91 datasets found
  1. g

    Coronavirus (Covid-19) Data in the United States

    • github.com
    • openicpsr.org
    • +3more
    csv
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    New York Times, Coronavirus (Covid-19) Data in the United States [Dataset]. https://github.com/nytimes/covid-19-data
    Explore at:
    csvAvailable download formats
    Dataset provided by
    New York Times
    License

    https://github.com/nytimes/covid-19-data/blob/master/LICENSEhttps://github.com/nytimes/covid-19-data/blob/master/LICENSE

    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 the first reported coronavirus case in Washington State on Jan. 21, 2020, 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.

  2. Living, working and COVID-19 data

    • data.europa.eu
    html
    Updated May 6, 2020
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Eurofound (2020). Living, working and COVID-19 data [Dataset]. https://data.europa.eu/88u/dataset/living-working-and-covid-19-data
    Explore at:
    htmlAvailable download formats
    Dataset updated
    May 6, 2020
    Dataset provided by
    European Foundation for the Improvement of Living and Working Conditionshttp://www.eurofound.europa.eu/
    Authors
    Eurofound
    License

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

    Description

    Eurofound's e-survey 'Living, working and COVID-19' captures how the pandemic impacts living and working in Europe. The survey looks at quality of life and well-being, with questions ranging from life satisfaction, happiness and optimism, to health and levels of trust in institutions. Respondents are also asked about their work situation, their work–life balance and level of teleworking during COVID-19. The survey also assesses the impact of the pandemic on people’s living conditions and financial situation.

  3. Coronavirus (Covid-19) Data in the United States

    • openicpsr.org
    Updated Dec 7, 2020
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    New York Times (2020). Coronavirus (Covid-19) Data in the United States [Dataset]. http://doi.org/10.3886/E128303V1
    Explore at:
    Dataset updated
    Dec 7, 2020
    Dataset provided by
    The New York Timeshttp://nytimes.com/
    Authors
    New York Times
    Time period covered
    Jan 21, 2020 - Nov 22, 2020
    Area covered
    United States
    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. This time series data is being compiled from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak. This deposit contains live data from three geographic levels: U.S., states and counties. ICPSR staff scraped these data on 11/22/2020. For the most current data, please visit https://github.com/nytimes/covid-19-data.

  4. R

    WageIndicator Survey of Living and Working in Coronavirus Times

    • dataverse.iza.org
    • datasets.iza.org
    zip
    Updated Feb 21, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Research Data Center of IZA (IDSC) (2024). WageIndicator Survey of Living and Working in Coronavirus Times [Dataset]. http://doi.org/10.15185/wif.corona.1
    Explore at:
    zip(1577392), zip(122268054)Available download formats
    Dataset updated
    Feb 21, 2024
    Dataset provided by
    Research Data Center of IZA (IDSC)
    License

    https://www.iza.org/wc/dataverse/IIL-1.0.pdfhttps://www.iza.org/wc/dataverse/IIL-1.0.pdf

    Area covered
    Brazil, Puerto Rico, Lebanon, Lithuania, Kenya, Burundi, Ukraine, Morocco, New Zealand, Panama
    Description

    WageIndicator is interviewing people around the world to discover what makes the Coronavirus lockdown easier (or tougher), and what is the COVID-19 effect on our jobs, lives and mood. WageIndicator shows coronavirus-induced changes in living and working conditions in over 110 countries on the basis of answers on the following questions among others in the Corona survey: Is your work affected by the corona crisis? Are precautionary measures taken at the workplace? Do you have to work from home? Has your workload increased/decreased? Have you lost your job/work/assignments? The survey contains questions about the home situation of respondents as well as about the possible manifestation of the corona disease in members of the household. Also the effect of having a pet in the house in corona-crisis times is included.

  5. d

    MD COVID-19 - Total Cases in Congregate Facility Settings (Nursing Homes,...

    • datasets.ai
    • opendata.maryland.gov
    • +1more
    23, 40, 55, 8
    Updated Sep 20, 2024
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    State of Maryland (2024). MD COVID-19 - Total Cases in Congregate Facility Settings (Nursing Homes, Assisted Living, State and Local Facilities and Group Homes with +10 Residents) [Dataset]. https://datasets.ai/datasets/md-covid-19-total-cases-in-congregate-facility-settings-nursing-homes-assisted-living-stat
    Explore at:
    23, 8, 40, 55Available download formats
    Dataset updated
    Sep 20, 2024
    Dataset authored and provided by
    State of Maryland
    Area covered
    Maryland
    Description

    Summary This layer has been DEPRECATED. (last updated 12/1/2021). Was formerly a weekly update.

    The Outbreak-Associated Cases in Congregate Living data dashboard on coronavirus.maryland.gov was redesigned on 11/17/21 to align with other outbreak reporting. Visit https://opendata.maryland.gov/dataset/MD-COVID-19-Congregate-Outbreak/ey5n-qn5s to view Outbreak-Associated Cases in Congregate Living data as reported after 11/17/21.

    Confirmed COVID-19 cases among Maryland residents who live and work in congregate living facilities in Maryland for the reporting period.

    Description The MD COVID-19 - Total Cases in Congregate Facility Settings data layer is a total of positive COVID-19 test results have been reported to MDH in nursing homes, assisted living facilities, group homes of 10 or more and state and local facilities for the reporting period. Data are reported to MDH by local health departments, the Department of Public Safety and Correctional Services and the Department of Juvenile Services. To appear on the list, facilities report at least one confirmed case of COVID-19 over the prior 14 days. Facilities are removed from the list when health officials determine 14 days have passed with no new cases and no tests pending. The list provides a point-in-time picture of COVID-19 case activity among these facilities. Numbers reported for each facility listed reflect totals ever reported for cases. Data are updated once weekly.

    Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

  6. COVID-19 Trends in Each Country

    • coronavirus-response-israel-systematics.hub.arcgis.com
    • coronavirus-resources.esri.com
    • +2more
    Updated Mar 27, 2020
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Urban Observatory by Esri (2020). COVID-19 Trends in Each Country [Dataset]. https://coronavirus-response-israel-systematics.hub.arcgis.com/maps/a16bb8b137ba4d8bbe645301b80e5740
    Explore at:
    Dataset updated
    Mar 27, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Urban Observatory by Esri
    Area covered
    Earth
    Description

    On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: World Health Organization (WHO)For more information, visit the Johns Hopkins Coronavirus Resource Center.COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.DOI: https://doi.org/10.6084/m9.figshare.125529863/7/2022 - Adjusted the rate of active cases calculation in the U.S. to reflect the rates of serious and severe cases due nearly completely dominant Omicron variant.6/24/2020 - Expanded Case Rates discussion to include fix on 6/23 for calculating active cases.6/22/2020 - Added Executive Summary and Subsequent Outbreaks sectionsRevisions on 6/10/2020 based on updated CDC reporting. This affects the estimate of active cases by revising the average duration of cases with hospital stays downward from 30 days to 25 days. The result shifted 76 U.S. counties out of Epidemic to Spreading trend and no change for national level trends.Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Correction on 6/1/2020Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Revisions added on 4/30/2020 are highlighted.Revisions added on 4/23/2020 are highlighted.Executive SummaryCOVID-19 Trends is a methodology for characterizing the current trend for places during the COVID-19 global pandemic. Each day we assign one of five trends: Emergent, Spreading, Epidemic, Controlled, or End Stage to geographic areas to geographic areas based on the number of new cases, the number of active cases, the total population, and an algorithm (described below) that contextualize the most recent fourteen days with the overall COVID-19 case history. Currently we analyze the countries of the world and the U.S. Counties. The purpose is to give policymakers, citizens, and analysts a fact-based data driven sense for the direction each place is currently going. When a place has the initial cases, they are assigned Emergent, and if that place controls the rate of new cases, they can move directly to Controlled, and even to End Stage in a short time. However, if the reporting or measures to curtail spread are not adequate and significant numbers of new cases continue, they are assigned to Spreading, and in cases where the spread is clearly uncontrolled, Epidemic trend.We analyze the data reported by Johns Hopkins University to produce the trends, and we report the rates of cases, spikes of new cases, the number of days since the last reported case, and number of deaths. We also make adjustments to the assignments based on population so rural areas are not assigned trends based solely on case rates, which can be quite high relative to local populations.Two key factors are not consistently known or available and should be taken into consideration with the assigned trend. First is the amount of resources, e.g., hospital beds, physicians, etc.that are currently available in each area. Second is the number of recoveries, which are often not tested or reported. On the latter, we provide a probable number of active cases based on CDC guidance for the typical duration of mild to severe cases.Reasons for undertaking this work in March of 2020:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-25 days + 5% from past 26-49 days - total deaths. On 3/17/2022, the U.S. calculation was adjusted to: Active Cases = 100% of new cases in past 14 days + 6% from past 15-25 days + 3% from past 26-49 days - total deaths. Sources: https://www.cdc.gov/mmwr/volumes/71/wr/mm7104e4.htm https://covid.cdc.gov/covid-data-tracker/#variant-proportions If a new variant arrives and appears to cause higher rates of serious cases, we will roll back this adjustment. We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source

  7. e

    Coronavirus resources: US state and local health deparments (Live Science)

    • coronavirus-resources.esri.com
    • data.amerigeoss.org
    Updated Mar 16, 2020
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Esri’s Disaster Response Program (2020). Coronavirus resources: US state and local health deparments (Live Science) [Dataset]. https://coronavirus-resources.esri.com/documents/4b3f5f45d8ef4638a42dde9911190760
    Explore at:
    Dataset updated
    Mar 16, 2020
    Dataset authored and provided by
    Esri’s Disaster Response Program
    Description

    Coronavirus resources: US state and local health deparments (Live Science web page)._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

  8. a

    COVID-19 US Confirmed Cases

    • hub.arcgis.com
    • disaster-amerigeoss.opendata.arcgis.com
    Updated Apr 11, 2020
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    CivicImpactJHU (2020). COVID-19 US Confirmed Cases [Dataset]. https://hub.arcgis.com/maps/c477155f93d940d0ba01828900a7ff7d
    Explore at:
    Dataset updated
    Apr 11, 2020
    Dataset authored and provided by
    CivicImpactJHU
    Area covered
    Description

    On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit the following sources:Global: World Health Organization (WHO)U.S.: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.This web map contains the most up-to-date information on confirmed cases of the coronavirus COVID-19 in the US. Data is pulled from the Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, the Red Cross, the Census American Community Survey, and the Bureau of Labor and Statistics, and aggregated at the US county level. This web map created and maintained by the Centers for Civic Impact at the Johns Hopkins University, and is supported by the Esri Living Atlas team and JHU Data Services. It is used in the COVID-19 United States Cases by County dashboard. For more information on Johns Hopkins University’s response to COVID-19, visit the Johns Hopkins Coronavirus Resource Center where our experts help to advance understanding of the virus, inform the public, and brief policymakers in order to guide a response, improve care, and save lives.

  9. d

    Development of a new method, Rapid Viability RT-PCR, for Detection of Live...

    • datasets.ai
    • s.cnmilf.com
    • +2more
    10, 33
    Updated Aug 6, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    U.S. Environmental Protection Agency (2024). Development of a new method, Rapid Viability RT-PCR, for Detection of Live (Infectious) Coronavirus (SARS-CoV-2) that causes COVID-19 from swab. [Dataset]. https://datasets.ai/datasets/development-of-a-new-method-rapid-viability-rt-pcr-for-detection-of-live-infectious-corona
    Explore at:
    33, 10Available download formats
    Dataset updated
    Aug 6, 2024
    Dataset authored and provided by
    U.S. Environmental Protection Agency
    Description

    There is a need for development of an analytical method for rapid detection of SARS-CoV-2 virus which is causing the COVID-19 pandemic. Currently available traditional tissue/cell culture-based analytical method is too laborious and takes several days to get the results on the presence/absence of viable/infectious virus in a sample. Such a delay in getting the sample analysis results can be a serious obstacle in rapidly determining the presence of infectious virus in environment which, in turn, can impact environmental epidemiological investigations and studies on surface transmission of this virus. In this manuscript, development of a Rapid Viability Reverse Transcriptase Polymerase Chain Reaction (RV-RT-PCR) method that can significantly reduce the time-to-results for sample analysis from several days to less than a day is described. The RV-RT-PCR method integrates cell-culture based enrichment of the virus with virus-specific RT-PCR analysis. The RTPCR analysis is conducted before and after the cell-culture-virus (sample) incubation. An optimum algorithm is established such that the resultant RT-PCR cycle threshold (CT) value difference between before and after cell-culture-virus incubation RT-PCR analyses determines the presence of viable/infectious virus in the sample. The data set included here is from this research work. A manuscript has also been included here along with the Supplemental Tables for additional data. The Data-Metadata file includes all the data and a glossary to explain the scientific terms used.

    This dataset is associated with the following publication: Shah, S., S. Kane, M. Elsheikh, and T. Alfaro. Development of a Rapid Viability RT-PCR (RV-RT-PCR) Method to Detect Infectious SARS-CoV-2 from Swabs. JOURNAL OF VIROLOGICAL METHODS. Elsevier Science Ltd, New York, NY, USA, 297: 114251, (2021).

  10. U.S. Counties and Territories for COVID-19 Trends

    • disasterpartners.org
    Updated Apr 28, 2020
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Urban Observatory by Esri (2020). U.S. Counties and Territories for COVID-19 Trends [Dataset]. https://www.disasterpartners.org/datasets/49c25e0ce50340e08fcfe51fe6f26d1e
    Explore at:
    Dataset updated
    Apr 28, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Urban Observatory by Esri
    Area covered
    Description

    On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.Trends represent the day-to-day rate of new cases with a focus on the most recent 10 to 14 days. Includes Puerto Rico, Guam, Northern Marianas, and U.S. Virgin Islands. Daily new case counts are volatile for many reasons and sometimes the trends reflect that volatility. Thus, we decided to include longer-term summaries here. County Trends as of 9 Mar 20230 (-0) in Emergent1135 (+51) in Spreading1664 (-63) in Epidemic230 (+10) in Controlled110 (+2) in End StageNotes: Many states now only report once per week, and FL only once every two weeks. On 3/7/2022 we adjusted the formula for active cases to reflect the Omicron Variant which is documented to cause lower rates of serious and severe illness. To produce these trends we analyze daily updates from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.For more information about COVID-19 trends, see our country level trends story map and the full methodology.Data Source: Johns Hopkins University CSSE US Cases by County dashboard and USAFacts for Utah County level Data.Feature layer generated from running the Join Features solution that is the basis for daily updates for the U.S. County COVID-19 Tends Story Map.

  11. d

    DOHMH Covid-19 Milestone Data: New Cases of Covid-19 (7 Day Average)

    • datasets.ai
    • data.cityofnewyork.us
    • +1more
    23, 40, 55, 8
    Updated Oct 9, 2024
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    City of New York (2024). DOHMH Covid-19 Milestone Data: New Cases of Covid-19 (7 Day Average) [Dataset]. https://datasets.ai/datasets/dohmh-covid-19-milestone-data-new-cases-of-covid-19-7-day-average
    Explore at:
    40, 55, 23, 8Available download formats
    Dataset updated
    Oct 9, 2024
    Dataset authored and provided by
    City of New York
    Description

    This dataset shows daily confirmed and probable cases of COVID-19 in New York City by date of specimen collection. Total cases has been calculated as the sum of daily confirmed and probable cases. Seven-day averages of confirmed, probable, and total cases are also included in the dataset. A person is classified as a confirmed COVID-19 case if they test positive with a nucleic acid amplification test (NAAT, also known as a molecular test; e.g. a PCR test). A probable case is a person who meets the following criteria with no positive molecular test on record: a) test positive with an antigen test, b) have symptoms and an exposure to a confirmed COVID-19 case, or c) died and their cause of death is listed as COVID-19 or similar. As of June 9, 2021, people who meet the definition of a confirmed or probable COVID-19 case >90 days after a previous positive test (date of first positive test) or probable COVID-19 onset date will be counted as a new case. Prior to June 9, 2021, new cases were counted ≥365 days after the first date of specimen collection or clinical diagnosis. Any person with a residence outside of NYC is not included in counts. Data is sourced from electronic laboratory reporting from the New York State Electronic Clinical Laboratory Reporting System to the NYC Health Department. All identifying health information is excluded from the dataset.

    These data are used to evaluate the overall number of confirmed and probable cases by day (seven day average) to track the trajectory of the pandemic. Cases are classified by the date that the case occurred. NYC COVID-19 data include people who live in NYC. Any person with a residence outside of NYC is not included.

  12. V

    COVID-19 Healthcare Coalition data and tools

    • data.virginia.gov
    html, pdf
    Updated Feb 3, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Other (2024). COVID-19 Healthcare Coalition data and tools [Dataset]. https://data.virginia.gov/dataset/covid-19-healthcare-coalition-data-and-tools
    Explore at:
    html, pdfAvailable download formats
    Dataset updated
    Feb 3, 2024
    Dataset authored and provided by
    Other
    Description

    From the Web site: The COVID-19 Healthcare Coalition is a collaborative private-industry response to novel coronavirus. Our mission is to save lives by providing real-time learning to preserve healthcare delivery and protect people. We’re brought together the best, brightest minds, assets and insights from across private industry to coordinate a response. We’re sharing resources, sharing plans, and working together.

  13. Sources of information about the COVID-19 / coronavirus pandemic 2020

    • statista.com
    Updated Jul 3, 2020
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2020). Sources of information about the COVID-19 / coronavirus pandemic 2020 [Dataset]. https://www.statista.com/statistics/1108009/sources-of-information-about-the-covid-19-corona-pandemic/
    Explore at:
    Dataset updated
    Jul 3, 2020
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    May 25, 2020 - May 31, 2020
    Area covered
    Germany, United Kingdom, United States
    Description

    Respondents from Germany, United Kingdom and the United States primarily rely on TV to stay informed about COVID-19. A rather modest share of respondents from either one of these countries, however, depend on podcasts or blogs as information source regarding the pandemic. Effect of coronavirus on global internet usage All over the world, coronavirus has forced millions of people to stay at home. They are self-isolating to save themselves and their social environment from a further spread of the pandemic. Businesses and markets are closed, and people have more time to spend online. This might be one of the reasons why global internet traffic increased by 20 percent in April 2020 as compared to January and February of that year. Different industries experienced an increase in online traffic during this period: Supermarkets and retail tech for instance, were greatly in-demand among the online consumers. Coronavirus and fake news The internet is overflowing with news about coronavirus. People claim more information about the pandemic that has brought their lives to a standstill and they are certainly getting it via all kinds of media. But how much of the news is deemed reliable? As of May 2020, respondents around the world are worried that a lot of fake news about COVID-19 is being spread. Some are finding it difficult to get reliable information about this topic, and almost half of respondents in the U.S. (aged 18 and older) which are democrats or republicans, encountered made-up news about this pandemic.

  14. Opinions on live music attendance during the coronavirus outbreak in Italy...

    • statista.com
    Updated May 29, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2024). Opinions on live music attendance during the coronavirus outbreak in Italy 2020 [Dataset]. https://www.statista.com/statistics/1112865/opinions-on-live-music-attendance-during-coronavirus-outbreak-italy/
    Explore at:
    Dataset updated
    May 29, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Apr 18, 2020 - Apr 22, 2020
    Area covered
    Italy
    Description

    A survey from April 2020 analyzed opinions in Italy about the live music industry and the coronavirus (COVID-19) outbreak. Roughly seven in ten respondents believed that live music events will restart in 2021. Specifically, about 45 percent of interviewees thought these events will restart in March 2021. Moreover, almost 30 percent of the survey sample imagined that it will not be possible to attend live music gigs before January 2021. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

  15. e

    JHU Centers for Civic Impact Covid-19 County Cases (Daily Update)

    • coronavirus-resources.esri.com
    • covid-hub.gio.georgia.gov
    • +6more
    Updated Apr 11, 2020
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    CivicImpactJHU (2020). JHU Centers for Civic Impact Covid-19 County Cases (Daily Update) [Dataset]. https://coronavirus-resources.esri.com/maps/4cb598ae041348fb92270f102a6783cb
    Explore at:
    Dataset updated
    Apr 11, 2020
    Dataset authored and provided by
    CivicImpactJHU
    Area covered
    Description

    On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit the following sources:Global: World Health Organization (WHO)U.S.: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.This feature layer contains the most up-to-date COVID-19 cases for the US. Data is pulled from the Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, the Red Cross, the Census American Community Survey, and the Bureau of Labor and Statistics, and aggregated at the US county level. This web map created and maintained by the Centers for Civic Impact at the Johns Hopkins University, and is supported by the Esri Living Atlas team and JHU Data Services. It is used in the COVID-19 United States Cases by County dashboard. For more information on Johns Hopkins University’s response to COVID-19, visit the Johns Hopkins Coronavirus Resource Center where our experts help to advance understanding of the virus, inform the public, and brief policymakers in order to guide a response, improve care, and save lives.

  16. g

    Coronavirus COVID-19 Global Cases by the Center for Systems Science and...

    • github.com
    • systems.jhu.edu
    • +1more
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE), Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) [Dataset]. https://github.com/CSSEGISandData/COVID-19
    Explore at:
    Dataset provided by
    Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE)
    Area covered
    Global
    Description

    2019 Novel Coronavirus COVID-19 (2019-nCoV) Visual Dashboard and Map:
    https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

    • Confirmed Cases by Country/Region/Sovereignty
    • Confirmed Cases by Province/State/Dependency
    • Deaths
    • Recovered

    Downloadable data:
    https://github.com/CSSEGISandData/COVID-19

    Additional Information about the Visual Dashboard:
    https://systems.jhu.edu/research/public-health/ncov

  17. a

    Florida COVID19 Caseline Data 2020 Update 06032021 CSV

    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • covid19-usflibrary.hub.arcgis.com
    Updated Jun 3, 2021
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    University of South Florida GIS (2021). Florida COVID19 Caseline Data 2020 Update 06032021 CSV [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/datasets/usflibrary::florida-covid19-caseline-data-2020-update-06032021-csv
    Explore at:
    Dataset updated
    Jun 3, 2021
    Dataset authored and provided by
    University of South Florida GIS
    Area covered
    Florida
    Description

    Florida COVID-19 Case Line data, exported from the Florida Department of Health (FDOH) GIS Layer on date seen in file name. Archived by the University of South Florida Libraries, Digital Heritage and Humanities Collections. Contact: LibraryGIS@usf.edu. Starting on 4/6/2021, the Florida Department of Health (FDOH) changed the way they provide COVID-19 caseline data. Beginning with this date forward, the caseline data is being archived as two separate files, one for 2020 and one for 2021. The 2020 file is still being modified by the FDOH, with attributes and cases added for events recorded in 2020. These data will continue to be archived by the USF Libraries DHHC as long as they are still being modified by the FDOH, to provide legacy archive, data research, and transparency-checking potential. Please Cite Our GIS HUB. If you are a researcher or other utilizing our Florida COVID-19 HUB as a tool or accessing and utilizing the data provided herein, please provide an acknowledgement of such in any publication or re-publication. The following citation is suggested: University of South Florida Libraries, Digital Heritage and Humanities Collections. 2020-2021. Florida COVID-19 Hub. Available at https://covid19-usflibrary.hub.arcgis.com/. https://doi.org/10.5038/USF-COVID-19-GISLive FDOH Data Source: https://www.arcgis.com/home/item.html?id=1d8756918efd40258ae05723f1c4ece0 or Direct Download at: https://www.arcgis.com/home/item.html?id=a47fdde491934397b7e94043e71eb741. Archives for this data layer begin on 5/11/2020. Archived data was exported directly from the live FDOH layer into the archive by the University of South Florida Libraries - Digital Heritage and Humanities Collection.For data definitions please visit the following box folder: https://usf.box.com/s/vfjwbczkj73ucj19yvwz53at6v6w614hData definition files names include the relative date they were published. The below information was taken from ancillary documents associated with the original layer from the Florida Department of Health. This data table represents all laboratory-confirmed cases of COVID-19 in Florida tabulated from the previous day's totals by the Florida Department of Health. Persons Under Investigation/Surveillance (PUI):Essentially, PUIs are any person who has been or is waiting to be tested. This includes: persons who are considered high-risk for COVID-19 due to recent travel, contact with a known case, exhibiting symptoms of COVID-19 as determined by a healthcare professional, or some combination thereof. PUI’s also include people who meet laboratory testing criteria based on symptoms and exposure, as well as confirmed cases with positive test results. PUIs include any person who is or was being tested, including those with negative and pending results.All PUIs fit into one of three residency types:1. Florida residents tested in Florida2. Non-Florida residents tested in Florida 3. Florida residents tested outside of Florida Florida Residents Tested Elsewhere: The total number of Florida residents with positive COVID-19 test results who were tested outsideof Florida, and were not exposed/infectious in Florida. Non-Florida Residents Tested in Florida: The total number of people with positive COVID-19 test results who were tested, exposed, and/or infectious while in Florida, but are legal residents of another state.Table Guide for Records of Confirmed Positive Cases of COVID-19"County": The Florida county where the individual with COVID-19's case has been processed. "Jurisdiction" of the case:"FL resident" -- a resident of Florida"Non-FL resident" -- someone who resides outside of Florida "Travel_Related": Whether or not the positive case of COVID-19 is designated as related to recent travel by the individual. "No" -- Case designated as not being a risk related to recent travel"Unknown" -- Case designated where a travel-related designation has not yet been made."Yes" -- Case is designated as travel-related for a person who recently traveled overseas or to an area with community"Origin": Where the person likely contracted the virus before arriving / returning to Florida."EDvisit": Whether or not an individual who tested positive for coronavirus visited and was admitted to an Emergency Department related to health conditions surrounding COVID-19."No" -- Individual was not admitted to an emergency department relating to health conditions surrounding the contraction of COVID-19"Unknown" -- It is unknown whether the individual was admitted to an emergency department relating to health conditions surrounding the contraction of COVID-19"Yes" -- Individual was admitted to an emergency department relating to health conditions surrounding the contraction of COVID-19“Hospitalized”: Whether or not a patient who receives a positive laboratory confirmed test for COVID-19 receives inpatient care at a hospital at any time during illness. These people may no longer be hospitalized. This information does not indicate that a COVID-19 positive person is currently hospitalized, only that they have been hospitalized for health conditions relating to COVID-19 at some point during their illness. "No" -- Individual was not admitted for inpatient care at a hospital at any time during illness "Unknown" -- It is unknown whether the individual was admitted for inpatient care at a hospital at any time during illness "Yes" -- Individual was admitted for inpatient care at a hospital at some point during the illness "Died": Whether or not the individual who tested positive for COVID-19 died as a result of health complications from the viral infection. "NA" -- Not applicable / resident has not died "Yes" -- Individual died of a health complication resulting from COVID-19 "Contact": Whether the person contracted COVID-19 from contact with current or previously confirmedcases."No" -- Case with no known contact with current or previously confirmed cases"Yes" -- Case with known contact with current or previously confirmed cases"Unknown" -- Case where contact with current or previous confirmedcases is not known or under investigation"Case_": The date the positive laboratory result was received in the Department of Health’s database system and became a “confirmed case.” This is not the date a person contracted the virus, became symptomatic, or was treated. Florida does not create a case or count suspected/probable cases in the case counts without a confirmed-positive lab result. "EventDate": When the individual reported likely first experiencing symptoms related to COVID-19. "ChartDate": Also the date the positive laboratory result for an individual was received in the Department ofHealth’s database system and became a recorded, “confirmed case” of COVID-19 in the state. Data definitions updated by the FDOH on 5/13/2020.

  18. MDCOVID19 TotalDeathsInCongregateFacilitySettingsByCounty

    • hub.arcgis.com
    • data.imap.maryland.gov
    • +2more
    Updated Jul 8, 2020
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    ArcGIS Online for Maryland (2020). MDCOVID19 TotalDeathsInCongregateFacilitySettingsByCounty [Dataset]. https://hub.arcgis.com/datasets/8c1cc2f158df4f0ea70c2cb6c0be3751
    Explore at:
    Dataset updated
    Jul 8, 2020
    Dataset provided by
    Authors
    ArcGIS Online for Maryland
    Description

    SummaryConfirmed COVID-19 deaths among Maryland residents within a single Maryland jurisdiction who live and work in congregate living facilities for the reporting period.DescriptionDeprecated as of November 17, 2021.The Outbreak-Associated Cases in Congregate Living data dashboard on coronavirus.maryland.gov was redesigned on 11/17/21 to align with other outbreak reporting. Visit MD COVID-19 Congregate Outbreaks to view Outbreak-Associated Cases in Congregate Living data as reported after 11/17/21.The MD COVID-19 - Total Deaths in Congregate Facility Settings data layer is a total of deaths confirmed by a positive COVID-19 test result that have been reported to MDH in nursing homes, assisted living facilities, group homes of 10 or more and state and local facilities in each Maryland jurisdiction for the reporting period. Data are reported to MDH by local health departments, the Department of Public Safety and Correctional Services and the Department of Juvenile Services. To appear on the list, facilities report at least one confirmed case of COVID-19 over the prior 14 days. Facilities are removed from the list when health officials determine 14 days have passed with no new cases and no tests pending. The list provides a point-in-time picture of COVID-19 case activity among these facilities. Numbers reported for each facility listed reflect totals ever reported for deaths. Data are updated once weekly.COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county.

  19. O

    COVID-19 Daily Surveillance Data Public

    • data.sanantonio.gov
    • cosacovid-cosagis.hub.arcgis.com
    Updated Jan 18, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    COVID-19 (2023). COVID-19 Daily Surveillance Data Public [Dataset]. https://data.sanantonio.gov/dataset/covid-19-daily-surveillance-data-public
    Explore at:
    gpkg, kml, gdb, txt, geojson, zip, html, xlsx, csv, arcgis geoservices rest apiAvailable download formats
    Dataset updated
    Jan 18, 2023
    Dataset provided by
    City of San Antonio
    Authors
    COVID-19
    Description
    This is the daily information that are used in the public CoVID-19 Surveillance, Trends, and Progress and Warnings Dashboards. Each field is updated after 6pm CST Monday through Friday. Weekend data is added on Monday as individual records, along with Monday's reported data. The Surveillance Dashboard is live and available here.

    Backlog CoVID-19 cases are cases that are reported more than 14-days after the event date (date of Test or date of onset of symptoms). Backlog cases are reported along with the Monday Cumulative Cases, but are not included in in the daily Case Change.

    This data reflects information provided by the City of San Antonio Metro Health Department, and is released Monday through Friday at 6PM on the City of San Antonio CoVID-19 website.
  20. Coronavirus: World connectivity can save lives (Esri Newsroom)

    • coronavirus-resources.esri.com
    • coronavirus-disasterresponse.hub.arcgis.com
    Updated Mar 17, 2020
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Esri’s Disaster Response Program (2020). Coronavirus: World connectivity can save lives (Esri Newsroom) [Dataset]. https://coronavirus-resources.esri.com/documents/e9a45c03c4d34003b71b80c6e180c110
    Explore at:
    Dataset updated
    Mar 17, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri’s Disaster Response Program
    Description

    Coronavirus: World connectivity can save lives (Esri Newsroom). As pandemic fears escalated in late January, Johns Hopkins University published its now-famous coronavirus dashboard—a map-based tool developed to track and fight the spread of the disease now called COVID-19. Developed by Lauren Gardner and her team from the University’s Center for Systems Science and Engineering, the dashboard went viral almost instantly with hundreds of news articles and shares on social media and hundreds of millions of page views._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
New York Times, Coronavirus (Covid-19) Data in the United States [Dataset]. https://github.com/nytimes/covid-19-data

Coronavirus (Covid-19) Data in the United States

Explore at:
csvAvailable download formats
Dataset provided by
New York Times
License

https://github.com/nytimes/covid-19-data/blob/master/LICENSEhttps://github.com/nytimes/covid-19-data/blob/master/LICENSE

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 the first reported coronavirus case in Washington State on Jan. 21, 2020, 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.

Search
Clear search
Close search
Google apps
Main menu