As of September 27, 2020, there were around 125 COVID-19 deaths per 1,000 residents in nursing homes in Massachusetts. This statistic illustrates the rate of COVID-19 deaths in nursing homes in the United States as of September 27, 2020, by state.

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

As of March 7, 2021, there had been a total number of 641,608 confirmed COVID-19 cases and 130,296 deaths among nursing home residents in the United States. The number of COVID-19 cases among nursing home staff in the United States reached 130,296 cases, as of March 7, 2021.

As of May 25, 2020, around 81 percent of Canada's COVID-19 deaths were among long-term care residents. This statistic shows the percentage of all COVID-19 deaths in select counrties worldwide that were among long-term care residents as of May 2020.

Estimating nursing home COVID-19 deaths by U.S. Health and Human Service (HHS) Regions, 6-July to 26-July 2020: Zero-inflated negative binomial models1.

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

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

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

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

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

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

This dataset includes a count and rate per 100,000 population for COVID-19 cases, a count of COVID-19 molecular diagnostic tests, and a percent positivity rate for tests among people living in community settings for the previous two-week period. Dates are based on date of specimen collection (cases and positivity).

A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

Percent positivity is calculated as the number of positive tests among community residents conducted during the 14 days divided by the total number of positive and negative tests among community residents during the same period. If someone was tested more than once during that 14 day period, then those multiple test results (regardless of whether they were positive or negative) are included in the calculation.

These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

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).

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/COVID-19-case-rate-per-100-000-population-and-perc/s22x-83rd

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).

Additional notes: As of 11/5/2020, CT DPH has added antigen testing for SARS-CoV-2 to reported test counts in this dataset. The tests included in this dataset include both molecular and antigen datasets. Molecular tests reported include polymerase chain reaction (PCR) and nucleic acid amplicfication (NAAT) tests.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Data suppression is applied when the rate is <5 cases per 100,000 or if there are <5 cases within the town. Information on why data suppression rules are applied can be found online here: https://www.cdc.gov/cancer/uscs/technical_notes/stat_methods/suppression.htm

Abstract The COVID-19 pandemic poses difficulties for long-term care institutions for the elderly, with increased mortality rates for the residents. This study aims to estimate the impact of COVID-19 on mortality of institutionalized elderly in Brazil. Estimates of the percentage of elderly deaths occurring in care homes were calculated for Brazil, States and Regions using estimates for the total number of deaths. The estimation was based upon information available for other countries. The weighted percentage was 44.7% and 107,538 COVID-19 deaths were estimated for the elderly in these institutions in Brazil in 2020. Higher numbers of deaths were expected in the Southeast Region (48,779 deaths), followed by the Northeast Region (28,451 deaths); São Paulo was the most affected State (24,500 deaths). The strong impact of COVID-19 on the elderly population living in long-term care facilities is clear. Estimates for the country exceeded 100,000 elderly people, potentially the most fragile and vulnerable, and are based upon a conservative number of total deaths, in view of other estimates and the alarming situation of death growth in Brazil from COVID-19.

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

Variety of data files supporting CDC vaccination dashboards, downloaded 2.4.25. Includes weekly vaccination data for children, adults;COVID vaccination coverage overall and for pregnant women, nursing home residents, adults;Laboratory-confirmed RSV, COVID-19, and flu hospitalizations (source: RESPNet);Deaths from COVID-19, influenza, and RSV overall, by state, by race and ethnicity;ED visits with COVID-19, influenza, RSV, by demographics;NIS-ACM data on COVID-19 for adults (source:RespVaxView);Cumulative COVID-19 vaccination by age, jurisdictionCDC wastewater surviellance data tablesFluView Phase 2 Data***For CDC Covid-19 Nursing Home Data:Microdata: YesLevel of Analysis: Nursing HomesVariables Present: YesFile Layout: .csvCodebook: Yes Methods: YesWeights (with appropriate documentation): NoPublications: NoAggregate Data: No***For CDC NHSN Report State HCP Influenza Vaccination:Microdata: NoLevel of Analysis: StateVariables Present: YesFile Layout: N/ACodebook: NoMethods: YesWeights (with appropriate documentation): NoPublications: NoAggregate Data: No***For CDC Adult Covid NIS-ACM RespVax Data: Microdata: YesLevel of Analysis: Local - county, cityVariables Present: YesFile Layout: .csvCodebook: YesMethods: YesWeights (with appropriate documentation): YesPublications: NoAggregate Data: No***For NSSP Emergency Department Visits - COVID-19, Flu, etc. Microdata: YesLevel of Analysis: AilmentsVariables Present: YesFile Layout: .csvCodebook: NoMethods: Yes (https://docs.google.com/spreadsheets/d/19Po9Ir57Q-81Q5DfE1yKnW9NDLHXqPXc2307QY1hq24/edit?gid=1803019...) Weights (with appropriate documentation): NoPublications: NoAggregate Data: Yes***For CDC Percentage of Emergency Department Visits with Diagnosed COVID-19 in US:Microdata: YesLevel of Analysis: Demographic GroupsVariables Present: YesFile Layout: .csvCodebook: NoMethods: Yes (https://archive.cdc.gov/www_cdc_gov/ncird/surveillance/respiratory-illnesses/index.html)Weights (with appropriate documentation): NoPublications: NoAggregate Data: Yes***CDC Provisional COVID-19, Flu, and Pneumonia Death Counts:Microdata: YesLevel of Analysis: State, Demographic GroupsVariables Present: YesFile Layout: .csvCodebook: Yes (https://www.cdc.gov/nchs/nvss/vsrr/covid19/index.htm)Methods: YesWeights (with appropriate documentation): NoPublications: NoAggregate Data: No***For CDC Rates of Laboratory Confirmed RSV, Covid Hospitalizations:Microdata: YesLevel of Analysis: Weekly Rates by StateVariables Present: YesFile Layout: .csvCodebook: YesMethods: YesWeights (with appropriate documentation): NoPublications: NoAggregate Data: No***For CDC Vaccination Rates Among Adults 18 Years and Older :Microdata: YesLevel of Analysis: Yearly State Rate by Demographic Variables Present: YesFile Layout: .csvCodebook: YesMethods: Yes https://www.cdc.gov/adultvaxview/publications-resources/vaccination-coverage-adults-2021.html Weights (with appropriate documentation): NoPublications: NoAggregate Data: No***For CDC Vaccination Rates Among Pregnant Women:Microdata: YesLevel of Analysis: Percent Vaccinated Per Year by Demographic Type and Vaccination StatusVariables Present: YesFile Layout: .csvCodebook: YesMethods: Yes https://www.cdc.gov/fluvaxview/coverage-by-season/pregnant-april-2024.htmlWeights (with appropriate documentation): NoPublications: NoAggregate Data: Yes***For CDC Weekly Cum. COVID-19 Vaccination Coverage by Season, Race and Ethnicity, Medicare FFS aged 65+:Microdata: YesLevel of Analysis: Demographic Groups Variables Present: YesFile Layout: .csvCodebook: Yes https://data.cdc.gov/Vaccinations/Weekly-Cumulative-COVID-19-Vaccination-Coverage-an/ksfb-ug5d/about...Methods: Yes (above link)Weights (with appropriate documentation): NoPublications: NoAggregate Data: Yes***CDC Weekly Cum. Est No COVID-19 Vax Admin in Pharmacy...:Microdata: YesLevel of Analysis: National (delineated by Age Group)Variables Present: Yes - separate document https://data.cdc.gov/Vaccinations/Weekly-Cumulative-Estimated-Number-of-COVID-19-Vac/ewpg-rz7g/about...File Layout: .csvCodebook: Yes (see above link)Methods: Yes (see above link)Weights (with appropriate documentation): NoPublications: NoAggregate Data: No***CDC Weekly Cum. Doses (in millions) of Influenza Vaccinations...:Microdata: YesLevel of Analysis: National Variables Present: Yes Fi

A. SUMMARY This dataset includes COVID-19 tests by resident neighborhood and specimen collection date (the day the test was collected). Specifically, this dataset includes tests of San Francisco residents who listed a San Francisco home address at the time of testing. These resident addresses were then geo-located and mapped to neighborhoods. The resident address associated with each test is hand-entered and susceptible to errors, therefore neighborhood data should be interpreted as an approximation, not a precise nor comprehensive total.

In recent months, about 5% of tests are missing addresses and therefore cannot be included in any neighborhood totals. In earlier months, more tests were missing address data. Because of this high percentage of tests missing resident address data, this neighborhood testing data for March, April, and May should be interpreted with caution (see below)

Percentage of tests missing address information, by month in 2020 Mar - 33.6% Apr - 25.9% May - 11.1% Jun - 7.2% Jul - 5.8% Aug - 5.4% Sep - 5.1% Oct (Oct 1-12) - 5.1%

To protect the privacy of residents, the City does not disclose the number of tests in neighborhoods with resident populations of fewer than 1,000 people. These neighborhoods are omitted from the data (they include Golden Gate Park, John McLaren Park, and Lands End).

Tests for residents that listed a Skilled Nursing Facility as their home address are not included in this neighborhood-level testing data. Skilled Nursing Facilities have required and repeated testing of residents, which would change neighborhood trends and not reflect the broader neighborhood's testing data.

This data was de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected).

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco. During this investigation, some test results are found to be for persons living outside of San Francisco and some people in San Francisco may be tested multiple times (which is common). To see the number of new confirmed cases by neighborhood, reference this map: https://sf.gov/data/covid-19-case-maps#new-cases-maps

B. HOW THE DATASET IS CREATED COVID-19 laboratory test data is based on electronic laboratory test reports. Deduplication, quality assurance measures and other data verification processes maximize accuracy of laboratory test information. All testing data is then geo-coded by resident address. Then data is aggregated by analysis neighborhood and specimen collection date.

Data are prepared by close of business Monday through Saturday for public display.

C. UPDATE PROCESS Updates automatically at 05:00 Pacific Time each day. Redundant runs are scheduled at 07:00 and 09:00 in case of pipeline failure.

D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

Due to the high degree of variation in the time needed to complete tests by different labs there is a delay in this reporting. On March 24 the Health Officer ordered all labs in the City to report complete COVID-19 testing information to the local and state health departments.

In order to track trends over time, a data user can analyze this data by "specimen_collection_date".

Calculating Percent Positivity: The positivity rate is the percentage of tests that return a positive result for COVID-19 (positive tests divided by the sum of positive and negative tests). Indeterminate results, which could not conclusively determine whether COVID-19 virus was present, are not included in the calculation of percent positive. Percent positivity indicates how widespread COVID-19 is in San Francisco and it helps public health officials determine if we are testing enough given the number of people who are testing positive. When there are fewer than 20 positives tests for a given neighborhood and time period, the positivity rate is not calculated for the public tracker because rates of small test counts are less reliable.

Calculating Testing Rates: To calculate the testing rate per 10,000 residents, divide the total number of tests collected (positive, negative, and indeterminate results) for neighborhood by the total number of residents who live in that neighborhood (included in the dataset), then multiply by 10,000. When there are fewer than 20 total tests for a given neighborhood and time period, the testing rate is not calculated for the public tracker because rates of small test counts are less reliable.

Read more about how this data is updated and validated daily: https://sf.gov/information/covid-19-data-questions

E. CHANGE LOG

• 1/12/2024 - This dataset will stop updating as of 1/12/2024
• 6/21/2023 - A small number of additional COVID-19 testing records were released as part of our ongoing cleaning efforts.
• 1/31/2023 - updated “acs_population” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - implemented system updates to streamline and improve our geo-coded data, resulting in small shifts in our testing data by geography.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 1/31/2023 - removed the “multipolygon” column. To access the multipolygon geometry column for each geography unit, refer to COVID-19 Cases and Deaths Summarized by Geography.
• 4/16/2021 - dataset updated to refresh with a five-day data lag.

Effective June 7th, 2024, this dataset will no longer be updated.This file contains data on:

1. Cumulative count of Ottawa residents with laboratory-confirmed COVID-19 by episode date (i.e. the earliest of symptom onset, testing or reported date), including active cases and resolved cases.

2. Cumulative count of Ottawa residents with laboratory-confirmed COVID-19 who died by date of death.

3. Daily count of Ottawa residents with laboratory-confirmed COVID-19 by reported date and episode date.

4. Daily count of Ottawa residents with laboratory-confirmed COVID-19 by outbreak association and episode date.

5. Daily count of Ottawa residents with laboratory-confirmed COVID-19 newly admitted to the hospital, currently in hospital, and currently in the intensive care unit (ICU).

6. Cumulative rate of confirmed COVID-19 for Ottawa residents by age group and episode date.

7. Cumulative rate of confirmed COVID-19 for Ottawa residents by gender and episode date.

8. Daily count of Ottawa residents with laboratory-confirmed COVID-19 by source of infection and episode date.

Data are from the Ontario Ministry of Health Public Health Case and Contact Management Solution (CCM).

Accuracy: Points of consideration for interpretation of the data:

The percent of cases with no known epidemiological (epi) link, during the current day and previous 13 days, is calculated as the number of cases with no known epi link among all cases. The percent of cases with no known epi link is unstable during time periods with few cases.

Source of infection is based on a case's epidemiologic linkage. If no epidemiologic linkage is identified, source of infection is allocated using a hierarchy of risk factors: related to travel prior to April 1, 2020 > part of an outbreak > close or household contact of a known case > related to travel since April 1, 2020 > unspecified epidemiological link > no known source of infection > no information available.

Data are entered into and extracted by Ottawa Public Health from the Ontario Ministry of Health Public Health Case and Contact Management Solution (CCM). The CCM is a dynamic disease reporting system that allows for ongoing updates; data represent a snapshot at the time of extraction and may differ from previous or subsequent reports.

As the cases are investigated and more information is available, the dates are updated.

A person’s exposure may have occurred up to 14 days prior to onset of symptoms. Symptomatic cases occurring in approximately the last 14 days are likely under-reported due to the time for individuals to seek medical assessment, availability of testing, and receipt of test results.

Confirmed cases are those with a confirmed COVID-19 laboratory result as per the Ministry of Health Public health management of cases and contacts of COVID-19 in Ontario. March 25, 2020 version 6.0.

Counts will be subject to varying degrees of underreporting due to a variety of factors, such as disease awareness and medical care seeking behaviours, which may depend on severity of illness, clinical practice, changes in laboratory testing, and reporting behaviours.

Data on hospital admissions, ICU admissions and deaths are likely under-reported as these events may occur after the completion of public health follow up of cases. Cases that were admitted to hospital or died after follow-up was completed may not be captured in iPHIS or local health unit reporting tools.

Cases are associated with a specific, isolated community outbreak; an institutional outbreak (e.g. healthcare, childcare, education); or no known outbreak (i.e., sporadic).

The distribution of the source of infection among confirmed cases is impacted by the provincial guidance on testing.

Surveillance testing for COVID-19 began in long term care facilities on April 25, 2020.

Source of infection is allocated using a hierarchy: Related to travel prior to April 1, 2020 > Close contact of a known case or part of a community outbreak or source of infection is an institutional outbreak > Related to travel since April 1, 2020 > No known source of infection > Missing.

The percent of cases with unknown source, during the current day and previous 13 days, is calculated as the number of cases with no known source among cases who source of infection is not an institutional outbreak. Calculated over a 14 day period (i.e. the day of interest and the preceding 13 days). The percent of cases with no known source is unstable during time periods with few cases.

Update Frequency: Wednesdays

Attributes: Data fields:

Data fields:

Date – Date in format YYYY-MM-DD H:MM. The date type varies based on the column of interest and could be:

 - Episode date – Earliest of

symptom onset, test or reported date for cases;

 - Date of death – The date

the person was reported to have died

 - Reported date – Date the

confirmed laboratory results were reported to Ottawa Public Health

 - Hospitalization date

Cumulative Cases by Episode Date – cumulative number of Ottawa residents with laboratory-confirmed COVID-19 by episode date. Cumulative Resolved Cases by Episode Date – cumulative number of Ottawa residents with laboratory-confirmed COVID-19 that have not died and are either (1) assessed as ‘recovered’ in The CCM or (2) 14 days past their episode date and not currently hospitalized. Cumulative Active Cases by Episode Date– cumulative number of Ottawa residents with an active COVID-19 infection. Calculated as the total number of Ottawa residents with COVID-19 excluding resolved and deceased cases. Cumulative Deaths by Date of Death - cumulative number of Ottawa residents with laboratory-confirmed COVID-19 who died by date of death. Deaths are included whether or not COVID-19 was determined to be a contributing or underlying cause of death. Daily Cases by Reported Date – number of Ottawa residents with laboratory-confirmed COVID-19 by reported date 7-Day Average of Newly Reported Cases by Reported Date – number of Ottawa residents with laboratory-confirmed COVID-19 by reported date. Calculated over a 7 day period (i.e. the day of interest and the preceding 6 days). Daily Cases by Episode Date - number of Ottawa residents with laboratory-confirmed COVID-19 by episode date. Daily Cases Linked to a Community Outbreak by Episode Date – number of Ottawa residents with laboratory-confirmed COVID-19 associated with a specific isolated community outbreak by episode date. Daily Cases Linked to an Institutional Outbreak – number of Ottawa residents with laboratory-confirmed COVID-19 associated with a COVID-19 outbreak in a healthcare, childcare or educational establishment by case episode date. Healthcare institutions include places such as long-term care homes, retirement homes, hospitals, other healthcare institutions (e.g. group homes, shelters). Daily Cases Not Linked to an Institutional Outbreak (i.e. Sporadic Cases) – number of Ottawa residents with laboratory-confirmed COVID-19 not associated to an outbreak of COVID-19. Cases Newly Admitted to Hospital – Daily number of Ottawa residents with confirmed COVID-19 admitted to hospital. Emergency room visits are not included in the number of hospital admissions. Cases Currently in Hospital – Number of Ottawa residents with confirmed COVID-19 currently in hospital, includes patients in intensive care. Emergency room visits are not included in the number of hospitalizations. Cases Currently in ICU - Number of Ottawa residents with confirmed COVID-19 currently being treated in the intensive care unit (ICU). It is a subset of the count of hospitalized cases. Cumulative Rate of COVID-19 by 10-year Age Groupings (per 100,000 pop) and Episode Date – The number of Ottawa residents with confirmed COVID-19 within an age group (e.g. 0-9 years) divided by the total Ottawa population for that age group. This fraction is then multiplied by 100,000 to get a rate of COVID-19 per 100,000 population for that age group. Cumulative Rate of COVID-19 by Gender (per 100,000 pop) and Episode Date – The number of Ottawa residents with confirmed COVID-19 of a given gender (e.g. female) divided by the total Ottawa population for that gender. This fraction is then multiplied by 100,000 to get a rate of COVID-19 per 100,000 population for that gender. Source of infection is travel by episode date: individuals who are most likely to have acquired their infection during out-of-province travel. Number of cases with missing information on source of infection by episode date: assessment for source of infection was not completed. Number of cases with no known epidemiological link by episode date: individuals who did not travel outside Ontario, are not part of an outbreak, and are not able to identify someone with COVID-19 from whom they might have acquired infection. The assessment for source of infection was completed, but no sources were identified. Source of infection is a close contact by episode date: individuals presumed to have acquired their infection following close contact (e.g. household member, friend, relative) with an individual with confirmed COVID-19. Source of infection is an outbreak by episode date: individuals who are most likely to have acquired their infection as part of a confirmed COVID-19 outbreak. Source of Infection is Unknown by Episode Date: Ottawa residents with confirmed COVID-19 who did not travel outside

Trends in the place of death by cause of death in Japan in 2001–2021.

Vaccinations in London Between 8 December 2020 and 15 September 2021 5,838,305 1st doses and 5,232,885 2nd doses have been administered to London residents.

Differences in vaccine roll out between London and the Rest of England London Rest of England Priority Group Vaccinations given Percentage vaccinated Vaccinations given Percentage vaccinated Group 1 Older Adult Care Home Residents 21,883 95% 275,964 96% Older Adult Care Home Staff 29,405 85% 381,637 88% Group 2 80+ years 251,021 83% 2,368,284 93% Health Care Worker 174,944 99% 1,139,243 100%* Group 3 75 - 79 years 177,665 90% 1,796,408 99% Group 4 70 - 74 years 252,609 90% 2,454,381 97% Clinically Extremely Vulnerable 278,967 88% 1,850,485 95% Group 5 65 - 69 years 285,768 90% 2,381,250 97% Group 6 At Risk or Carer (Under 65) 983,379 78% 6,093,082 88% Younger Adult Care Home Residents 3,822 92% 30,321 93% Group 7 60 - 64 years 373,327 92% 2,748,412 98% Group 8 55 - 59 years 465,276 91% 3,152,412 97% Group 9 50 - 54 years 510,132 90% 3,141,219 95% Data as at 15 September 2021 for age based groups and as at 12 September 2021 for non-age based groups * The number who have received their first dose exceeds the latest official estimate of the population for this group There is considerable uncertainty in the population denominators used to calculate the percentage vaccinated. Comparing implied vaccination rates for multiple sources of denominators provides some indication of uncertainty in the true values. Confidence is higher where the results from multiple sources agree more closely. Because the denominator sources are not fully independent of one another, users should interpret the range of values across sources as indicating the minimum range of uncertainty in the true value. The following datasets can be used to estimate vaccine uptake by age group for London:

ONS 2020 mid-year estimates (MYE). This is the population estimate used for age groups throughout the rest of the analysis.


Number of people ages 18 and over on the National Immunisation Management Service (NIMS)


ONS Public Health Data Asset (PHDA) dataset. This is a linked dataset combining the 2011 Census, the General Practice Extraction Service (GPES) data for pandemic planning and research and the Hospital Episode Statistics (HES). This data covers a subset of the population.

Vaccine roll out in London by Ethnic Group Understanding how vaccine uptake varies across different ethnic groups in London is complicated by two issues:

Ethnicity information for recipients is unavailable for a very large number of the vaccinations that have been delivered. As a result, estimates of vaccine uptake by ethnic group are highly sensitive to the assumptions about and treatment of the Unknown group in calculations of rates.

For vaccinations given to people aged 50 and over in London nearly 10% do not have ethnicity information available,

The accuracy of available population denominators by ethnic group is limited. Because ethnicity information is not captured in official estimates of births, deaths, and migration, the available population denominators typically rely on projecting forward patterns captured in the 2011 Census. Subsequent changes to these patterns, particularly with respect to international migration, leads to increasing uncertainty in the accuracy of denominators sources as we move further away from 2011.

Comparing estimated population sizes and implied vaccination rates for multiple sources of denominators provides some indication of uncertainty in the true values. Confidence is higher where the results from multiple sources agree more closely. Because the denominator sources are not fully independent of one another, users should interpret the range of values across sources as indicating the minimum range of uncertainty in the true value. The following population estimates are available by Ethnic group for London:

GLA Ethnic group population projections - 2016 as at 2021


ONS Population Denominators produced for Race Disparity Audit as at 2018


ETHPOP population projections produced by the University of Leeds as at 2020

Antibody prevalence estimates As part of the ONS Coronavirus (COVID-19) Infection Survey ONS publish a modelled estimate of the percent of the adult population testing positive for antibodies to Coronavirus by region. Antibodies can be generated by vaccination or previous infection.

Vaccine effects on cases, hospitalisations and deaths When the vaccine roll out began in December 2020 coronavirus cases, hospital admissions and deaths were rising steeply. The peak of infections came in London in early January 2021, before reducing during the national lockdown and as the vaccine roll out progressed. As the vaccine roll out began in older age groups the effect of vaccinations can be separated from the effect of national lockdown by comparing changes in cases, admissions and deaths

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

Trends in the place of death by age group in Japan in 2001–2021.

Percentage of COVID-19 cases/deaths and population for African Americans and Hispanic/Latinos by DMV state.