develop an initial codebook and identify key themes in the data. Findings revealed a number of pathways through which the pandemic impacted adolescent refugees and immigrants’ mental health and wellbeing, with online programming impacting students’ engagement, motivation and social isolation in terms of peer and provider relationships. Specific dynamics in refugee adolescents’ households increased stressors and reduced engagement through online learning, and access to space and resources needed to support learning during school closures were limited. Service providers emphasized multiple and overlapping impacts on service quality and access, resulting in reduced social supports and mental health prevention and response approaches. Due to the long-term impacts of school closures in the first two years of the pandemic, and ongoing disruption, these data both provide a snapshot of the impacts of the pandemic at a specific moment, as well as insights into ways forward in terms of adapting services and engaging students within restrictions and limitations due to the pandemic. These findings emphasize the need for educators and mental health service providers to rebuild and strengthen relationships with students and families. These findings indicate the need to consider, support and expand social support and mental health services, specifically for refugee adolescent students, in the context of learning and well-being during the COVID-19 pandemic.

  12. u

    Data from: Doctors of Tomorrow: Evaluating the effectiveness and impact of a...

    • deepblue.lib.umich.edu
    Updated Mar 26, 2024
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    Irani, Sanaya; Tolia, Sangini; Finks, Jonathan; Sandhu, Gurjit (2024). Doctors of Tomorrow: Evaluating the effectiveness and impact of a virtual medical pipeline program during the COVID-19 pandemic [Dataset]. http://doi.org/10.7302/4s9f-q855
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    Dataset updated
    Mar 26, 2024
    Dataset provided by
    Deep Blue Data
    Authors
    Irani, Sanaya; Tolia, Sangini; Finks, Jonathan; Sandhu, Gurjit
    License

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

    Description

    Program Description DoT was founded in 2012 with a mission to increase diversity amongst medical professionals by preparing students from underrepresented communities in Detroit to successfully pursue careers in healthcare. Our program builds on a partnership between Cass Technical High School (CTHS) and the University of Michigan Medical School (UMMS). The CTHS student body is reflective of the Detroit population with more than 80% of students identifying with racial and ethnic minority backgrounds. Students with an interest in healthcare apply for the program as ninth graders. In recent years, the program has received over 60 applications for approximately 30 positions in each grade.

     DoT’s unique strength lies in its longitudinal structure. There are three branches of the program – Foundations (ninth and tenth grade), Rising (eleventh and twelfth grade) and Succeed (undergraduate). Ninth graders start out in DoT Foundations. Each student is paired with a first-year medical student mentor at UMMS for the entire academic year. DoT students travel to UMMS every month for a visit day, with activities designed to give students hands-on experiences in healthcare, such as suturing and ultrasound skills in the simulation center, and clinical shadowing. Students then meet with their medical student mentor over lunch. The latter part of the day is dedicated to working on their capstone projects. For the capstone projects, students work in small teams led by medical student leaders to identify a community health issue, partner with a local organization, and present their proposed solutions at a formal symposium at the end of the year.
    

    ;Transition to Virtual Programming In light of the recent COVID-19 pandemic, a growing number of universities cancelled all campus events including those of pipeline programs. We felt that our programming offered an important service to our students that would be greatly missed, so our team worked to quickly create and implement a virtual program. We ensured that each of our students had access to technology at home and those who did not were offered scholarships. During our introductory student session and new parent meeting, our leadership team discussed how to set up a Gmail email address for weekly communications and taught the students how to use Zoom, Google Drive, Google Docs and Google Sheets for online learning collaboration.

     For the virtual Foundations program, we offered 1-hour seminars each month, where a physician was invited to give a 30-minute presentation about different organ systems, followed by a 30-minute case-based session where students worked with medical student mentors to apply their new knowledge. We also created novel sessions such as “The Path to College and Medical School” and collaborated with members of the Black Medical Association (BMA) and Latin American and Native American Medical Association (LANAMA) to host a panel session where students could learn from medical students who identified as URiM. 
    
     For the mentorship aspect, we created “pods” of Foundations, Rising, and Succeed students along with medical student and physician mentors. The Foundations students and mentors met every month for an hour on Zoom, a virtual communication platform, to work on their Capstone project. Rising and Succeed students joined the group for three full-pod meetings. The goal was to increase near-peer mentorship and connections between DoT students at all levels. ;Study Population 
    Due to the virtual nature of the 2020-2021 program, we accepted 100% of 9th grade applicants from CTHS. We also expanded our reach to a new school, The School at Marygrove (TSM), which is also located in Detroit, Michigan. TSM is involved in the Detroit-20 Partnership with the University of Michigan College of Education and includes a novel three-year residency program for novice teachers.
    
     During the 2020-2021 school year, 108 students participated in the Foundations programming with 72 of them being 9th graders and 36 being 10th graders. The students were mostly from CTHS with 12 students out of the 108 total being from TSM. Students were predominantly from an African American/Black racial background (68.4% from N=98 respondents). The students were representative of their respective schools. The majority of students at CTHS identify as black, come from low-income homes, and have variable levels of parental education.
    
  13. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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City of Detroit (2021). Detroit City COVID Confirmed Cases and Rates by ZIP Code [Dataset]. https://data.detroitmi.gov/datasets/detroitmi::detroit-city-covid-confirmed-cases-and-rates-by-zip-code/about

Detroit City COVID Confirmed Cases and Rates by ZIP Code

Explore at:
Dataset updated
Apr 5, 2021
Dataset authored and provided by
City of Detroit
Area covered
Detroit
Description

Detroit-specific ZIP code populations, along with their cumulative COVID case counts, deaths, and rates. Data provided by Detroit Health Department. The public-facing COVID Cases Dashboard is hosted at: detroitmi.gov/healthUPDATE* July 29 2021:The underlying calculation for disease date was updated to allow for individuals to appear on the curve in multiple locations if they experienced more than one case of COVID-19 that was at least 90 days apart.Geospatial information analysis was also improved and additional criterial for address clean up were implemented, which leads to more accurate case counts within Zip Codes. Some unverified addresses that may have appeared in previous Zip Code counts are now excluded.This change discourages direct comparison of dashboard visualizations and counts prior to the new calculation, and non-significant shifts in numbers will be noticed.Case numbers represent Detroit residents only. Some ZIP codes with very low case counts are excluded to protect privacy. Case counts are totals per ZIP code and are not adjusted for population. ZIP code totals are preliminary; addresses are updated as new information becomes available and counts are subject to change. Not all cases have an accurate location; only cases with a known ZIP code are represented. Where a ZIP code is split between cities, only the Detroit portion is shown (48203, 48211, 48212, 48236, 48239). The counts exclude cases among prisoners at the Wayne County Jail and known hospital or laboratory locations.ZIP_Code: The USPS ZIP postal code Clipped_ZIP_Population: The 2010 population of the ZIP code, clipped to include Detroit City residents only.ZIP_Case_Count: The current cumulative count of Confirmed COVID cases within the ZIP code, since the beginning of the pandemic. (Have a "Confimed" case status in MDSS)ZIP_Death_Count: The current cumulative count of Confirmed COVID cases within the ZIP code, since the beginning of the pandemic. (Have a "Confimed" case status in MDSS and are deceased)ZIP_Case_Rate: Rate of confirmed cases per 100 thousand residents in the ZIP code. For each zip, the rate was calculated by (C/P)*100000 C = the count of confirmed (MDSS case status = Confirmed) cases with a resident address in the ZIP code P = the population count of the ZIP codeZIP_Death_Rate: Rate of confirmed cases that were marked deceased, per 100 thousand residents in the ZIP code. For each zip, the rate was calculated by (D/P)*100000 D = the count of confirmed (MDSS case status = Confirmed) cases marked as deceased, with a resident address in the ZIP P = the population count of the ZIP code

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