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

Toronto Public Health (TPH) shared anonymized, person-level information for all COVID-19 cases reported from the start of the COVID-19 pandemic in January 2020. As case and outbreak management guidelines changed and COVID-19 specific resources were no longer funded, the level of detail available for cases decreased, and more recent data are less complete and not comparable to previous years. TPH discontinued the production of this report with the final refresh as of February 14, 2024 As of February 2023, the fields "currently hospitalized", "currently in ICU" and "currently intubated" have been removed from the Open Data set. Due to current provincial guidelines on COVID-19 case management, discharge information is not always available. This makes it difficult to report accurately on these fields. The time period for the inaccuracy is not known therefore data in these fields from previous downloads of the open data set should be interpreted with caution. As of July 1, 2023, data entry practices will change to align with updated provincial guidance. TPH will no longer be entering: cases received only by fax and non-severe (not hospitalized or fatal) probable cases associated with outbreaks. This will likely result in an undercount when compared to previous COVID-19 case reporting. As of November 27, 2023, deaths and hospitalization due to COVID are not being entered into CCM due to operational limitations.

Toronto Public Health (TPH) shared anonymized, person-level information for all COVID-19 cases reported from the start of the COVID-19 pandemic in January 2020. As case and outbreak management guidelines changed and COVID-19 specific resources were no longer funded, the level of detail available for cases decreased, and more recent data are less complete and not comparable to previous years. TPH discontinued the production of this report with the final refresh as of February 14, 2024 As of February 2023, the fields "currently hospitalized", "currently in ICU" and "currently intubated" have been removed from the Open Data set. Due to current provincial guidelines on COVID-19 case management, discharge information is not always available. This makes it difficult to report accurately on these fields. The time period for the inaccuracy is not known therefore data in these fields from previous downloads of the open data set should be interpreted with caution. As of July 1, 2023, data entry practices will change to align with updated provincial guidance. TPH will no longer be entering: cases received only by fax and non-severe (not hospitalized or fatal) probable cases associated with outbreaks. This will likely result in an undercount when compared to previous COVID-19 case reporting. As of November 27, 2023, deaths and hospitalization due to COVID are not being entered into CCM due to operational limitations.

The file comprises COVID-19 case counts, population, demographic and air pollution data by Toronto neighbourhood. The data were employed in an ecological study of the association between air pollution and incidence of COVID-19. Data were obtained from the Toronto Open Data portal, McGill University, the University of Toronto, the Canadian Urban Environmental Health Research Consortium (CANUE) and Statistics Canada. The study found that there was a positive association between COVID-19 incidence and long-term exposure to reactive oxygen species in fine particulate matter (PM2.5). The association was larger in magnitude in neighbourhoods with a higher proportion of Black residents. The results require further examination using studies based on individual-level rather than area-level data. Supporting documentation: https://doi.org/10.1164/rccm.202011-4142OC

As of April 15, 2023, there had been a total of around 51,921 deaths attributed to COVID-19 in Canada. As of this time, every province and territory has reported deaths, with Quebec and Ontario reporting the highest numbers.

COVID-19 in Canada Canada has recorded almost 4.65 million coronavirus cases since the first infection in the country was confirmed on January 25, 2020. The number of cases by province shows that Ontario and Quebec have been the most severely affected. The number of daily new cases reached record highs at the end of 2021 and began to decrease as spring arrived in 2022.

COVID-19 vaccinations in Canada Seven COVID-19 vaccines have now been approved for use in Canada and vaccines are widely available. As of January 1, 2023 around 83 percent of the Canadian population had received at least one dose of a COVID-19 vaccine. The provinces with the highest share of people fully vaccinated against COVID-19 are Newfoundland and Labrador and Nova Scotia. However, Ontario and Quebec are the provinces with the highest total number of people vaccinated.

This dataset reports the daily reported number of deaths involving COVID-19 by fatality type.

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

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

Data includes:

• Date on which the death occurred
• Total number of deaths involving COVID-19
• Number of deaths with “COVID-19 as the underlying cause of death”
• Number of deaths with “COVID-19 contributed but not underlying cause”
• Number of deaths where the “Cause of death unknown” or “Cause of death missing”

Additional Notes

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.

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.

As of December 1, 2022, data are based on the date on which the death occurred. This reporting method differs from the prior method which is based on net change in COVID-19 deaths reported day over day.

Data are based on net change in COVID-19 deaths for which COVID-19 caused the death reported day over day. Deaths are not reported by the date on which death happened as reporting may include deaths that happened on previous dates.

Spikes, negative numbers and other data anomalies: Due to ongoing data entry and data quality assurance activities in Case and Contact Management system (CCM) file, Public Health Units continually clean up COVID-19, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes, negative numbers and current totals being different from previously reported case and death counts.

Public Health Units report cause of death in the CCM based on information available to them at the time of reporting and in accordance with definitions provided by Public Health Ontario. The medical certificate of death is the official record and the cause of death could be different.

Deaths are defined per the outcome field in CCM marked as “Fatal”. Deaths in COVID-19 cases identified as unrelated to COVID-19 are not included in the number of deaths involving COVID-19 reported.

"_Cause of death unknown_" is the category of death for COVID-19 positive individuals with cause of death still under investigation, or for which the public health unit was unable to determine cause of death. The category may change later when the cause of death is confirmed either as “COVID-19 as the underlying cause of death”, “COVID-19 contributed but not underlying cause,” or “COVID-19 unrelated”.

"_Cause of death missing_" is the category of death for COVID-19 positive individuals with the cause of death missing in CCM.

Rates for the most recent days are subject to reporting lags

All data reflects totals from 8 p.m. the previous day.

This dataset is subject to change.

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.

Data includes:

• date
• OH region
• current hospitalizations with COVID-19
• current patients in Intensive Care Units (ICUs) due to COVID-related critical Illness
• current patients in Intensive Care Units (ICUs) testing positive for COVID
• current patients in Intensive Care Units (ICUs) no longer testing positive for COVID
• 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
• current patients in Intensive Care Units (ICUs) on ventilators no longer testing positive for COVID

**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 **

Additional notes

Data for the period of October 24, 2023 to March 24, 2024 excludes hospitals in the West region who were experiencing data availability issues.

Daily adult, pediatric, and neonatal patient ICU census data were impacted by technical issues between September 9 and October 20, 2023. As a result, when public reporting resumes on November 16, 2023, historical ICU data for this time period will be excluded.

As of August 3, 2023, the data in this file has been updated to reflect that there are now six Ontario Health (OH) regions.

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

ObjectiveThis work aims to study the disproportionate impact of the COVID-19 pandemic on the Jane and Finch community, one of the socially vulnerable neighborhoods in the Greater Toronto Area (GTA), Ontario, Canada, in terms of morbidity, mortality, and healthcare services.MethodologyA dataset provided by the Black Creek Community Health Centre (BCCHC), gathered from different health-related portals, covering various health statistics during COVID-19, namely, COVID-19 number of cases, hospitalizations, deaths, percentage of vaccination with one-, two-, and three-dose(s), Primary and Preventive Care (PPC) visits which include fecal and pap-smear cancer tests, and percentage of completed Imaging, Procedures, and Surgeries (IPS) which include the number of patients waiting for surgery were studied using statistical analysis. Underserved communities in the Peel, York, and City of Toronto regions were recognized using the Ontario Marginalized Index (ON-Marg). The Jane and Finch community was selected from the fifth quintile of the ON-Marg index and compared with the remaining locations (first to fourth ON-Marg quantiles) using Kruskal-Wallis, Mann–Whitney u, and t-tests. The Gini index was used to understand the inequality of the health parameters among the selected neighborhoods. Local Indicator of Spatial Association (LISA) was used to detect the neighborhoods with significantly higher numbers of COVID-19 cases, hospitalizations, and mortalities.ResultsThe Jane and Finch community had a significantly (p

This is the Zenodo archive for the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada" (Mucaki EJ, Shirley BC and Rogan PK. F1000Research 2021, 10:1312, DOI: 10.12688/f1000research.75891.1). This study aimed to produce community-level geo-spatial mapping of patterns and clusters of symptoms, and of confirmed COVID-19 cases, in near real-time in order to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. This archive will contain data and image files from this study, which were too numerous to be included in the manuscript for this study. It also provides all program files pertaining to the Geostatistical Epidemiology Toolbox (Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript and other software developed (cluster, outlier, streak identification and pairing)..

We also provide a guide which provides a general description of the contents of the four sections in this archive (Documentation_for_Sections_of_Zenodo_Archive.docx). If you have any intent to utilize the data provided in Section 3, we greatly advise you to review this document as it describes the output of all geostatistical analyses performed in this study in detail.

Data Files:

Section 1. "Section_1.Tables_S1_S7.Figures_S1_S11.zip"

This section contains all additional tables and figures described in the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada". Additional tables S1 to S7 are presented in an Excel document. These 7 tables provide summary statistics of various geostatistical tests described in the study (“Section 1 – Tables S1-S4”) and lists all identified single and paired high-case cluster streaks (“Section 1 – Tables S5-S7”). This section also contains 11 additional figures referred to in the manuscript (“Section 1 – Figures S1-S11”) both individually and within a Word document which describes them.

Section 2. "Section_2.Localized_Hotspot_Lists.zip"

All localized hotspots (identified through kriging analysis) were catalogued for each municipality evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex). These files indicate the FSA in which the hotspot was identified, the date in which it was identified (utilizing 3-day case data at the postal code level), the amount of cases which occurred within the FSA within these 3 dates, the range of cases interpolated by kriging analysis (between 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-50, >50), and whether or not the FSA was deemed a hotspot by Gi* relative to the rest of Ontario on any of the three dates evaluated. Please see Section 4 for map images of these localized hotspots.

Section 3. "Section_3.All-Data_Files.Kriging_GiStar_Local_and_GlobalMorans.2020_2021"

Section 3 – All output files from the geostatistical tests performed in this study are provided in this section. This includes the output from Ontario-wide FSA-level Gi* and Cluster and Outlier analyses, and PC-level Cluster and Outlier, Spatial Autocorrelation, and kriging analysis of 6 municipal regions. It also includes kriging analysis of 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan). This section also provides data files from our analyses of stratified case data (by age, gender, and at-risk condition). All coordinates presented in these data files are given in “PCS_Lambert_Conformal_Conic” format. Case values between 1-5 were masked (appear as “NA”).

Section 4. "Section_4.All_Map_Images_of_Geostat_Analyses.zip"

Sets of image files which map the results of our geostatistical analyses onto a map of Ontario or within the municipalities evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex) are provided. This includes: Kriging analysis (PC-level), Local Moran's I cluster and outlier analysis (FSA and PC-level), normal and space-time Gi* analysis, and all images for all analyses performed on stratified data (by age, gender and at-risk condition). Kriging contour maps are also included for 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan).

Software:

This Zenodo archive also provides all program files pertaining to the Geostatistical Epidemiology Toolbox (Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript. This geostatistical toolbox was developed by CytoGnomix Inc., London ON, Canada and is distributed freely under the terms of the GNU General Public License v3.0. It can be easily modified to accommodate other Canadian provinces and, with some additional effort, other countries.

This distribution of the Geostatistical Epidemiology Toolbox does not include postal code (PC) boundary files (which are required for some of the tools included in the toolbox). The PC boundary shapefiles used to test the toolbox were obtained from DMTI (https://www.dmtispatial.com/canmap/) through the Scholar's Geoportal at the University of Western Ontario (http://geo2.scholarsportal.info/). The distribution of these files (through sharing, sale, donation, transfer, or exchange) is strictly prohibited. However, any equivalent PC boundary shape file should suffice, provided it contains polygon boundaries representing postal code regions (see guide for more details).

Software File 1. "Software.GeostatisticalEpidemiologyToolbox.zip"

The Geostatistical Epidemiology Toolbox is a set of custom Python-based geoprocessing tools which function as any built-in tool in the ArcGIS system. This toolbox implements data preprocessing, geostatistical analysis and post-processing software developed to evaluate the distribution and progression of COVID-19 cases in Canada. The purpose of developing this toolbox is to allow external users without programming knowledge to utilize the software scripts which generated our analyses and was intended to be used to evaluate Canadian datasets. While the toolbox was developed for evaluating the distribution of COVID-19, it could be utilized for other purposes.

The toolbox was developed to evaluate statistically significant distributions of COVID-19 case data at Canadian Forward Sortation Area (FSA) and Postal Code-level in the province of Ontario utilizing geostatistical tools available through the ArcGIS system. These tools include: 1) Standard Gi* analysis (finds areas where cases are significantly spatially clustered), 2) spacetime based Gi* analysis (finds areas where cases are both spatially and temporally clustered), 3) cluster and outlier analysis (determines if high case regions are an regional outlier or part of a case cluster), 4) spatial autocorrelation (determines the cases in a region are clustered overall) and, 5) Empirical Bayesian Kriging analysis (creates contour maps which define the interpolation of COVID-19 cases in measured and unmeasured areas). Post-processing tools are included that import these all of the preceding results into the ArcGIS system and automatically generate PNG images.

This archive also includes a guide ("UserManual_GeostatisticalEpidemiologyToolbox_CytoGnomix.pdf") which describes in detail how to set up the toolbox, how to format input case data, and how to use each tool (describing both the relevant input parameters and the structure of the resultant output files).

Software File 2: “Software.Additional_Programs_for_Cluster_Outlier_Streak_Idendification_and_Pairing.zip"

In the manuscript associated with this archive, Perl scripts were utilized to evaluate postal code-level Cluster and Outlier analysis to identify significantly, highly clustered postal codes over consecutive periods (i.e., high-case cluster “streaks”). The identified streaks are then paired to those in close proximity, based on the neighbors of each postal code from PC centroid data ("paired streaks"). Multinomial logistic regression models were then derived in the R programming language to measure the correlation between the number of cases reported in each paired streak, the interval of time separating each streak, and the physical distance between the two postal codes. Here, we provide the 3 Perl scripts and the R markdown file which perform these tasks:

“Ontario_City_Closest_Postal_Code_Identification.pl”

Using an input file with postal code coordinates (by centroid), this program identifies the nearest neighbors to all postal codes for a given municipal region (the name of this region is entered on the command line). Postal code centroids were calculated in ArcGIS using the “Calculate Geometry” function against DMTI postal code boundary files (not provided). Input from other sources could be used, however, as long as the input includes a list of coordinates with a unique label associated with a particular municipality.

The output of this program (for the same municipal region being evaluated) is required for the following two Perl

A three-way cross-tabulation comparing the number of COVID-19 positive indications (at a patient-level) from the lab text, health condition diagnosis text and clinical note data streams.

Descriptive characteristics of lab texts, health condition diagnosis texts and clinical notes included in the study sample, measured on a record/encounter-level.

The file comprises COVID-19 case counts, population, demographic and air pollution data by Toronto neighbourhood. The data were employed in an ecological study of the association between air pollution and incidence of COVID-19. Data were obtained from the Toronto Open Data portal, McGill University, the University of Toronto, the Canadian Urban Environmental Health Research Consortium (CANUE) and Statistics Canada. The study found that there was a positive association between COVID-19 incidence and long-term exposure to reactive oxygen species in fine particulate matter (PM2.5). The association was larger in magnitude in neighbourhoods with a higher proportion of Black residents. The results require further examination using studies based on individual-level rather than area-level data. Supporting documentation: https://doi.org/10.1164/rccm.202011-4142OC

BlueDot, a Toronto-based company that monitors the spread of infectious diseases using artificial intelligence, alerted its customers to a cluster of rare cases of pneumonia in Wuhan, China. Nine days later, in Wuhan, China, about December 30, 2019, the World Health Organization announced the discovery of a novel coronavirus, later called COVID-19. Today, COVID-19 is a pandemic that has spread to 180 nations, claimed over 83,000 lives and caused a near-global lock.....

This dataset compiles daily counts of patients (both COVID-related and non-COVID-related) in adult and pediatric ICU beds and the number of adult and pediatric ICU beds that are unoccupied.

**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 **

Data includes:

• date
• number of adults in ICU for COVID-related critical illness (CRCI)_**_
• number of adults in ICU for non-CRCI reasons
• number of adult ICU beds that are unoccupied
• total number of adults in ICU for any reason
• number of patients in pediatric ICU for COVID-related critical illness (CRCI)_**_
• number of patients in pediatric ICU beds for non-CRCI reasons
• number of pediatric ICU beds that are unoccupied
• total number of patients in pediatric ICU beds for any reason

**These results may not match the CRCI cases in ICU reported elsewhere (on Ontario.ca) as they are restricted to either adults only or pediatric patients only and do not include cases in other ICU bed types.

• ICU data includes patients in levels 2 and 3 adult or pediatric ICU beds. The reported numbers reflect the previous day’s values. Patients are counted at a single point in time (11:59 pm) to ensure that each person is only counted once, and their COVID status is updated at 6 am, prior to posting. This may vary slightly from similar sources who update at different times.
• COVID-related critical illness (CRCI) includes patients currently testing positive for COVID and patients in ICU due to COVID who are no longer testing positive for COVID.
• Since the start of the pandemic, the province has invested in “incremental” ICU beds to accommodate potential surges in ICU demand due to COVID. These beds were added at various points in time (i.e., October 2020, February 2021, April 2021) to ensure system preparedness and meet operational needs. Aligned with the decline of Wave 3 and COVID-related pressures and at the direction of Ontario Health, a number of these beds were brought offline in July 2021. These events account for the sudden increases and/or decreases in ICU beds seen in the data. The number of ICU beds continues to fluctuate slightly as beds are brought on and offline to meet localized demands/need.

Modifications to this data

Data for the period of October 24, 2023 to March 24, 2024 excludes hospitals in the West region who were experiencing data availability issues.

Daily adult, pediatric, and neonatal patient ICU census data were impacted by technical issues between September 9 and October 20, 2023. As a result, when public reporting resumes on November 16, 2023, historical ICU data for this time period will be excluded.

January 18, 2022: Information on pediatric ICU beds was added to the file for the period of May 2020 to present.

January 7, 2022: Due to some methodology changes, historical data were impacted during the following timeframes:

• May 1, 2020 to October 22, 2020.
• February 19, 2021 to July 26, 2021.

How the data was impacted

To ensure system preparedness throughout the pandemic, hospitals were asked to identify the number of beds (i.e., non-ICU beds) and related resources that could be made available within 24 hours for use as an ICU bed in case of a surge in COVID patients. These beds were considered expanded ICU capacity and were not used to calculate hospitals’ ICU occupancy. These beds were previously included in this data.

The current numbers include only funded ICU beds based on data from the Critical Care Information System (CCIS).