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: This dataset has been retired and marked as historical-only.

This dataset is a companion to the COVID-19 Daily Cases and Deaths dataset (https://data.cityofchicago.org/d/naz8-j4nc). The major difference in this dataset is that the case, death, and hospitalization corresponding rates per 100,000 population are not those for the single date indicated. They are rolling averages for the seven-day period ending on that date. This rolling average is used to account for fluctuations that may occur in the data, such as fewer cases being reported on weekends, and small numbers. The intent is to give a more representative view of the ongoing COVID-19 experience, less affected by what is essentially noise in the data.

All rates are per 100,000 population in the indicated group, or Chicago, as a whole, for “Total” columns.

Only Chicago residents are included based on the home address as provided by the medical provider.

Cases with a positive molecular (PCR) or antigen test are included in this dataset. Cases are counted based on the date the test specimen was collected. Deaths among cases are aggregated by day of death. Hospitalizations are reported by date of first hospital admission. Demographic data are based on what is reported by medical providers or collected by CDPH during follow-up investigation.

Denominators are from the U.S. Census Bureau American Community Survey 1-year estimate for 2018 and can be seen in the Citywide, 2018 row of the Chicago Population Counts dataset (https://data.cityofchicago.org/d/85cm-7uqa).

All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects cases and deaths currently known to CDPH.

Numbers in this dataset may differ from other public sources due to definitions of COVID-19-related cases and deaths, sources used, how cases and deaths are associated to a specific date, and similar factors.

Data Source: Illinois National Electronic Disease Surveillance System, Cook County Medical Examiner’s Office, U.S. Census Bureau American Community Survey

Global Covid-19 Data

Global Covid-19 data on cases, deaths, vaccinations, and more

By Valtteri Kurkela [source]

About this dataset

The dataset is constantly updated and synced hourly to ensure up-to-date information. With over several columns available for analysis and exploration purposes, users can extract valuable insights from this extensive dataset.

Some of the key metrics covered in the dataset include:

1. Vaccinations: The dataset covers total vaccinations administered worldwide as well as breakdowns of people vaccinated per hundred people and fully vaccinated individuals per hundred people.

2. Testing & Positivity: Information on total tests conducted along with new tests conducted per thousand people is provided. Additionally, details on positive rate (percentage of positive Covid-19 tests out of all conducted) are included.

3. Hospital & ICU: Data on ICU patients and hospital patients are available along with corresponding figures normalized per million people. Weekly admissions to intensive care units and hospitals are also provided.

4. Confirmed Cases: The number of confirmed Covid-19 cases globally is captured in both absolute numbers as well as normalized values representing cases per million people.

5.Confirmed Deaths: Total confirmed deaths due to Covid-19 worldwide are provided with figures adjusted for population size (total deaths per million).

6.Reproduction Rate: The estimated reproduction rate (R) indicates the contagiousness of the virus within a particular country or region.

7.Policy Responses: Besides healthcare-related metrics, this comprehensive dataset includes policy responses implemented by countries or regions such as lockdown measures or travel restrictions.

8.Other Variables of InterestThe data encompasses various socioeconomic factors that may influence Covid-19 outcomes including population density,membership in a continent,gross domestic product(GDP)per capita;

For demographic factors: -Age Structure : percentage populations aged 65 and older,aged (70)older,median age -Gender-specific factors: Percentage of female smokers -Lifestyle-related factors: Diabetes prevalence rate and extreme poverty rate

1. Excess Mortality: The dataset further provides insights into excess mortality rates, indicating the percentage increase in deaths above the expected number based on historical data.

The dataset consists of numerous columns providing specific information for analysis, such as ISO code for countries/regions, location names,and units of measurement for different parameters.

Overall,this dataset serves as a valuable resource for researchers, analysts, and policymakers seeking to explore various aspects related to Covid-19

How to use the dataset

Introduction:

• Understanding the Basic Structure:

  • The dataset consists of various columns containing different data related to vaccinations, testing, hospitalization, cases, deaths, policy responses, and other key variables.
  • Each row represents data for a specific country or region at a certain point in time.

• Selecting Desired Columns:

  • Identify the specific columns that are relevant to your analysis or research needs.
  • Some important columns include population, total cases, total deaths, new cases per million people, and vaccination-related metrics.

• Filtering Data:

  • Use filters based on specific conditions such as date ranges or continents to focus on relevant subsets of data.
  • This can help you analyze trends over time or compare data between different regions.

• Analyzing Vaccination Metrics:

  • Explore variables like total_vaccinations, people_vaccinated, and people_fully_vaccinated to assess vaccination coverage in different countries.
  • Calculate metrics such as people_vaccinated_per_hundred or total_boosters_per_hundred for standardized comparisons across populations.

• Investigating Testing Information:

  • Examine columns such as total_tests, new_tests, and tests_per_case to understand testing efforts in various countries.
  • Calculate rates like tests_per_case to assess testing efficiency or identify changes in testing strategies over time.

• Exploring Hospitalization and ICU Data:

  • Analyze variables like hosp_patients, icu_patients, and hospital_beds_per_thousand to understand healthcare systems' strain.
  • Calculate rates like icu_patients_per_million or hosp_patients_per_million for cross-country comparisons.

• Assessing Covid-19 Cases and Deaths:

  • Analyze variables like total_cases, new_ca...

Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.This archived public use dataset has 11 data elements reflecting United States COVID-19 community levels for all available counties.The COVID-19 community levels were developed using a combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days. The COVID-19 community level was determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge.Using these data, the COVID-19 community level was classified as low, medium, or high.COVID-19 Community Levels were used to help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals.For the most accurate and up-to-date data for any county or state, visit the relevant health department website. COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.Archived Data Notes:This dataset was renamed from "United States COVID-19 Community Levels by County as Originally Posted" to "United States COVID-19 Community Levels by County" on March 31, 2022.March 31, 2022: Column name for county population was changed to “county_population”. No change was made to the data points previous released.March 31, 2022: New column, “health_service_area_population”, was added to the dataset to denote the total population in the designated Health Service Area based on 2019 Census estimate.March 31, 2022: FIPS codes for territories American Samoa, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands were re-formatted to 5-digit numeric for records released on 3/3/2022 to be consistent with other records in the dataset.March 31, 2022: Changes were made to the text fields in variables “county”, “state”, and “health_service_area” so the formats are consistent across releases.March 31, 2022: The “%” sign was removed from the text field in column “covid_inpatient_bed_utilization”. No change was made to the data. As indicated in the column description, values in this column represent the percentage of staffed inpatient beds occupied by COVID-19 patients (7-day average).March 31, 2022: Data values for columns, “county_population”, “health_service_area_number”, and “health_service_area” were backfilled for records released on 2/24/2022. These columns were added since the week of 3/3/2022, thus the values were previously missing for records released the week prior.April 7, 2022: Updates made to data released on 3/24/2022 for Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands to correct a data mapping error.April 21, 2022: COVID-19 Community Level (CCL) data released for counties in Nebraska for the week of April 21, 2022 have 3 counties identified in the high category and 37 in the medium category. CDC has been working with state officials to verify the data submitted, as other data systems are not providing alerts for substantial increases in disease transmission or severity in the state.May 26, 2022: COVID-19 Community Level (CCL) data released for McCracken County, KY for the week of May 5, 2022 have been updated to correct a data processing error. McCracken County, KY should have appeared in the low community level category during the week of May 5, 2022. This correction is reflect

Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

Weekly COVID-19 Community Levels (CCLs) have been replaced with levels of COVID-19 hospital admission rates (low, medium, or high) which demonstrate >99% concordance by county during February 2022–March 2023. For more information on the latest COVID-19 status levels in your area and hospital admission rates, visit United States COVID-19 Hospitalizations, Deaths, and Emergency Visits by Geographic Area.

This archived public use dataset contains historical case and percent positivity data updated weekly for all available counties and jurisdictions. Each week, the dataset was refreshed to capture any historical updates. Please note, percent positivity data may be incomplete for the most recent time period.

This archived public use dataset contains weekly community transmission levels data for all available counties and jurisdictions since October 20, 2022. The dataset was appended to contain the most recent week's data as originally posted on COVID Data Tracker. Historical corrections are not made to these data if new case or testing information become available. A separate archived file is made available here (: Weekly COVID-19 County Level of Community Transmission Historical Changes) if historically updated data are desired.

Related data CDC provides the public with two active versions of COVID-19 county-level community transmission level data: this dataset with the levels as originally posted (Weekly Originally Posted dataset), updated weekly with the most recent week’s data since October 20, 2022, and a historical dataset with the county-level transmission data from January 22, 2020 (Weekly Historical Changes dataset).

Methods for calculating county level of community transmission indicator The County Level of Community Transmission indicator uses two metrics: (1) total new COVID-19 cases per 100,000 persons in the last 7 days and (2) percentage of positive SARS-CoV-2 diagnostic nucleic acid amplification tests (NAAT) in the last 7 days. For each of these metrics, CDC classifies transmission values as low, moderate, substantial, or high (below and here). If the values for each of these two metrics differ (e.g., one indicates moderate and the other low), then the higher of the two should be used for decision-making.

CDC core metrics of and thresholds for community transmission levels of SARS-CoV-2 Total New Case Rate Metric: "New cases per 100,000 persons in the past 7 days" is calculated by adding the number of new cases in the county (or other administrative level) in the last 7 days divided by the population in the county (or other administrative level) and multiplying by 100,000. "New cases per 100,000 persons in the past 7 days" is considered to have a transmission level of Low (0-9.99); Moderate (10.00-49.99); Substantial (50.00-99.99); and High (greater than or equal to 100.00).

Test Percent Positivity Metric: "Percentage of positive NAAT in the past 7 days" is calculated by dividing the number of positive tests in the county (or other administrative level) during the last 7 days by the total number of tests conducted

Note:This dataset represents archived records covering the period from March 2020 through October 2025. The most recent dataset, containing data from October 2025 to the present, is available at: https://data.maryland.gov/Health-and-Human-Services/COVID-Master-Tracker/37gh-4yqf/about_data Summary The cases, tests, positivity rates, hospitalizations, and confirmed and probable deaths for COVID-19 in Maryland. Description The MD COVID-19 - MASTER Case Tracker is a collection of Total Cases, Total Tests, Postivity Rates, Persons Tested Negative, Total Daily Hospital Beds, Total Ever Hospitalized, Total Persons Documented to Have Completed Home Isolation, Cases by County, Cases by Age Distribution, Cases by Gender Distribution, Cases by Race and Ethnicity Distribution, Confirmed Deaths Statewide, Confirmed Deaths by Date of Death, Confirmed Deaths by County, Confirmed Deaths by Age Distribution, Confirmed Deaths by Gender Distribution, Confirmed Deaths by Race And Ethnicity Distribution, Probable Deaths Statewide, Probable Deaths by Date of Death, Probable Deaths by County, Probable Deaths by Age Distribution, Probable Deaths by Gender Distribution, and Probable Deaths by Race And Ethnicity Distribution. 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.

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

This dataset represents preliminary estimates of cumulative U.S. COVID-19 disease burden for the 2024-2025 period, including illnesses, outpatient visits, hospitalizations, and deaths. The weekly COVID-19-associated burden estimates are preliminary and based on continuously collected surveillance data from patients hospitalized with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. The data come from the Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET), a surveillance platform that captures data from hospitals that serve about 10% of the U.S. population. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of COVID-19 -associated burden that have occurred since October 1, 2024.

Note: Data are preliminary and subject to change as more data become available. Rates for recent COVID-19-associated hospital admissions are subject to reporting delays; as new data are received each week, previous rates are updated accordingly.

References

1. Reed C, Chaves SS, Daily Kirley P, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. https://doi.org/10.1371/journal.pone.0118369 
2. Rolfes, MA, Foppa, IM, Garg, S, et al. Annual estimates of the burden of seasonal influenza in the United States: A tool for strengthening influenza surveillance and preparedness. Influenza Other Respi Viruses. 2018; 12: 132– 137. https://doi.org/10.1111/irv.12486
3. Tokars JI, Rolfes MA, Foppa IM, Reed C. An evaluation and update of methods for estimating the number of influenza cases averted by vaccination in the United States. Vaccine. 2018;36(48):7331-7337. doi:10.1016/j.vaccine.2018.10.026 
4. Collier SA, Deng L, Adam EA, Benedict KM, Beshearse EM, Blackstock AJ, Bruce BB, Derado G, Edens C, Fullerton KE, Gargano JW, Geissler AL, Hall AJ, Havelaar AH, Hill VR, Hoekstra RM, Reddy SC, Scallan E, Stokes EK, Yoder JS, Beach MJ. Estimate of Burden and Direct Healthcare Cost of Infectious Waterborne Disease in the United States. Emerg Infect Dis. 2021 Jan;27(1):140-149. doi: 10.3201/eid2701.190676. PMID: 33350905; PMCID: PMC7774540.
5. Reed C, Kim IK, Singleton JA,  et al. Estimated influenza illnesses and hospitalizations averted by vaccination–United States, 2013-14 influenza season. MMWR Morb Mortal Wkly Rep. 2014 Dec 12;63(49):1151-4. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6349a2.htm 
6. Reed C, Angulo FJ, Swerdlow DL, et al. Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April–July 2009. Emerg Infect Dis. 2009;15(12):2004-2007. https://dx.doi.org/10.3201/eid1512.091413  
7. Devine O, Pham H, Gunnels B, et al. Extrapolating Sentinel Surveillance Information to Estimate National COVID-19 Hospital Admission Rates: A Bayesian Modeling Approach. Influenza and Other Respiratory Viruses. https://onlinelibrary.wiley.com/doi/10.1111/irv.70026. Volume18, Issue10. October 2024.
8. https://www.cdc.gov/covid/php/covid-net/index.html">COVID-NET | COVID-19 | CDC 
9. https://www.cdc.gov/covid/hcp/clinical-care/systematic-review-process.html 
10. https://academic.oup.com/pnasnexus/article/1/3/pgac079/6604394?login=false">Excess natural-cause deaths in California by cause and setting: March 2020 through February 2021 | PNAS Nexus | Oxford Academic (oup.com)
11. Kruschke, J. K. 2011. Doing Bayesian data analysis: a tutorial with R and BUGS. Elsevier, Amsterdam, Section 3.3.5.

Status of COVID-19 cases in Ontario

This dataset compiles daily snapshots of publicly reported data on 2019 Novel Coronavirus (COVID-19) testing in Ontario.

Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak.

Effective April 13, 2023, this dataset will be discontinued. The public can continue to access the data within this dataset in the following locations updated weekly on the Ontario Data Catalogue:

• Ontario COVID-19 testing percent positive by age group
• Confirmed positive cases of COVID-19 in Ontario
• Ontario COVID-19 testing metrics by Public Health Unit (PHU)
• Ontario COVID-19 testing percent positive by age group
• COVID-19 cases in hospital and ICU, by Ontario Health (OH) region
• Cumulative deaths (new methodology)
• Deaths Involving COVID-19 by Fatality Type

For information on Long-Term Care Home COVID-19 Data, please visit: Long-Term Care Home COVID-19 Data.

Data includes:

• reporting date
• daily tests completed
• total tests completed
• test outcomes
• total case outcomes (resolutions and deaths)
• current tests under investigation
• current hospitalizations
  • current patients in Intensive Care Units (ICUs) due to COVID-related critical Illness
  • current patients in Intensive Care Units (ICUs) testing positive for COVID-19
  • current patients in Intensive Care Units (ICUs) no longer testing positive for COVID-19
  • current patients in Intensive Care Units (ICUs) on ventilators due to COVID-related critical illness
  • current patients in Intensive Care Units (ICUs) on ventilators testing positive for COVID-19
  • current patients in Intensive Care Units (ICUs) on ventilators no longer testing positive for COVID-19
• Long-Term Care (LTC) resident and worker COVID-19 case and death totals
• Variants of Concern case totals
• number of new deaths reported (occurred in the last month)
• number of historical deaths reported (occurred more than one month ago)
• change in number of cases from previous day by Public Health Unit (PHU).

This dataset is subject to change. Please review the daily epidemiologic summaries for information on variables, methodology, and technical considerations.

Cumulative Deaths

**Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool **

The methodology used to count COVID-19 deaths has changed to exclude deaths not caused by COVID. This impacts data captured in the columns “Deaths”, “Deaths_Data_Cleaning” and “newly_reported_deaths” starting with data for March 11, 2022. A new column has been added to the file “Deaths_New_Methodology” which represents the methodological change.

The method used to count COVID-19 deaths has changed, effective December 1, 2022. Prior to December 1, 2022, deaths were counted based on the date the death was updated in the public health unit’s system. Going forward, deaths are counted on the date they occurred.

On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023. A small number of COVID deaths (less than 20) do not have recorded death date and will be excluded from this file.

CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags.

Related dataset(s)

• Confirmed positive cases of COVID-19 in Ontario

NOTE: This dataset has been retired and marked as historical-only. Weekly rates of COVID-19 cases, hospitalizations, and deaths among people living in Chicago by vaccination status and age. Rates for fully vaccinated and unvaccinated begin the week ending April 3, 2021 when COVID-19 vaccines became widely available in Chicago. Rates for boosted begin the week ending October 23, 2021 after booster shots were recommended by the Centers for Disease Control and Prevention (CDC) for adults 65+ years old and adults in certain populations and high risk occupational and institutional settings who received Pfizer or Moderna for their primary series or anyone who received the Johnson & Johnson vaccine. Chicago residency is based on home address, as reported in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE) and Illinois National Electronic Disease Surveillance System (I-NEDSS). Outcomes: • Cases: People with a positive molecular (PCR) or antigen COVID-19 test result from an FDA-authorized COVID-19 test that was reported into I-NEDSS. A person can become re-infected with SARS-CoV-2 over time and so may be counted more than once in this dataset. Cases are counted by week the test specimen was collected. • Hospitalizations: COVID-19 cases who are hospitalized due to a documented COVID-19 related illness or who are admitted for any reason within 14 days of a positive SARS-CoV-2 test. Hospitalizations are counted by week of hospital admission. • Deaths: COVID-19 cases who died from COVID-19-related health complications as determined by vital records or a public health investigation. Deaths are counted by week of death. Vaccination status: • Fully vaccinated: Completion of primary series of a U.S. Food and Drug Administration (FDA)-authorized or approved COVID-19 vaccine at least 14 days prior to a positive test (with no other positive tests in the previous 45 days). • Boosted: Fully vaccinated with an additional or booster dose of any FDA-authorized or approved COVID-19 vaccine received at least 14 days prior to a positive test (with no other positive tests in the previous 45 days). • Unvaccinated: No evidence of having received a dose of an FDA-authorized or approved vaccine prior to a positive test. CLARIFYING NOTE: Those who started but did not complete all recommended doses of an FDA-authorized or approved vaccine prior to a positive test (i.e., partially vaccinated) are excluded from this dataset. Incidence rates for fully vaccinated but not boosted people (Vaccinated columns) are calculated as total fully vaccinated but not boosted with outcome divided by cumulative fully vaccinated but not boosted at the end of each week. Incidence rates for boosted (Boosted columns) are calculated as total boosted with outcome divided by cumulative boosted at the end of each week. Incidence rates for unvaccinated (Unvaccinated columns) are calculated as total unvaccinated with outcome divided by total population minus cumulative boosted, fully, and partially vaccinated at the end of each week. All rates are multiplied by 100,000. Incidence rate ratios (IRRs) are calculated by dividing the weekly incidence rates among unvaccinated people by those among fully vaccinated but not boosted and boosted people. Overall age-adjusted incidence rates and IRRs are standardized using the 2000 U.S. Census standard population. Population totals are from U.S. Census Bureau American Community Survey 1-year estimates for 2019. All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. This dataset reflects data known to CDPH at the time when the dataset is updated each week. Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined. For all datasets related to COVID-19, see https://data.cityofchic

Context

There are two datasets. 1. owid-covid-data.csv :- Contains covid data from 1st Jan 2020 to 7th Feb, 2023 2. owid-covid-latest.csv:- Contains covid data from 8th Feb, 2023.

Content

Dataset Attribute Details:

iso_code: ISO 3166-1 alpha-3 – three-letter country codes continent: Continent of the geographical location location: Geographical location date: Date of observation total_cases: Total confirmed cases of COVID-19 new_cases: New confirmed cases of COVID-19 new_cases_smoothed: New confirmed cases of COVID-19 (7-day smoothed) total_cases_per_million: Total confirmed cases of COVID-19 per 1,000,000 people new_cases_per_million: New confirmed cases of COVID-19 per 1,000,000 people new_cases_smoothed_per_million: New confirmed cases of COVID-19 (7-day smoothed) per 1,000,000 people total_deaths: Total deaths attributed to COVID-19 new_deaths: New deaths attributed to COVID-19 new_deaths_smoothed: New deaths attributed to COVID-19 (7-day smoothed) total_deaths_per_million: Total deaths attributed to COVID-19 per 1,000,000 people new_deaths_per_million: New deaths attributed to COVID-19 per 1,000,000 people new_deaths_smoothed_per_million: New deaths attributed to COVID-19 (7-day smoothed) per 1,000,000 people excess_mortality: Percentage difference between the reported number of weekly or monthly deaths in 2020–2021 and the projected number of deaths for the same period based on previous years. excess_mortality_cumulative: Percentage difference between the cumulative number of deaths since 1 January 2020 and the cumulative projected deaths for the same period based on previous years. excess_mortality_cumulative_absolute: Cumulative difference between the reported number of deaths since 1 January 2020 and the projected number of deaths for the same period based on previous years. excess_mortality_cumulative_per_million: Cumulative difference between the reported number of deaths since 1 January 2020 and the projected number of deaths for the same period based on previous years, per million people. icu_patients: Number of COVID-19 patients in intensive care units (ICUs) on a given day icu_patients_per_million: Number of COVID-19 patients in intensive care units (ICUs) on a given day per 1,000,000 people hosp_patients: Number of COVID-19 patients in the hospital on a given day hosp_patients_per_million: Number of COVID-19 patients in hospital on a given day per 1,000,000 people weekly_icu_admissions: Number of COVID-19 patients newly admitted to intensive care units (ICUs) in a given week weekly_icu_admissions_per_million: Number of COVID-19 patients newly admitted to intensive care units (ICUs) in a given week per 1,000,000 people weekly_hosp_admissions: Number of COVID-19 patients newly admitted to hospitals in a given week weekly_hosp_admissions_per_million: Number of COVID-19 patients newly admitted to hospitals in a given week per 1,000,000 people stringency_index: Government Response Stringency Index: composite measure based on 9 response indicators including school closures, workplace closures, and travel bans, rescaled to a value from 0 to 100 (100 = strictest response) reproduction_rate: Real-time estimate of the effective reproduction rate (R) of COVID-19. total_tests: Total tests for COVID-19 new_tests: New tests for COVID-19 (only calculated for consecutive days) total_tests_per_thousand: Total tests for COVID-19 per 1,000 people new_tests_per_thousand: New tests for COVID-19 per 1,000 people new_tests_smoothed: New tests for COVID-19 (7-day smoothed). For countries that don't report testing data on a daily basis, we assume that testing changed equally on a daily basis over any periods in which no data was reported. This produces a complete series of daily figures, which is then averaged over a rolling 7-day window new_tests_smoothed_per_thousand: New tests for COVID-19 (7-day smoothed) per 1,000 people positive_rate: The share of COVID-19 tests that are positive, given as a rolling 7-day average (this is the inverse of tests_per_case) tests_per_case: Tests conducted per new confirmed case of COVID-19, given as a rolling 7-day average (this is the inverse of positive_rate) tests_units: Units used by the location to report its testing data total_vaccinations: Total number of COVID-19 vaccination doses administered people_vaccinated: Total number of people who received at least one vaccine dose people_fully_vaccinated: Total number of people who received all doses prescribed by the vaccination protocol total_boosters: Total number of COVID-19 vaccination booster doses administered (doses administered beyond the number prescribed by the vaccination protocol) new_vaccinations: New COVID-19 vaccination doses a...

Dataset description

Daily official UK Covid data. The data is available per country (England, Scotland, Wales and Northern Ireland) and for different regions in England. The different regions are split into two different files as part of the data is directly gathered by the NHS (National Health Service). The files that contain the word 'nhsregion' in their name, include data related to hospitals only, such as number of admissions or number of people in respirators. The files containing the word 'region' in their name, include the rest of the data, such as number of cases, number of vaccinated people or number of tests performed per day. The next paragraphs describe the columns for the different file types.

Region files

Files related to regions (word 'region' included in the file name) have the following columns: - "date": date in YYYY-MM-DD format - "area type": type of area covered in the file (region or nation) - "area name": name of area covered in the file (region or nation name) - "daily cases": new cases on a given date - "cum cases": cumulative cases - "new deaths 28days": new deaths within 28 days of a positive test - "cum deaths 28days": cumulative deaths within 28 days of a positive test - "new deaths_60days": new deaths within 60 days of a positive test - "cum deaths 60days": cumulative deaths within 60 days of a positive test - "new_first_episode": new first episodes by date - "cum_first_episode": cumulative first episodes by date - "new_reinfections": new reinfections by specimen data - "cum_reinfections": cumualtive reinfections by specimen data - "new_virus_test": new virus tests by date - "cum_virus_test": cumulative virus tests by date - "new_pcr_test": new PCR tests by date - "cum_pcr_test": cumulative PCR tests by date - "new_lfd_test": new LFD tests by date - "cum_lfd_test": cumulative LFD tests by date - "test_roll_pos_pct": percentage of unique case positivity by date rolling sum - "test_roll_people": unique people tested by date rolling sum - "new first dose": new people vaccinated with a first dose - "cum first dose": cumulative people vaccinated with a first dose - "new second dose": new people vaccinated with a first dose - "cum second dose": cumulative people vaccinated with a first dose - "new third dose": new people vaccinated with a booster or third dose - "cum third dose": cumulative people vaccinated with a booster or third dose

Country files

Files related to countries (England, Northern Ireland, Scotland and Wales) have the above columns and also: - "new admissions": new admissions, - "cum admissions": cumulative admissions, - "hospital cases": patients in hospitals, - "ventilator beds": COVID occupied mechanical ventilator beds - "trans_rate_min": minimum transmission rate (R) - "trans_rate_max": maximum transmission rate (R) - "trans_growth_min": transmission rate growth min - "trans_growth_max": transmission rate growth max

NHS Region files

Files related to nhsregion (word 'nhsregion' included in the file name) have the following columns: - "new admissions": new admissions, - "cum admissions": cumulative admissions, - "hospital cases": patients in hospitals, - "ventilator beds": COVID occupied mechanical ventilator beds - "trans_rate_min": minimum transmission rate (R) - "trans_rate_max": maximum transmission rate (R) - "trans_growth_min": transmission rate growth min - "trans_growth_max": transmission rate growth max

It's worth noting that the dataset hasn't been cleaned and it needs cleaning. Also, different files have different null columns. This isn't an error in the dataset but the way different countries and regions report the data.

NOTE: This dataset pertains only to the 2020-2021 school year and is no longer being updated. For additional data on COVID-19, visit data.ct.gov/coronavirus.

This dataset includes the leading and secondary metrics identified by the Connecticut Department of Health (DPH) and the Department of Education (CSDE) to support local district decision-making on the level of in-person, hybrid (blended), and remote learning model for Pre K-12 education.

Data represent daily averages for two-week periods by date of specimen collection (cases and positivity), date of hospital admission, or date of ED visit. Hospitalization data come from the Connecticut Hospital Association and are based on hospital location, not county of patient residence. COVID-19-like illness includes fever and cough or shortness of breath or difficulty breathing or the presence of coronavirus diagnosis code and excludes patients with influenza-like illness. All data are preliminary.

These data are updated weekly and reflect the previous two full Sunday-Saturday (MMWR) weeks (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf).

These metrics were adapted from recommendations by the Harvard Global Institute and supplemented by existing DPH measures.

For national data on COVID-19, see COVID View, the national weekly surveillance summary of U.S. COVID-19 activity, at https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html

DPH note about change from 7-day to 14-day metrics: Prior to 10/15/2020, these metrics were calculated using a 7-day average rather than a 14-day average. The 7-day metrics are no longer being updated as of 10/15/2020 but the archived dataset can be accessed here: https://data.ct.gov/Health-and-Human-Services/CT-School-Learning-Model-Indicators-by-County/rpph-4ysy

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

Nursing homes with residents positive for COVID-19 from 4/22/2020 to 6/19/2020. Starting in July 2020, this dataset will no longer be updated and will be replaced by the CMS COVID-19 Nursing Home Dataset, available at the following link: https://data.ct.gov/Health-and-Human-Services/CMS-COVID-19-Nursing-Home-Dataset/w8wc-65i5. Methods: 1) Laboratory-confirmed case counts are based upon data reported via the FLIS web portal. Nursing homes were asked to provide cumulative totals of residents with laboratory confirmed covid. This includes residents currently in-house, in the hospital, or who are deceased. Residents were excluded if they tested positive prior to initial admission to the nursing home. 2) The cumulative number of deaths among nursing home residents is based upon data reported by the Office of the Chief Medical Examiner. For public health surveillance, COVID-19-associated deaths include persons who tested positive for COVID-19 around the time of death (laboratory-confirmed) and persons whose death certificate lists COVID-19 disease as a cause of death or a significant condition contributing to death (probable). Limitations: 1) As of the week of 5/10/20, Point Prevalence Survey testing is being offered to all asymptomatic nursing home residents to inform infection prevention efforts. Point prevalence surveys will be conducted over a period of several weeks. Some nursing homes had adequate testing resources available to conduct surveys prior to this date. Differences in survey timing will impact the number of positive results that a nursing home reports. 2) Cumulative totals of residents testing positive are being collected rather than individual resident data. Thus we cannot verify the counts, de-duplicate, and/or verify whether there is a record of a positive lab test. This may result in either under- or over-counting. 3) The number of COVID-19 positive residents and the number of confirmed deaths among residents are tabulated from different data sources. Due to the timing of availability of test results for deceased residents, it is not appropriate to calculate the percent of cases who died due to COVID-19 at any particular facility based upon this data. 4) The count of deaths reported for 4/14 are not included in this dataset, as they were not broken out by laboratory-confirmed or probable. They can be viewed in the DPH Report here: https://portal.ct.gov/-/media/Coronavirus/CTDPHCOVID19summary4162020.pdf?la=en

To characterize how the 2020 winter surge in COVID-19 volumes impacted case fatality in an adequately-resourced health system.

OMOP dataset: Hospital COVID patients: severity, acuity, therapies, outcomes Dataset number 2.0

Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 6 million cases & more than 1.5 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS) & death. There is a pressing need for tools to stratify patients, to identify those at greatest risk. Acuity scores are composite scores which help identify patients who are more unwell to support & prioritise clinical care. There are no validated acuity scores for COVID-19 & it is unclear whether standard tools are accurate enough to provide this support. This secondary care COVID OMOP dataset contains granular demographic, morbidity, serial acuity and outcome data to inform risk prediction tools in COVID-19.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 & 2.

EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. UHB has cared for >5000 COVID admissions to date. This is a subset of data in OMOP format.

Scope: All COVID swab confirmed hospitalised patients to UHB from January – August 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.

Available supplementary data: Health data preceding & following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data. Further OMOP data available as an additional service.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

Data is from the California Department of Public Health (CDPH) Respiratory Virus Weekly Report.

The report is updated each Friday.

Laboratory surveillance data: California laboratories report SARS-CoV-2 test results to CDPH through electronic laboratory reporting. Los Angeles County SARS-CoV-2 lab data has a 7-day reporting lag. Test positivity is calculated using SARS-CoV-2 lab tests that has a specimen collection date reported during a given week.

Laboratory surveillance for influenza, respiratory syncytial virus (RSV), and other respiratory viruses (parainfluenza types 1-4, human metapneumovirus, non-SARS-CoV-2 coronaviruses, adenovirus, enterovirus/rhinovirus) involves the use of data from clinical sentinel laboratories (hospital, academic or private) located throughout California. Specimens for testing are collected from patients in healthcare settings and do not reflect all testing for influenza, respiratory syncytial virus, and other respiratory viruses in California. These laboratories report the number of laboratory-confirmed influenza, respiratory syncytial virus, and other respiratory virus detections and isolations, and the total number of specimens tested by virus type on a weekly basis.

Test positivity for a given week is calculated by dividing the number of positive COVID-19, influenza, RSV, or other respiratory virus results by the total number of specimens tested for that virus. Weekly laboratory surveillance data are defined as Sunday through Saturday.

Hospitalization data: Data on COVID-19 and influenza hospital admissions are from Centers for Disease Control and Prevention’s (CDC) National Healthcare Safety Network (NHSN) Hospitalization dataset. The requirement to report COVID-19 and influenza-associated hospitalizations was effective November 1, 2024. CDPH pulls NHSN data from the CDC on the Wednesday prior to the publication of the report. Results may differ depending on which day data are pulled. Admission rates are calculated using population estimates from the P-3: Complete State and County Projections Dataset provided by the State of California Department of Finance (https://dof.ca.gov/forecasting/demographics/projections/). Reported weekly admission rates for the entire season use the population estimates for the year the season started. For more information on NHSN data including the protocol and data collection information, see the CDC NHSN webpage (https://www.cdc.gov/nhsn/index.html).

CDPH collaborates with Northern California Kaiser Permanente (NCKP) to monitor trends in RSV admissions. The percentage of RSV admissions is calculated by dividing the number of RSV-related admissions by the total number of admissions during the same period. Admissions for pregnancy, labor and delivery, birth, and outpatient procedures are not included in total number of admissions. These admissions serve as a proxy for RSV activity and do not necessarily represent laboratory confirmed hospitalizations for RSV infections; NCKP members are not representative of all Californians.

Weekly hospitalization data are defined as Sunday through Saturday.

Death certificate data: CDPH receives weekly year-to-date dynamic data on deaths occurring in California from the CDPH Center for Health Statistics and Informatics. These data are limited to deaths occurring among California residents and are analyzed to identify influenza, respiratory syncytial virus, and COVID-19-coded deaths. These deaths are not necessarily laboratory-confirmed and are an underestimate of all influenza, respiratory syncytial virus, and COVID-19-associated deaths in California. Weekly death data are defined as Sunday through Saturday.

Wastewater data: This dataset represents statewide weekly SARS-CoV-2 wastewater summary values. SARS-CoV-2 wastewater concentrations from all sites in California are combined into a single, statewide, unit-less summary value for each week, using a method for data transformation and aggregation developed by the CDC National Wastewater Surveillance System (NWSS). Please see the CDC NWSS data methods page for a description of how these summary values are calculated. Weekly wastewater data are defined as Sunday through Saturday.

Supplementary Table 1. Pharmaceutical disease cohorts' characteristics

Supplementary Table 2. Oncology disease cohorts' characteristics

Supplementary Table 3. Disease areas and ICD-10 clinical modification diagnosis codes.

Supplementary Table 4. Total pre-pandemic and pandemic outpatient visits and the percentage change in outpatient visits for each disease area.

Supplementary Table 5. Total pre-pandemic and pandemic inpatient visits and the percentage change in inpatient visits for each disease area.

Supplementary Table 6. Total pre-pandemic and pandemic ED visits and the percentage change in ED visits for each disease area.

Context

Complete COVID-19 dataset is a collection of the COVID-19 data maintained by Our World in Data. It is updated daily and includes data on confirmed cases, deaths, hospitalizations, testing, and vaccinations as well as other variables of potential interest.

Content

The variables represent all data related to confirmed cases, deaths, hospitalizations, and testing, as well as other variables of potential interest.
the columns are: iso_code, continent, location, date, total_cases, new_cases, new_cases_smoothed, total_deaths, new_deaths, new_deaths_smoothed, total_cases_per_million, new_cases_per_million, new_cases_smoothed_per_million, total_deaths_per_million, new_deaths_per_million, new_deaths_smoothed_per_million, reproduction_rate, icu_patients, icu_patients_per_million, hosp_patients, hosp_patients_per_million, weekly_icu_admissions, weekly_icu_admissions_per_million, weekly_hosp_admissions, weekly_hosp_admissions_per_million, total_tests, new_tests, total_tests_per_thousand, new_tests_per_thousand, new_tests_smoothed, new_tests_smoothed_per_thousand, positive_rate, tests_per_case, tests_units, total_vaccinations, people_vaccinated, people_fully_vaccinated, new_vaccinations, new_vaccinations_smoothed, total_vaccinations_per_hundred, people_vaccinated_per_hundred, people_fully_vaccinated_per_hundred, new_vaccinations_smoothed_per_million, stringency_index, population, population_density, median_age, aged_65_older, aged_70_older, gdp_per_capita, extreme_poverty, cardiovasc_death_rate, diabetes_prevalence, female_smokers, male_smokers, handwashing_facilities, hospital_beds_per_thousand, life_expectancy, human_development_index

Acknowledgements/ Data Source

https://systems.jhu.edu/research/public-health/ncov/ https://www.ecdc.europa.eu/en/publications-data/download-data-hospital-and-icu-admission-rates-and-current-occupancy-covid-19 https://coronavirus.data.gov.uk/details/healthcare https://covid19tracker.ca/ https://healthdata.gov/dataset/covid-19-reported-patient-impact-and-hospital-capacity-state-timeseries https://ourworldindata.org/coronavirus-testing#our-checklist-for-covid-19-testing-data

DPH note about change from 7-day to 14-day metrics:

As of 10/15/2020, this dataset is no longer being updated. Starting on 10/15/2020, the school learning model indicator metrics will be calculated using a 14-day average rather than a 7-day average. The new school learning model indicators dataset using 14-day averages can be accessed here: https://data.ct.gov/Health-and-Human-Services/CT-School-Learning-Model-Indicators-by-County-14-d/e4bh-ax24

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

This dataset includes the leading and secondary metrics identified by the Connecticut Department of Health (DPH) and the Department of Education (CSDE) to support local district decision-making on the level of in-person, hybrid (blended), and remote learning model for Pre K-12 education.

Data represent daily averages for each week by date of specimen collection (cases and positivity), date of hospital admission, or date of ED visit. Hospitalization data come from the Connecticut Hospital Association and are based on hospital location, not county of patient residence. COVID-19-like illness includes fever and cough or shortness of breath or difficulty breathing or the presence of coronavirus diagnosis code and excludes patients with influenza-like illness. All data are preliminary.

These data are updated weekly; the previous week period for each dataset is the previous Sunday-Saturday, known as an MMWR week (https://wwwn.cdc.gov/nndss/document/MMWRweekoverview.pdf). The date listed is the date the dataset was last updated and corresponds to a reporting period of the previous MMWR week. For instance, the data for 8/20/2020 corresponds to a reporting period of 8/9/2020-8/15/2020.

These metrics were adapted from recommendations by the Harvard Global Institute and supplemented by existing DPH measures.

For national data on COVID-19, see COVID View, the national weekly surveillance summary of U.S. COVID-19 activity, at https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html

Notes:

9/25/2020: Data for Mansfield and Middletown for the week of Sept 13-19 were unavailable at the time of reporting due to delays in lab reporting.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.