The coronavirus (COVID-19) pandemic forced many museums worldwide to remain closed for long periods in 2020 and 2021. As a result of the closures, some of these institutions boosted their digital activities for the public. According to a May 2021 study, about 53 percent of the surveyed museums worldwide increased their social media activities after the lockdown. Meanwhile, 26.5 percent of institutions involved in the survey claimed to have started offering digital live events after the lockdown.

When asked about the COVID-19 restrictions in May 2021, nearly half of Finns said that the current measures should be maintained as they are. However, about one third of respondents were of the opinion that restrictions should be further eased. The current plan for lifting the coronavirus restrictions is based on the epidemiological situation, aiming for a controlled reopening of Finnish society.

In light of the coronavirus (COVID-19) pandemic, adults in the United States were surveyed in March 2021 on whether they believed the U.S.-Canadian border should be re-opened. The majority of respondents, approximately 45 percent, believed that the border should not be re-opened. Meanwhile, approximately 34 percent of respondents believed that the border should be re-opened. The remaining respondents expressed uncertainty.

The file contains Tweet IDs* for COVID-19 related tweets collected in June, 2021 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/).Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overviewNote: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).

This report analyzes the impact of COVID-19 across industry sectors. It provides side-by-side analysis of alternative datasets to present you with unique quantitative analysis of the effects of COVID-19 and how these differ across sectors. We also provide qualitative analysis of each sector and analyse COVID-19’s impact on leading companies. Read More

As of May 2021, Black, non-Hispanic adults aged 18-39 were least likely to have been vaccinated or to definitely plan to get vaccinated against COVID-19 (40 percent) compared to to other ethnic groups. Nearly a third of Black adults stated they probably or definitely will not get vaccinated. This statistic presents the percentage of adults 18-39 years who were, would, or would not get vaccinated against COVID-19 in the United States from March-May 2021, by ethnicity.

The file contains Tweet IDs* for COVID-19 related tweets containing at least one vaccine-related word (i.e., words that starts with vaccin*, vacin*, or vax*) collected in May, 2021 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/). Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.

For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview

• Note: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Communalytic (https://communalytic.com/), Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).

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.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

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

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

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Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

Note: In these datasets, a person is defined as up to date if they have received at least one dose of an updated COVID-19 vaccine. The Centers for Disease Control and Prevention (CDC) recommends that certain groups, including adults ages 65 years and older, receive additional doses.

Starting on July 13, 2022, the denominator for calculating vaccine coverage has been changed from age 5+ to all ages to reflect new vaccine eligibility criteria. Previously the denominator was changed from age 16+ to age 12+ on May 18, 2021, then changed from age 12+ to age 5+ on November 10, 2021, to reflect previous changes in vaccine eligibility criteria. The previous datasets based on age 12+ and age 5+ denominators have been uploaded as archived tables.

Starting June 30, 2021, the dataset has been reconfigured so that all updates are appended to one dataset to make it easier for API and other interfaces. In addition, historical data has been extended back to January 5, 2021.

This dataset shows full, partial, and at least 1 dose coverage rates by zip code tabulation area (ZCTA) for the state of California. Data sources include the California Immunization Registry and the American Community Survey’s 2015-2019 5-Year data.

This is the data table for the LHJ Vaccine Equity Performance dashboard. However, this data table also includes ZTCAs that do not have a VEM score.

This dataset also includes Vaccine Equity Metric score quartiles (when applicable), which combine the Public Health Alliance of Southern California’s Healthy Places Index (HPI) measure with CDPH-derived scores to estimate factors that impact health, like income, education, and access to health care. ZTCAs range from less healthy community conditions in Quartile 1 to more healthy community conditions in Quartile 4.

The Vaccine Equity Metric is for weekly vaccination allocation and reporting purposes only. CDPH-derived quartiles should not be considered as indicative of the HPI score for these zip codes. CDPH-derived quartiles were assigned to zip codes excluded from the HPI score produced by the Public Health Alliance of Southern California due to concerns with statistical reliability and validity in populations smaller than 1,500 or where more than 50% of the population resides in a group setting.

These data do not include doses administered by the following federal agencies who received vaccine allocated directly from CDC: Indian Health Service, Veterans Health Administration, Department of Defense, and the Federal Bureau of Prisons.

For some ZTCAs, vaccination coverage may exceed 100%. This may be a result of many people from outside the county coming to that ZTCA to get their vaccine and providers reporting the county of administration as the county of residence, and/or the DOF estimates of the population in that ZTCA are too low. Please note that population numbers provided by DOF are projections and so may not be accurate, especially given unprecedented shifts in population as a result of the pandemic.

After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations.

The following dataset provides facility-level data for hospital utilization aggregated on a weekly basis (Sunday to Saturday). These are derived from reports with facility-level granularity across two main sources: (1) HHS TeleTracking, and (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities.

The hospital population includes all hospitals registered with Centers for Medicare & Medicaid Services (CMS) as of June 1, 2020. It includes non-CMS hospitals that have reported since July 15, 2020. It does not include psychiatric, rehabilitation, Indian Health Service (IHS) facilities, U.S. Department of Veterans Affairs (VA) facilities, Defense Health Agency (DHA) facilities, and religious non-medical facilities.

For a given entry, the term “collection_week” signifies the start of the period that is aggregated. For example, a “collection_week” of 2020-11-15 means the average/sum/coverage of the elements captured from that given facility starting and including Sunday, November 15, 2020, and ending and including reports for Saturday, November 21, 2020.

Reported elements include an append of either “_coverage”, “_sum”, or “_avg”.

• A “_coverage” append denotes how many times the facility reported that element during that collection week.
• A “_sum” append denotes the sum of the reports provided for that facility for that element during that collection week.
• A “_avg” append is the average of the reports provided for that facility for that element during that collection week.

The file will be updated weekly. No statistical analysis is applied to impute non-response. For averages, calculations are based on the number of values collected for a given hospital in that collection week. Suppression is applied to the file for sums and averages less than four (4). In these cases, the field will be replaced with “-999,999”.

A story page was created to display both corrected and raw datasets and can be accessed at this link: https://healthdata.gov/stories/s/nhgk-5gpv

This data is preliminary and subject to change as more data become available. Data is available starting on July 31, 2020.

Sometimes, reports for a given facility will be provided to both HHS TeleTracking and HHS Protect. When this occurs, to ensure that there are not duplicate reports, deduplication is applied according to prioritization rules within HHS Protect.

For influenza fields listed in the file, the current HHS guidance marks these fields as optional. As a result, coverage of these elements are varied.

For recent updates to the dataset, scroll to the bottom of the dataset description.

On May 3, 2021, the following fields have been added to this data set.

• hhs_ids
• previous_day_admission_adult_covid_confirmed_7_day_coverage
• previous_day_admission_pediatric_covid_confirmed_7_day_coverage
• previous_day_admission_adult_covid_suspected_7_day_coverage
• previous_day_admission_pediatric_covid_suspected_7_day_coverage
• previous_week_personnel_covid_vaccinated_doses_administered_7_day_sum
• total_personnel_covid_vaccinated_doses_none_7_day_sum
• total_personnel_covid_vaccinated_doses_one_7_day_sum
• total_personnel_covid_vaccinated_doses_all_7_day_sum
• previous_week_patients_covid_vaccinated_doses_one_7_day_sum
• previous_week_patients_covid_vaccinated_doses_all_7_day_sum

On May 8, 2021, this data set has been converted to a corrected data set. The corrections applied to this data set are to smooth out data anomalies caused by keyed in data errors. To help determine which records have had corrections made to it. An additional Boolean field called is_corrected has been added.

On May 13, 2021 Changed vaccination fields from sum to max or min fields. This reflects the maximum or minimum number reported for that metric in a given week.

On June 7, 2021 Changed vaccination fields from max or min fields to Wednesday reported only. This reflects that the number reported for that metric is only reported on Wednesdays in a given week.

On September 20, 2021, the following has been updated: The use of analytic dataset as a source.

On January 19, 2022, the following fields have been added to this dataset:

• inpatient_beds_used_covid_7_day_avg
• inpatient_beds_used_covid_7_day_sum
• inpatient_beds_used_covid_7_day_coverage

On April 28, 2022, the following pediatric fields have been added to this dataset:

• all_pediatric_inpatient_bed_occupied_7_day_avg
• all_pediatric_inpatient_bed_occupied_7_day_coverage
• all_pediatric_inpatient_bed_occupied_7_day_sum
• all_pediatric_inpatient_beds_7_day_avg
• all_pediatric_inpatient_beds_7_day_coverage
• all_pediatric_inpatient_beds_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum
• staffed_icu_pediatric_patients_confirmed_covid_7_day_avg
• staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage
• staffed_icu_pediatric_patients_confirmed_covid_7_day_sum
• staffed_pediatric_icu_bed_occupancy_7_day_avg
• staffed_pediatric_icu_bed_occupancy_7_day_coverage
• staffed_pediatric_icu_bed_occupancy_7_day_sum
• total_staffed_pediatric_icu_beds_7_day_avg
• total_staffed_pediatric_icu_beds_7_day_coverage
• total_staffed_pediatric_icu_beds_7_day_sum

On October 24, 2022, the data includes more analytical calculations in efforts to provide a cleaner dataset. For a raw version of this dataset, please follow this link: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/uqq2-txqb

Due to changes in reporting requirements, after June 19, 2023, a collection week is defined as starting on a Sunday and ending on the next Saturday.

BackgroundSince the pandemic onset, deprivation has been seen as a significant determinant of COVID-19 incidence and mortality. This study explores outcomes of COVID-19 in the context of material deprivation across three pandemic waves in Ireland.MethodsBetween 1st March 2020 and 13th May 2021, 252,637 PCR-confirmed COVID-19 cases were notified in Ireland. Cases were notified to the national Computerised Infectious Disease Reporting (CIDR) system. Each case was geo-referenced and assigned a deprivation category according to the Haase-Pratschke (HP) Deprivation Index. Regression modelling examined three outcomes: admission to hospital; admission to an intensive care unit (ICU) and death.ResultsDeprivation increased the likelihood of contracting COVID-19 in all age groups and across all pandemic waves, except for the 20–39 age group. Deprivation, age, comorbidity and male gender carried increased risk of hospital admission. Deprivation was not a factor in predicting ICU admission or death, and diagnosis in wave 2 was associated with the lowest risk of all three outcomes.ConclusionsOur study suggests that COVID-19 spreads easily through all strata of society and particularly in the more deprived population; however this was not a consistent finding. Ireland is ethnically more homogenous than other countries reporting a larger deprivation gradient, and in such societies, structural racial differences may contribute more to poor COVID outcomes than elements of deprivation.

Abstract

After implementing Phase 1 of the High-Frequency Phone Survey (HFPS) project in Latin America and The Caribbean in 2020, the World Bank conducted Phase 2 in 2021 to continue to assess the socio-economic impacts of the COVID-19 pandemic on households. This new phase, conducted in partnership with the UNDP LAC Chief Economist office, included two waves. Wave 1 covering 24 countries. Wave 2 collected between October and December 2022, covering 22 countries. Of these countries, 13 participated in Phase 1: Argentina, Bolivia, Colombia, Costa Rica, Chile, Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Paraguay and Peru. Countries that joined in Phase 2 are: Antigua & Barbuda and Brazil (only in Wave 1), Belize, Dominica, Guyana, Haiti, Jamaica, Nicaragua, Panama, St. Lucia and Uruguay.

This study presents information from 23 countries for which data was collected between May and July 2021. Brazil was integrated into the LAC HFPS Phase 2 project at a later point and was implemented with a slightly different approach. See the project information here: https://microdata.worldbank.org/index.php/catalog/4533. For information on the LAC HFPS Phase 1, see here: https://pubdocs.worldbank.org/en/238561622829862035/HFPS-TECHNICAL-NOTE-MAY2021-FINAL.pdf

Geographic coverage

National level

Analysis unit

Households and individuals of 18 years of age and older.

Sampling procedure

The size of the Phase 2 Wave 2 overall (cell phones and landlines) selected sample of phone numbers (i.e., before any fieldwork activities) in each of the Original Countries (i.e. the 13 countries included in LAC HFPS Phase 1) is equal to the Phase 1 Wave 1 overall selected sample of phone numbers, plus the Phase 2 Wave 1 overall supplement fresh sample, plus the Phase 2 Wave 2 overall supplement fresh sample of phone numbers.

The samples of the Added Countries (i.e. those only included in Phase 2) is based on a dual frame of cell phone and landline numbers generated through a Random Digit Dialing (RDD) process. In the first phase, a large sample was selected in both frames, and then screened through an automated process to identify the active, eligible numbers. A smaller second-phase sample was selected from the active residential numbers from in the first-phase sample and was delivered to the country teams. Please see Sampling Design and Weighting document for more detail.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

Questionnaires are available for download in language of data collection for each country (i.e. Spanish, English, French).

State and territorial executive orders, administrative orders, resolutions, and proclamations are collected from government websites and cataloged and coded using Microsoft Excel by one coder with one or more additional coders conducting quality assurance. Data were collected to determine when restaurants in states and territories were subject to closing and reopening requirements through executive orders, administrative orders, resolutions, and proclamations for COVID-19. Data can be used to determine when restaurants in states and territories were subject to closing and reopening requirements through executive orders, administrative orders, resolutions, and proclamations for COVID-19. Data consists exclusively of state and territorial orders, many of which apply to specific counties within their respective state or territory; therefore, data is broken down to the county level. These data are derived from publicly available state and territorial executive orders, administrative orders, resolutions, and proclamations (“orders”) for COVID-19 that expressly close or reopen restaurants found by the CDC, COVID-19 Community Intervention & Critical Populations Task Force, Monitoring & Evaluation Team, Mitigation Policy Analysis Unit, and the CDC, Center for State, Tribal, Local, and Territorial Support, Public Health Law Program from March 11, 2020 through May 31, 2021. These data will be updated as new orders are collected. Any orders not available through publicly accessible websites are not included in these data. Only official copies of the documents or, where official copies were unavailable, official press releases from government websites describing requirements were coded; news media reports on restrictions were excluded. Recommendations not included in an order are not included in these data. Effective and expiration dates were coded using only the date provided; no distinction was made based on the specific time of the day the order became effective or expired. These data do not necessarily represent an official position of the Centers for Disease Control and Prevention.

Please see FAQ for latest information on COVID-19 Data Hub Data Flows: https://covid-19.geohive.ie/pages/helpfaqs. Notice: Please note that data for the 30th of May 2023 is missing from this dataset.If you are downloading this data set as a CSV please follow these steps to sort the dataset by date.1. Click the 'Download' button.2. In the download pane that opens on the left, click the 'Download' button under CSV. This should be the first option.3. Open the file.4. Highlight column D by click 'D'.5. In the ribbon, in the Editing group click 'Sort & Filter'.6. From the drop down menu that appears select the first option to sort from oldest to newest.7. In the pop-up window that appears make sure that 'Expand the selection' is selected.8. Click 'Sort', the dataset will now be sorted by date. See the section What impact has the cyber-attack of May 2021 on the HSE IT systems had on reporting of COVID-19 data on the Data Hub? in the FAQ for information about issues in data from May 2021.** Between 14th May 2021 and 29th July 2021 only the fields 'Number of confirmed COVID-19 cases Admitted on site' (SUM_number_of_confirmed_covid_19_ca) and 'Number of new COVID-19 cases confirmed in the past 24 hrs' (SUM_number_of_new_covid_19_cases_co) in this service were updated.The fields 'Number of New Admissions COVID-19 Positive previous 24hrs' (SUM_no_new_admissions_covid19_p) and 'Number of Discharges COVID-19 Positive previous 24hrs' (SUM_no_discharges_covid19_posit) have no data during this period of time. **Detailed dataset containing a range of COVID-19 related indicators for Acute Hospitals in Ireland. Data is provided for Confirmed COVID-19 cases and the number of new admissions and discharges. Data is based on an aggregate of 29 Acute Hospitals. Data has been provided by the HSE Performance Management Improvement Unit (PMIU).This service is used in Ireland's COVID-19 Data Hub, produced as a collaboration between Tailte Éireann, the Central Statistics Office (CSO), the Department of Housing, Planning and Local Government, the Department of Health, the Health Protection Surveillance Centre (HPSC), and the All-Island Research Observatory (AIRO). This service and Ireland's COVID-19 Data Hub are built using the GeoHive platform, Ireland's Geospatial Data Hub.

COVID-19 cases by province in Vietnam 27.05.2021-25.07.2021