Note: Note: Starting October 10th, 2025 this dataset is deprecated and is no longer being updated. As of April 27, 2023 updates changed from daily to weekly. Summary The cumulative number of confirmed COVID-19 deaths among Maryland residents by age: 0-9; 10-19; 20-29; 30-39; 40-49; 50-59; 60-69; 70-79; 80+; Unknown. Description The MD COVID-19 - Confirmed Deaths by Age Distribution data layer is a collection of the statewide confirmed COVID-19 related deaths that have been reported each day by the Vital Statistics Administration by designated age ranges. A death is classified as confirmed if the person had a laboratory-confirmed positive COVID-19 test result. Some data on deaths may be unavailable due to the time lag between the death, typically reported by a hospital or other facility, and the submission of the complete death certificate. Probable deaths are available from the MD COVID-19 - Probable Deaths by Age Distribution data layer. 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.

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.

This file contains COVID-19 death counts and rates by month and year of death, jurisdiction of residence (U.S., HHS Region) and demographic characteristics (sex, age, race and Hispanic origin, and age/race and Hispanic origin). United States death counts and rates include the 50 states, plus the District of Columbia. Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1. Number of deaths reported in this file are the total number of COVID-19 deaths received and coded as of the date of analysis and may not represent all deaths that occurred in that period. Counts of deaths occurring before or after the reporting period are not included in the file. Data during recent periods are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more, depending on the jurisdiction and cause of death. Death counts should not be compared across jurisdictions. Data timeliness varies by state. Some states report deaths on a daily basis, while other states report deaths weekly or monthly. The ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington. Rates were calculated using the population estimates for 2021, which are estimated as of July 1, 2021 based on the Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Blended Base consists of the blend of Vintage 2020 postcensal population estimates, 2020 Demographic Analysis Estimates, and 2020 Census PL 94-171 Redistricting File (see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2020-2021/methods-statement-v2021.pdf). Rate are based on deaths occurring in the specified week and are age-adjusted to the 2000 standard population using the direct method (see https://www.cdc.gov/nchs/data/nvsr/nvsr70/nvsr70-08-508.pdf). These rates differ from annual age-adjusted rates, typically presented in NCHS publications based on a full year of data and annualized weekly age-adjusted rates which have been adjusted to allow comparison with annual rates. Annualization rates presents deaths per year per 100,000 population that would be expected in a year if the observed period specific (weekly) rate prevailed for a full year. Sub-national death counts between 1-9 are suppressed in accordance with NCHS data confidentiality standards. Rates based on death counts less than 20 are suppressed in accordance with NCHS standards of reliability as specified in NCHS Data Presentation Standards for Proportions (available from: https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf.).

This dataset provides values for CORONAVIRUS DEATHS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Vaccination Status. Click 'More' for important dataset description and footnotes

Dataset and data visualization details: These data were posted on October 21, 2022, archived on November 18, 2022, and revised on February 22, 2023. These data reflect cases among persons with a positive specimen collection date through September 24, 2022, and deaths among persons with a positive specimen collection date through September 3, 2022.

Vaccination status: A person vaccinated with a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. Additional or booster dose: A person vaccinated with a primary series and an additional or booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after receipt of an additional or booster dose of any COVID-19 vaccine on or after August 13, 2021. For people ages 18 years and older, data are graphed starting the week including September 24, 2021, when a COVID-19 booster dose was first recommended by CDC for adults 65+ years old and people in certain populations and high risk occupational and institutional settings. For people ages 12-17 years, data are graphed starting the week of December 26, 2021, 2 weeks after the first recommendation for a booster dose for adolescents ages 16-17 years. For people ages 5-11 years, data are included starting the week of June 5, 2022, 2 weeks after the first recommendation for a booster dose for children aged 5-11 years. For people ages 50 years and older, data on second booster doses are graphed starting the week including March 29, 2022, when the recommendation was made for second boosters. Vertical lines represent dates when changes occurred in U.S. policy for COVID-19 vaccination (details provided above). Reporting is by primary series vaccine type rather than additional or booster dose vaccine type. The booster dose vaccine type may be different than the primary series vaccine type. ** Because data on the immune status of cases and associated deaths are unavailable, an additional dose in an immunocompromised person cannot be distinguished from a booster dose. This is a relevant consideration because vaccines can be less effective in this group. Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Rates of COVID-19 deaths by vaccination status are reported based on when the patient was tested for COVID-19, not the date they died. Deaths usually occur up to 30 days after COVID-19 diagnosis. Participating jurisdictions: Currently, these 31 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (New York), North Carolina, Philadelphia (Pennsylvania), Rhode Island, South Dakota, Tennessee, Texas, Utah, Washington, and West Virginia; 30 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 72% of the total U.S. population and all ten of the Health and Human Services Regions. Data on cases

Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Vaccination Status. Click 'More' for important dataset description and footnotes

Dataset and data visualization details: These data were posted on October 21, 2022, archived on November 18, 2022, and revised on February 22, 2023. These data reflect cases among persons with a positive specimen collection date through September 24, 2022, and deaths among persons with a positive specimen collection date through September 3, 2022.

Vaccination status: A person vaccinated with a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. Additional or booster dose: A person vaccinated with a primary series and an additional or booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after receipt of an additional or booster dose of any COVID-19 vaccine on or after August 13, 2021. For people ages 18 years and older, data are graphed starting the week including September 24, 2021, when a COVID-19 booster dose was first recommended by CDC for adults 65+ years old and people in certain populations and high risk occupational and institutional settings. For people ages 12-17 years, data are graphed starting the week of December 26, 2021, 2 weeks after the first recommendation for a booster dose for adolescents ages 16-17 years. For people ages 5-11 years, data are included starting the week of June 5, 2022, 2 weeks after the first recommendation for a booster dose for children aged 5-11 years. For people ages 50 years and older, data on second booster doses are graphed starting the week including March 29, 2022, when the recommendation was made for second boosters. Vertical lines represent dates when changes occurred in U.S. policy for COVID-19 vaccination (details provided above). Reporting is by primary series vaccine type rather than additional or booster dose vaccine type. The booster dose vaccine type may be different than the primary series vaccine type. ** Because data on the immune status of cases and associated deaths are unavailable, an additional dose in an immunocompromised person cannot be distinguished from a booster dose. This is a relevant consideration because vaccines can be less effective in this group. Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Rates of COVID-19 deaths by vaccination status are reported based on when the patient was tested for COVID-19, not the date they died. Deaths usually occur up to 30 days after COVID-19 diagnosis. Participating jurisdictions: Currently, these 31 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (New York), North Carolina, Philadelphia (Pennsylvania), Rhode Island, South Dakota, Tennessee, Texas, Utah, Washington, and West Virginia; 30 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 72% of the total U.S. population and all ten of the Health and Human Services Regions. Data on cases

Note: Data elements were retired from HERDS on 10/6/23 and this dataset was archived.

This dataset includes the cumulative number and percent of healthcare facility-reported fatalities for patients with lab-confirmed COVID-19 disease by reporting date and age group. This dataset does not include fatalities related to COVID-19 disease that did not occur at a hospital, nursing home, or adult care facility. The primary goal of publishing this dataset is to provide users with information about healthcare facility fatalities among patients with lab-confirmed COVID-19 disease.

The information in this dataset is also updated daily on the NYS COVID-19 Tracker at https://www.ny.gov/covid-19tracker.

The data source for this dataset is the daily COVID-19 survey through the New York State Department of Health (NYSDOH) Health Electronic Response Data System (HERDS). Hospitals, nursing homes, and adult care facilities are required to complete this survey daily. The information from the survey is used for statewide surveillance, planning, resource allocation, and emergency response activities. Hospitals began reporting for the HERDS COVID-19 survey in March 2020, while Nursing Homes and Adult Care Facilities began reporting in April 2020. It is important to note that fatalities related to COVID-19 disease that occurred prior to the first publication dates are also included.

The fatality numbers in this dataset are calculated by assigning age groups to each patient based on the patient age, then summing the patient fatalities within each age group, as of each reporting date. The statewide total fatality numbers are calculated by summing the number of fatalities across all age groups, by reporting date. The fatality percentages are calculated by dividing the number of fatalities in each age group by the statewide total number of fatalities, by reporting date. The fatality numbers represent the cumulative number of fatalities that have been reported as of each reporting date.

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COVID-19 Worldwide Daily Data

Daily global COVID-19 data for all countries, provided by Johns Hopkins University (JHU) Center for Systems Science and Engineering (CSSE). If you want to use the update version of the data, you can use our daily updated data with the help of api key by entering it via Altadata.

Overview

In this data product, you may find the latest and historical global daily data on the COVID-19 pandemic for all countries.

The COVID‑19 pandemic, also known as the coronavirus pandemic, is an ongoing global pandemic of coronavirus disease 2019 (COVID‑19), caused by severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2). The outbreak was first identified in December 2019 in Wuhan, China. The World Health Organization declared the outbreak a Public Health Emergency of International Concern on 30 January 2020 and a pandemic on 11 March. As of 12 August 2020, more than 20.2 million cases of COVID‑19 have been reported in more than 188 countries and territories, resulting in more than 741,000 deaths; more than 12.5 million people have recovered.

The Johns Hopkins Coronavirus Resource Center is a continuously updated source of COVID-19 data and expert guidance. They aggregate and analyze the best data available on COVID-19 - including cases, as well as testing, contact tracing and vaccine efforts - to help the public, policymakers and healthcare professionals worldwide respond to the pandemic.

Methodology

• Cases and Death counts include confirmed and probable (where reported)
• Recovered cases are estimates based on local media reports, and state and local reporting when available, and therefore may be substantially lower than the true number. US state-level recovered cases are from COVID Tracking Project.
• Active cases = total cases - total recovered - total deaths
• Incidence Rate = cases per 100,000 persons
• Case-Fatality Ratio (%) = Number recorded deaths / Number cases
• Country Population represents 2019 projections by UN Population Division, integrated to the JHU CSSE's COVID-19 data by ALTADATA

Data Source

• Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE)
• United Nations Population Division

Related Data Products

• COVID-19 US Daily Data
• OECD, EU28, G20 Life Expectancy and Mortality Indicators

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Data Dictionary

• Reported Date (reported_date) : Covid-19 Report Date
• Country_Region (country_region) : Country, region or sovereignty name
• Population (population) : Country populations as per United Nations Population Division
• Confirmed Case (confirmed) : Confirmed cases include presumptive positive cases and probable cases
• Active cases (active) : Active cases = total confirmed - total recovered - total deaths
• Deaths (deaths) : Death cases counts
• Recovered (recovered) : Recovered cases counts
• Mortality Rate (mortality_rate) : Number of recorded deaths * 100 / Number of confirmed cases
• Incident Rate (incident_rate) : Confirmed cases per 100,000 persons

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

To estimate county of residence of Filipinx healthcare workers who died of COVID-19, we retrieved data from the Kanlungan website during the month of December 2020.22 In deciding who to include on the website, the AF3IRM team that established the Kanlungan website set two standards in data collection. First, the team found at least one source explicitly stating that the fallen healthcare worker was of Philippine ancestry; this was mostly media articles or obituaries sharing the life stories of the deceased. In a few cases, the confirmation came directly from the deceased healthcare worker's family member who submitted a tribute. Second, the team required a minimum of two sources to identify and announce fallen healthcare workers. We retrieved 86 US tributes from Kanlungan, but only 81 of them had information on county of residence. In total, 45 US counties with at least one reported tribute to a Filipinx healthcare worker who died of COVID-19 were identified for analysis and will hereafter be referred to as “Kanlungan counties.” Mortality data by county, race, and ethnicity came from the National Center for Health Statistics (NCHS).24 Updated weekly, this dataset is based on vital statistics data for use in conducting public health surveillance in near real time to provide provisional mortality estimates based on data received and processed by a specified cutoff date, before data are finalized and publicly released.25 We used the data released on December 30, 2020, which included provisional COVID-19 death counts from February 1, 2020 to December 26, 2020—during the height of the pandemic and prior to COVID-19 vaccines being available—for counties with at least 100 total COVID-19 deaths. During this time period, 501 counties (15.9% of the total 3,142 counties in all 50 states and Washington DC)26 met this criterion. Data on COVID-19 deaths were available for six major racial/ethnic groups: Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Native Hawaiian or Other Pacific Islander, Non-Hispanic American Indian or Alaska Native, Non-Hispanic Asian (hereafter referred to as Asian American), and Hispanic. People with more than one race, and those with unknown race were included in the “Other” category. NCHS suppressed county-level data by race and ethnicity if death counts are less than 10. In total, 133 US counties reported COVID-19 mortality data for Asian Americans. These data were used to calculate the percentage of all COVID-19 decedents in the county who were Asian American. We used data from the 2018 American Community Survey (ACS) five-year estimates, downloaded from the Integrated Public Use Microdata Series (IPUMS) to create county-level population demographic variables.27 IPUMS is publicly available, and the database integrates samples using ACS data from 2000 to the present using a high degree of precision.27 We applied survey weights to calculate the following variables at the county-level: median age among Asian Americans, average income to poverty ratio among Asian Americans, the percentage of the county population that is Filipinx, and the percentage of healthcare workers in the county who are Filipinx. Healthcare workers encompassed all healthcare practitioners, technical occupations, and healthcare service occupations, including nurse practitioners, physicians, surgeons, dentists, physical therapists, home health aides, personal care aides, and other medical technicians and healthcare support workers. County-level data were available for 107 out of the 133 counties (80.5%) that had NCHS data on the distribution of COVID-19 deaths among Asian Americans, and 96 counties (72.2%) with Asian American healthcare workforce data. The ACS 2018 five-year estimates were also the source of county-level percentage of the Asian American population (alone or in combination) who are Filipinx.8 In addition, the ACS provided county-level population counts26 to calculate population density (people per 1,000 people per square mile), estimated by dividing the total population by the county area, then dividing by 1,000 people. The county area was calculated in ArcGIS 10.7.1 using the county boundary shapefile and projected to Albers equal area conic (for counties in the US contiguous states), Hawai’i Albers Equal Area Conic (for Hawai’i counties), and Alaska Albers Equal Area Conic (for Alaska counties).20

Updated weekly on Thursdays Older adults and people with disabilities who live in long term care facilities are at high risk for COVID-19 illness and death. The data below describes the impacts of COVID-19 on the residents and staff of Long Term Care Facilities licensed by the State Department of Social and Health Services (DSHS), including Skilled Nursing Facilities (nursing homes); Adult Family Homes and Assisted Living Facilities. Cases and deaths are also occurring in other forms of senior housing not licensed by DSHS, including subsidized housing for people age 50+, Permanent Supportive Housing, and naturally occurring retirement communities (NORCs) and among people with disabilities living in Supportive Living Facilities (also licensed by DSHS).

In 2020 the world was presently burdened with the COVID-19 pandemic. World Health Organization confirms 34,874,744 cases with 1,097,497 deaths (case fatality rate (CFR) 3.1%) were reported in 216 countries. In Indonesia, the number of people who have been infected and the number who have died are approximately 287,008 and 10,740 (CFR 3.7%), respectively, with the most predominant regions being Jakarta (73,700), East Java (43,536) and Central Java (22,440). Many factors can increase the transmission of COVID-19. One of them is wind speed. This data set contains covid-19 data in DKI Jakarta from June 2020 until August 2022 and wind speed in daily power point form. This data can be analyzed to see the correlation between wind speed and the COVID-19 cases. Methods The records of COVID-19 were obtained from the special website of coronavirus for the Daerah Khusus Ibukota (DKI) Jakarta at the Provincial Health Office (https://corona.jakarta.go.id/en/data-pemantauan). The COVID-19 data (n = 4,740) covered six administrative city areas and 261 sub-districts in DKI Jakarta as research locations, namely Kepulauan Seribu, West Jakarta, Central Jakarta, South Jakarta, East Jakarta, and Nort Jakarta. The wind speed data was taken from the Meteorology, Climatology and Geophysics Agency's data website. The wind speed data collected for the period June 2020 to August 2022 (n = 790) was obtained from the POWER LaRC Data Access Viewer, Jakarta. The wind speed data in .csv format is downloaded by specifying the type of daily data unit, data period (time extent), and parameter (in this case wind/pressure). The type of data extraction is POWER Single Point, where the location of the centroid or midpoint of DKI Jakarta Province is determined at latitude -6.1805 and longitude 106.8284. The data of wind speed is in the form of .csv in the form of time series-daily data; it was extracted into a tabular form with two variables, namely wind speed data of 10m and wind speed of 50m (n = 790). The total data (n = 4,740) were grouped into 6 regions with n = 790/region. At the processing steps, the collected data was grouped into variable wind speeds of 10m, wind speeds of 50m, and variables of COVID-19 cases in six areas in DKI Jakarta Province. To find out the distribution of Wind Speed, the daily data before being processed was grouped into per month.

Number of deaths and age-specific mortality rates for selected grouped causes, by age group and sex, 2000 to most recent year.

This file contains the following characteristics per case tested positive in the Netherlands: Date for statistics, Age group, Sex, Hospital admission, Death, Week of death, Province, Notifying GGD

The file is structured as follows: A record for every laboratory confirmed COVID-19 patient in the Netherlands, since the first COVID-19 report in the Netherlands on 27/02/2020 (Date for statistics may be earlier). The file is refreshed daily at 4 pm, based on the data as registered at 10 am that day in the national system for notifiable infectious diseases (Osiris AIZ).

Description of the variables

Date_file: Date and time when the data was published by RIVM

Date_statistics: Date for statistics; first day of illness, if unknown, date lab positive, if unknown, report date to GGD (format: yyyy-mm-dd)

Date_statistics_type: Type of date that was available for date for the variable "Date for statistics", where: DOO = Date of disease onset : First day of illness as reported by GGD. Please note: it is not always known whether this first day of illness was really Covid-19. DPL = Date of first Positive Labresult : Date of the (first) positive lab result. DON = Date of Notification : Date on which the notification was received by the GGD.

Agegroup: Age group at life; 0-9, 10-19, ..., 90+; at death <50, 50-59, 60-69, 70-79, 80-89, 90+, Unknown = Unknown

Sex: Sex; Unknown = Unknown, Male = Male, Female = Female

Province: Name of the province (based on the patient's whereabouts)

Hospital_admission: Hospital admission reported by the GGD. Unknown = Unknown, Yes = Yes, No = No From May 1, 2020, the indication of hospitalization will be related to Covid-19. If not, the value of this column is "No". Until June 1, only seriously ill people were tested, a large part of these people had already been or were admitted shortly afterwards. As a result, the hospital admissions registered by the GGD were more complete during the first wave. As of June 1, everyone can be tested and more people will be tested at an early stage. As a result, the GGD is not always informed, or with a delay, of a hospital admission. That is why RIVM has been actively naming the registered hospital admissions of the NICE Foundation (https://data.rivm.nl/geonetwork/srv/dut/catalog.search#/metadata/4f4ad069-8f24-4fe8-b2a7-533ef27a899f) since 6 October. RIVM uses these figures as a guideline because they provide a more complete picture than the hospital admissions reported by the GGD. Click here (https://www.rivm.nl/nieuws/nummer-nieuw-melde-covid-19-verzekeraars-stable) for more information about this.

Deceased: Death. Unknown = Unknown, Yes = Yes, No = No

Week of Death: Week of death. YYYYMM according to ISO week notation (start from Monday to Sunday)

Municipal_health_service: GGD that made the report.

Florida COVID-19 Case Line data, exported from the Florida Department of Health 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 the caseline data is being archived as two separate files, one for 2020 and one for 2021. The 2021 file will only include data from 1/1/2021 onward. In addition, FDOH has added two Object ID fields to their dataset. These caseline data are being preserved as they are provided by the FDOH, with a daily archive captured by the USF Libraries DHHC.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. 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=7a0c74a551904761812dc6b8bd620ee1 or Direct Download at: https://open-fdoh.hub.arcgis.com/datasets/7a0c74a551904761812dc6b8bd620ee1_0.

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.

CVD-COVID-UK, co-ordinated by the British Heart Foundation (BHF) Data Science Centre (https://bhfdatasciencecentre.org/), is one of the NIHR-BHF Cardiovascular Partnership’s National Flagship Projects.

CVD-COVID-UK aims to understand the relationship between COVID-19 and cardiovascular diseases through analyses of de-identified, pseudonymised, linked, nationally collated health datasets across the four nations of the UK. The consortium has over 400 members across more than 50 institutions including data custodians, data scientists and clinicians, all of whom have signed up to an agreed set of principles with an inclusive, open and transparent ethos.

Approved researchers access data within secure trusted/secure research environments (TREs/SDEs) provided by NHS England (England), the National Safe Haven (Scotland), the Secure Anonymised Information Linkage (SAIL) Databank (Wales) and the Honest Broker Service (Northern Ireland). A dashboard of datasets available in each nation’s TRE can be found here: https://bhfdatasciencecentre.org/areas/cvd-covid-uk-covid-impact/

This dataset represents the linked datasets in SAIL Databank’s TRE for Wales and contains the following datasets: • Welsh Longitudinal GP Dataset - Welsh Primary Care (Daily COVID codes only) (GPCD) • Welsh Longitudinal General Practice Dataset (WLGP) - Welsh Primary Care • Critical Care Dataset (CCDS) • Emergency Department Dataset Daily (EDDD) • Emergency Department Dataset (EDDS) • Outpatient Database for Wales (OPDW) • Outpatient Referral (OPRD) • Patient Episode Dataset for Wales (PEDW) • COVID-19 Test Results (PATD) • COVID-19 Test Trace and Protect (CTTP) - Legacy • COVID-19 Shielded People List (CVSP) • SARS-CoV-2 viral sequencing data (COG-UK data)-Lineage/Variant Data-Wales (CVSD) • Covid Vaccination Dataset (CVVD) • Annual District Death Daily (ADDD) • Annual District Death Extract (ADDE) • COVID-19 Consolidated Deaths (CDDS) • Intensive Care National Audit and Research Centre (ICCD) - Legacy - COVID only • Intensive Care National Audit and Research Centre (ICNC) • Welsh Dispensing Dataset (WDDS) - Legacy • Annual District Birth Extract (ADBE) • Maternity Indicators Dataset (MIDS) • National Community Child Health Database (NCCHD) • Care Home Dataset (CARE) • Congenital Anomaly Register and Information Service (CARS) • Referral to Treatment Times (RTTD) • SAIL Dementia e-Cohort (SDEC) • Welsh Ambulance Services NHS Trust (WASD) • Welsh Demographic Service Dataset (WDSD) • Welsh Results Reports Service (WRRS) • ONS 2011 Census Wales (CENW)

This dataset corresponds to paper titled "A Mathematical Model for COVID-19 Considering Waning Immunity, Vaccination and Control Measures". In this work we define a modified SEIR model that accounts for the spread of infection during the latent period, infections from asymptomatic or pauci-symptomatic infected individuals, potential loss of acquired immunity, people’s increasing awareness of social distancing and the use of vaccination as well as non-pharmaceutical interventions like social confinement. We estimate model parameters in three different scenarios - in Italy, where there is a growing number of cases and re-emergence of the epidemic, in India, where there are significant number of cases post confinement period and in Victoria, Australia where a re-emergence has been controlled with severe social confinement program. Our result shows the benefit of long term confinement of 50% or above population and extensive testing. With respect to loss of acquired immunity, our model suggests higher impact for Italy. We also show that a reasonably effective vaccine with mass vaccination program can be successful in significantly controlling the size of infected population. We show that for India, a reduction in contact rate by 50% compared to a reduction of 10% in the current stage can reduce death from 0.0268% to 0.0141% of population. Similarly, for Italy we show that reducing contact rate by half can reduce a potential peak infection of 15% population to less than 1.5% of population, and potential deaths from 0.48% to 0.04%. With respect to vaccination, we show that even a 75% efficient vaccine administered to 50% population can reduce the peak number of infected population by nearly 50% in Italy. Similarly, for India, a 0.056% of population would die without vaccination, while 93.75% efficient vaccine given to 30\% population would bring this down to 0.036% of population, and 93.75% efficient vaccine given to 70% population would bring this down to 0.034%.

Associated with manuscript titled: Fifty Muslim-majority countries have fewer COVID-19 cases and deaths than the 50 richest non-Muslim countriesThe objective of this research was to determine the difference in the total number of COVID-19 cases and deaths between Muslim-majority and non-Muslim countries, and investigate reasons for the disparities. Methods: The 50 Muslim-majority countries had more than 50.0% Muslims with an average of 87.5%. The non-Muslim country sample consisted of 50 countries with the highest GDP while omitting any Muslim-majority countries listed. The non-Muslim countries’ average percentage of Muslims was 4.7%. Data pulled on September 18, 2020 included the percentage of Muslim population per country by World Population Review15 and GDP per country, population count, and total number of COVID-19 cases and deaths by Worldometers.16 The data set was transferred via an Excel spreadsheet on September 23, 2020 and analyzed. To measure COVID-19’s incidence in the countries, three different Average Treatment Methods (ATE) were used to validate the results. Results published as a preprint at https://doi.org/10.31235/osf.io/84zq5(15) Muslim Majority Countries 2020 [Internet]. Walnut (CA): World Population Review. 2020- [Cited 2020 Sept 28]. Available from: http://worldpopulationreview.com/country-rankings/muslim-majority-countries (16) Worldometers.info. Worldometer. Dover (DE): Worldometer; 2020 [cited 2020 Sept 28]. Available from: http://worldometers.info

This dataset corresponds to paper titled "COVID-19: Risks of Re-emergence, Re-infection, and Control Measures -- A Long Term Modeling Study". In this work we define a modified SEIR model that accounts for the spread of infection during the latent period, infections from asymptomatic or pauci-symptomatic infected individuals, potential loss of acquired immunity, people’s increasing awareness of social distancing and the use of vaccination as well as non-pharmaceutical interventions like social confinement. We estimate model parameters in three different scenarios - in Italy, where there is a growing number of cases and re-emergence of the epidemic, in India, where there are significant number of cases post confinement period and in Victoria, Australia where a re-emergence has been controlled with severe social confinement program. Our result shows the benefit of long term confinement of 50% or above population and extensive testing. With respect to loss of acquired immunity, our model suggests higher impact for Italy. We also show that a reasonably effective vaccine with mass vaccination program can be successful in significantly controlling the size of infected population. We show that for India, a reduction in contact rate by 50% compared to a reduction of 10% in the current stage can reduce death from 0.0268% to 0.0141% of population. Similarly, for Italy we show that reducing contact rate by half can reduce a potential peak infection of 15% population to less than 1.5% of population, and potential deaths from 0.48% to 0.04%. With respect to vaccination, we show that even a 75% efficient vaccine administered to 50% population can reduce the peak number of infected population by nearly 50% in Italy. Similarly, for India, a 0.056% of population would die without vaccination, while 93.75% efficient vaccine given to 30\% population would bring this down to 0.036% of population, and 93.75% efficient vaccine given to 70% population would bring this down to 0.034%.

IntroductionAfter mild COVID-19 that does not require hospitalization, some individuals develop persistent symptoms that may worsen over time, producing a multisystemic condition termed Post-COVID condition (PCC). Among other disorders, PCC is characterized by persistent changes in the immune system that may not be solved several months after COVID-19 diagnosis.MethodsPeople with PCC were recruited to determine the distribution and functionality of CD4+ T helper (Th) subsets in comparison with individuals with mild, severe, and critical presentations of acute COVID-19 to evaluate their contribution as risk or protective factors for PCC.ResultsPeople with PCC showed low levels of Th1 cells, similar to individuals with severe and critical COVID-19, although these cells presented a higher capacity to express IFNγ in response to stimulation. Th2/Th1 correlation was negative in individuals with acute forms of COVID-19, but there was no significant Th2/Th1 correlation in people with PCC. Th2 cells from people with PCC presented high capacity to express IL-4 and IL-13, which are related to low ventilation and death associated with COVID-19. Levels of proinflammatory Th9 and Th17 subsets were significantly higher in people with PCC in comparison with acute COVID-19, being Th1/Th9 correlation negative in these individuals, which probably contributed to a more pro-inflammatory than antiviral scenario. Th17 cells from approximately 50% of individuals with PCC had no capacity to express IL-17A and IL-22, similar to individuals with critical COVID-19, which would prevent clearing extracellular pathogens. Th2/Th17 correlation was positive in people with PCC, which in the absence of negative Th1/Th2 correlation could also contribute to the proinflammatory state. Finally, Th22 cells from most individuals with PCC had no capacity to express IL-13 or IL-22, which could increase tendency to reinfections due to impaired epithelial regeneration.DiscussionPeople with PCC showed skewed polarization of CD4+ Th subsets with altered functionality that was more similar to individuals with severe and critical presentations of acute COVID-19 than to people who fully recovered from mild disease. New strategies aimed at reprogramming the immune response and redirecting CD4+ Th cell polarization may be necessary to reduce the proinflammatory environment characteristic of PCC.