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.

A. SUMMARY Medical provider confirmed COVID-19 cases and confirmed COVID-19 related deaths in San Francisco, CA aggregated by several different geographic areas and normalized by 2016-2020 American Community Survey (ACS) 5-year estimates for population data to calculate rate per 10,000 residents.

On September 12, 2021, a new case definition of COVID-19 was introduced that includes criteria for enumerating new infections after previous probable or confirmed infections (also known as reinfections). A reinfection is defined as a confirmed positive PCR lab test more than 90 days after a positive PCR or antigen test. The first reinfection case was identified on December 7, 2021.

Cases and deaths are both mapped to the residence of the individual, not to where they were infected or died. For example, if one was infected in San Francisco at work but lives in the East Bay, those are not counted as SF Cases or if one dies in Zuckerberg San Francisco General but is from another county, that is also not counted in this dataset.

Dataset is cumulative and covers cases going back to 3/2/2020 when testing began.

Geographic areas summarized are: 1. Analysis Neighborhoods 2. Census Tracts 3. Census Zip Code Tabulation Areas

B. HOW THE DATASET IS CREATED Addresses from medical data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area. The 2016-2020 American Community Survey (ACS) population estimates provided by the Census are used to create a rate which is equal to ([count] / [acs_population]) * 10000) representing the number of cases per 10,000 residents.

C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset daily at 7:30 Pacific Time.

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).

Privacy rules in effect To protect privacy, certain rules are in effect: 1. Case counts greater than 0 and less than 10 are dropped - these will be null (blank) values 2. Death counts greater than 0 and less than 10 are dropped - these will be null (blank) values 3. Cases and deaths dropped altogether for areas where acs_population < 1000

Rate suppression in effect where counts lower than 20 Rates are not calculated unless the case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology.

A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are areal representations of routes. Read how the Census develops ZCTAs on their website.

Row included for Citywide case counts, incidence rate, and deaths A single row is included that has the Citywide case counts and incidence rate. This can be used for comparisons. Citywide will capture all cases regardless of address quality. While some cases cannot be mapped to sub-areas like Census Tracts, ongoing data quality efforts result in improved mapping on a rolling basis.

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases and deaths summarized by geography are no longer being updated. This data is currently through 9/6/2023 and will not include any new data after this date.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 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 case and death data by geography.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 2/23/2022 - the New Cases Map dashboard began pulling from this dataset. To access Cases by Geography Over Time, please refer to this dataset.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 7/15/2022 - reinfections added to cases dataset. See section SUMMARY for more information on how reinfections are identified.
• 4/16/2021 - dataset updated to refresh with a five-day data lag.

As of April 19, 2021, there had been around 27 deaths due to COVID-19 in New York City per 10,000 population. New York has been one of the U.S. states most impacted by the COVID-19 pandemic, with New York accounting for the most deaths of any state in the U.S. This statistic shows the death rates for coronavirus (COVID-19) in New York State as of April 19, 2021, by county.

As of March 10, 2023, the death rate from COVID-19 in the state of New York was 397 per 100,000 people. New York is one of the states with the highest number of COVID-19 cases.

This dataset is no longer being updated as of 5/11/2023. It is being retained on the Open Data Portal for its potential historical interest.

This table displays the number of COVID-19 deaths among Cambridge residents by race and ethnicity. The count reflects total deaths among Cambridge COVID-19 cases.

The rate column shows the rate of COVID-19 deaths among Cambridge residents by race and ethnicity. The rates in this chart were calculated by dividing the total number of deaths among Cambridge COVID-19 cases for each racial or ethnic category by the total number of Cambridge residents in that racial or ethnic category, and multiplying by 10,000. The rates are considered “crude rates” because they are not age-adjusted. Population data are from the U.S. Census Bureau’s 2014–2018 American Community Survey estimates and may differ from actual population counts.

Of note:

This chart reflects the time period of March 25 (first known Cambridge death) through present.

It is important to note that race and ethnicity data are collected and reported by multiple entities and may or may not reflect self-reporting by the individual case. The Cambridge Public Health Department (CPHD) is actively reaching out to cases to collect this information. Due to these efforts, race and ethnicity information have been confirmed for over 80% of Cambridge cases, as of June 2020.

Race/Ethnicity Category Definitions: “White” indicates “White, not of Hispanic origin.” “Black” indicates “Black, not of Hispanic origin.” “Hispanic” refers to a person having Hispanic origin. A person having Hispanic origin may be of any race. “Asian” indicates “Asian, not of Hispanic origin.” To protect individual privacy, a category is suppressed when it has one to four people. Categories with zero cases are reported as zero. "Other" indicates multiple races, another race that is not listed above, and cases who have reported nationality in lieu of a race category recognized by the US Census. Population data are from the U.S. Census Bureau’s 2014–2018 American Community Survey estimates and may differ from actual population counts. "Other" also includes a small number of people who identify as Native American or Native Hawaiian/Pacific islander. Because the count for Native Americans or Native Hawaiian/Pacific Islanders is currently < 5 people, these categories have been combined with “Other” to protect individual privacy.

A. SUMMARY This dataset contains COVID-19 positive confirmed cases aggregated by several different geographic areas and by day. COVID-19 cases are mapped to the residence of the individual and shown on the date the positive test was collected. In addition, 2016-2020 American Community Survey (ACS) population estimates are included to calculate the cumulative rate per 10,000 residents. Dataset covers cases going back to 3/2/2020 when testing began. This data may not be immediately available for recently reported cases and data will change to reflect as information becomes available. Data updated daily. Geographic areas summarized are: 1. Analysis Neighborhoods 2. Census Tracts 3. Census Zip Code Tabulation Areas B. HOW THE DATASET IS CREATED Addresses from the COVID-19 case data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area for a given date. The 2016-2020 American Community Survey (ACS) population estimates provided by the Census are used to create a cumulative rate which is equal to ([cumulative count up to that date] / [acs_population]) * 10000) representing the number of total cases per 10,000 residents (as of the specified date). COVID-19 case data undergo quality assurance and other data verification processes and are continually updated to maximize completeness and accuracy of information. This means data may change for previous days as information is updated. C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset daily at 05:00 Pacific Time. 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). This dataset can be used to track the spread of COVID-19 throughout the city, in a variety of geographic areas. Note that the new cases column in the data represents the number of new cases confirmed in a certain area on the specified day, while the cumulative cases column is the cumulative total of cases in a certain area as of the specified date. Privacy rules in effect To protect privacy, certain rules are in effect: 1. Any area with a cumulative case count less than 10 are dropped for all days the cumulative count was less than 10. These will be null values. 2. Once an area has a cumulative case count of 10 or greater, that area will have a new row of case data every day following. 3. Cases are dropped altogether for areas where acs_population < 1000 4. Deaths data are not included in this dataset for privacy reasons. The low COVID-19 death rate in San Francisco, along with other publicly available information on deaths, means that deaths data by geography and day is too granular and potentially risky. Read more in our privacy guidelines Rate suppression in effect where counts lower than 20 Rates are not calculated unless the cumulative case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology. A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are spec

The death rate in New York City for adults aged 75 years and older was around 4,135 per 100,000 people as of December 22, 2022. The risk of developing more severe illness from COVID-19 increases with age, and the virus also poses a particular threat to people with underlying health conditions.

What is the death toll in NYC? The first coronavirus-related death in New York City was recorded on March 11, 2020. Since then, the total number of confirmed deaths has reached 37,452 while there have been 2.6 million positive tests for the disease. The number of daily new deaths in New York City has fallen sharply since nearly 600 residents lost their lives on April 7, 2020. A significant number of fatalities across New York State have been linked to long-term care facilities that provide support to vulnerable elderly adults and individuals with physical disabilities.

The impact on the counties of New York State Nearly every county in the state of New York has recorded at least one death due to the coronavirus. Outside of New York City, the counties of Nassau, Suffolk, and Westchester have confirmed over 11,500 deaths between them. When analyzing the ratio of deaths to county population, Rockland had one of the highest COVID-19 death rates in New York State in 2021. The county, which has approximately 325,700 residents, had a death rate of around 29 per 10,000 people in April 2021.

As of January 13, 2023, Bulgaria had the highest rate of COVID-19 deaths among its population in Europe at 548.6 deaths per 100,000 population. Hungary had recorded 496.4 deaths from COVID-19 per 100,000. Furthermore, Russia had the highest number of confirmed COVID-19 deaths in Europe, at over 394 thousand.

Number of cases in Europe During the same period, across the whole of Europe, there have been over 270 million confirmed cases of COVID-19. France has been Europe's worst affected country with around 38.3 million cases, this translates to an incidence rate of approximately 58,945 cases per 100,000 population. Germany and Italy had approximately 37.6 million and 25.3 million cases respectively.

Current situation In March 2023, the rate of cases in Austria over the last seven days was 224 per 100,000 which was the highest in Europe. Luxembourg and Slovenia both followed with seven day rates of infections at 122 and 108 respectively.

The COVID-19 Vulnerability and Recovery Index uses Tract and ZIP Code-level data* to identify California communities most in need of immediate and long-term pandemic and economic relief. Specifically, the Index is comprised of three components — Risk, Severity, and Recovery Need with the last scoring the ability to recover from the health, economic, and social costs of the pandemic. Communities with higher Index scores face a higher risk of COVID-19 infection and death and a longer uphill economic recovery. Conversely, those with lower scores are less vulnerable.

The Index includes one overarching Index score as well as a score for each of the individual components. Each component includes a set of indicators we found to be associated with COVID-19 risk, severity, or recovery in our review of existing indices and independent analysis. The Risk component includes indicators related to the risk of COVID-19 infection. The Severity component includes indicators designed to measure the risk of severe illness or death from COVID-19. The Recovery Need component includes indicators that measure community needs related to economic and social recovery. The overarching Index score is designed to show level of need from Highest to Lowest with ZIP Codes in the Highest or High need categories, or top 20th or 40th percentiles of the Index, having the greatest need for support.

The Index was originally developed as a statewide tool but has been adapted to LA County for the purposes of the Board motion. To distinguish between the LA County Index and the original Statewide Index, we refer to the revised Index for LA County as the LA County ARPA Index.

*Zip Code data has been crosswalked to Census Tract using HUD methodology

Indicators within each component of the LA County ARPA Index are:Risk: Individuals without U.S. citizenship; Population Below 200% of the Federal Poverty Level (FPL); Overcrowded Housing Units; Essential Workers Severity: Asthma Hospitalizations (per 10,000); Population Below 200% FPL; Seniors 75 and over in Poverty; Uninsured Population; Heart Disease Hospitalizations (per 10,000); Diabetes Hospitalizations (per 10,000)Recovery Need: Single-Parent Households; Gun Injuries (per 10,000); Population Below 200% FPL; Essential Workers; Unemployment; Uninsured PopulationData are sourced from US Census American Communities Survey (ACS) and the OSHPD Patient Discharge Database. For ACS indicators, the tables and variables used are as follows:

Indicator

ACS Table/Years

Numerator

Denominator

Non-US Citizen

B05001, 2019-2023

b05001_006e

b05001_001e

Below 200% FPL

S1701, 2019-2023

s1701_c01_042e

s1701_c01_001e

Overcrowded Housing Units

B25014, 2019-2023

b25014_006e + b25014_007e + b25014_012e + b25014_013e

b25014_001e

Essential Workers

S2401, 2019-2023

s2401_c01_005e + s2401_c01_011e + s2401_c01_013e + s2401_c01_015e + s2401_c01_019e + s2401_c01_020e + s2401_c01_023e + s2401_c01_024e + s2401_c01_029e + s2401_c01_033e

s2401_c01_001

Seniors 75+ in Poverty

B17020, 2019-2023

b17020_008e + b17020_009e

b17020_008e + b17020_009e + b17020_016e + b17020_017e

Uninsured

S2701, 2019-2023

s2701_c05_001e

NA, rate published in source table

Single-Parent Households

S1101, 2019-2023

s1101_c03_005e + s1101_c04_005e

s1101_c01_001e

Unemployment

S2301, 2019-2023

s2301_c04_001e

NA, rate published in source table

The remaining indicators are based data requested and received by Advancement Project CA from the OSHPD Patient Discharge database. Data are based on records aggregated at the ZIP Code level:

Indicator

Years

Definition

Denominator

Asthma Hospitalizations

2017-2019

All ICD 10 codes under J45 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Gun Injuries

2017-2019

Principal/Other External Cause Code "Gun Injury" with a Disposition not "Died/Expired". ICD 10 Code Y38.4 and all codes under X94, W32, W33, W34, X72, X73, X74, X93, X95, Y22, Y23, Y35 [All listed codes with 7th digit "A" for initial encounter]

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Heart Disease Hospitalizations

2017-2019

ICD 10 Code I46.2 and all ICD 10 codes under I21, I22, I24, I25, I42, I50 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Diabetes (Type 2) Hospitalizations

2017-2019

All ICD 10 codes under E11 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

For more information about this dataset, please contact egis@isd.lacounty.gov.

Additional inpatient cases/hospitalizations, ICU admissions and deaths (per 10,000 total population in country and as percentage of total COVID-19 outcomes) related to overweight and obesity in 2020 and 2021.

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.

This record contains raw data related to article “Incidence rates of hospitalization and death from COVID-19 in patients with psoriasis receiving biological treatment: a Northern Italy experience"

Introduction: Whether biologic therapies enhance the risk of coronavirus 2019 (COVID-19) or affect the disease outcome in patients with chronic plaque psoriasis remains to be ascertained.

Objective: We sought to investigate the incidence of hospitalization and death for COVID-19 in a large sample of patients with plaque psoriasis receiving biologic therapies compared with the general population.

Methods: This is a retrospective multicenter cohort study including patients with chronic plaque psoriasis (n = 6501) being treated with biologic therapy and regularly followed up at the divisions of dermatology of several main hospitals in the Northern Italian cities of Verona, Padua, Vicenza, Modena, Bologna, Piacenza, Turin, and Milan. Incidence rates of hospitalization and death per 10,000 person-months with exact mid-p 95% CIs and standardized incidence ratios were estimated in the patients with psoriasis and compared with those in the general population in the same geographic areas.

Results: The incidence rate of hospitalization for COVID-19 was 11.7 (95% CI, 7.2-18.1) per 10,000 person-months in patients with psoriasis and 14.4 (95% CI, 14.3-14.5) in the general population; the incidence rate of death from COVID-19 was 1.3 (95% CI, 0.2-4.3) and 4.7 (95% CI, 4.6-4.7) in patients with psoriasis and the general population, respectively. The standardized incidence ratio of hospitalization and death in patients with psoriasis compared with those in the general population was 0.94 (95% CI, 0.57-1.45; P = .82) and 0.42 (95% CI, 0.07-1.38; P = .19), respectively.

Conclusions: Our data did not show any adverse impact of biologics on COVID-19 outcome in patients with psoriasis. We would not advise biologic discontinuation in patients on treatment since more than 6 months and not infected with severe acute respiratory syndrome coronavirus 2 to prevent hospitalization and death from COVID-19.

This dataset combines multiple open data sets for Covid-19 cases and deaths (kaggle1), Death causes (ourworldindata1, ourworldindata2, ourworldindata3, Food sources (FAO1), Health Care System (WHO1, WHO2, WHO3), TB vaccine status (BCG1) School closures (UNESCO1), and People/Society facts (CIA1).

Background: Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 125 million cases, and more than 2.7 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonitis, adult respiratory distress syndrome (ARDS) and death. Many patients required ventilatory support including high flow oxygen, continuous positive airway pressure and intubated with or without tracheotomy. There was considerable learning on how to manage COVID-19 during the pandemic and new drugs became available during the different waves. This secondary care COVID dataset contains granular ventilatory, demographic, morbidity, serial acuity, medications and outcome data in COVID-19 across all waves and will be continuously refreshed.

PIONEER geography: The West Midlands (WM) has a population of 5.9 million and includes a diverse ethnic and 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, more than 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 across all waves.

Electronic Health Records (EHR): University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and 100 ITU beds. ITU capacity increased to 250 beds during the COVID pandemic. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary and secondary care record (Your Care Connected) and a patient portal “My Health”. UHB has cared for more than 10,000 COVID admissions to date.

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

Available supplementary data: Ambulance, 111, 999 data, synthetic data.

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

As of June 6, 2022, the novel coronavirus SARS-CoV-2 that originated in Wuhan, the capital of Hubei province in China, had infected over 2.1 million people and killed 14,612 in the country. Hong Kong is currently the region with the highest active cases in China.

From Wuhan to the rest of China

In late December 2019, health authorities in Wuhan detected several pneumonia cases of unknown cause. Most of these patients had links to the Huanan Seafood Market. With Chinese New Year approaching, millions of Chinese migrant workers travelled back to their hometowns for the celebration. Before the start of the travel ban on January 23, around five million people had left Wuhan. By the end of January, the number of infections had surged to over ten thousand. The death toll from the virus exceeded that of the SARS outbreak a few days later. On February 12, thousands more cases were confirmed in Wuhan after an improvement to the diagnosis method, resulting in another sudden surge of confirmed cases. On March 31, 2020, the National Health Commission (NHC) in China announced that it would begin reporting the infection number of symptom-free individuals who tested positive for coronavirus. On April 17, 2020, health authorities in Wuhan revised its death toll, adding 50 percent more fatalities. After quarantine measures were implemented, the country reported no new local coronavirus COVID-19 transmissions for the first time on March 18, 2020.

The overloaded healthcare system

In Wuhan, 28 hospitals were designated to treat coronavirus patients, but the outbreak continued to test China’s disease control system and most of the hospitals were soon fully occupied. To combat the virus, the government announced plans to build a new hospital swiftly. On February 3, 2020, Huoshenshan Hospital was opened to provide an additional 1,300 beds. Due to an extreme shortage of health-care professionals in Wuhan, thousands of medical staff from all over China came voluntarily to the epicenter to offer their support. After no new deaths reported for first time, China lifted ten-week lockdown on Wuhan on April 8, 2020. Daily life was returning slowly back to normal in the country.

In early-February, 2020, the first cases of the coronavirus (COVID-19) were reported in the United Kingdom (UK). The number of cases in the UK has since risen to 24,243,393, with 1,062 new cases reported on January 13, 2023. The highest daily figure since the beginning of the pandemic was on January 6, 2022 at 275,646 cases.

COVID deaths in the UK COVID-19 has so far been responsible for 202,157 deaths in the UK as of January 13, 2023, and the UK has one of the highest death toll from COVID-19 in Europe. As of January 13, the incidence of deaths in the UK is 298 per 100,000 population.

Regional breakdown The South East has the highest amount of cases in the country with 3,123,050 confirmed cases as of January 11. London and the North West have 2,912,859 and 2,580,090 cases respectively.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

There were 11,480 deaths registered in England and Wales for the week ending November 14, 2025, compared with 11,297 in the previous week. During this time period, the two weeks with the highest number of weekly deaths were in April 2020, with the week ending April 17, 2020, having 22,351 deaths, and the following week 21,997 deaths, a direct result of the COVID-19 pandemic in the UK. Death and life expectancy As of 2022, the life expectancy for women in the UK was just over 82.5 years, and almost 78.6 years for men. Compared with 1765, when average life expectancy was under 39 years, this is a huge improvement in historical terms. Even in the more recent past, life expectancy was less than 47 years at the start of the 20th Century, and was under 70 as recently as the 1950s. Despite these significant developments in the long-term, improvements in life expectancy stalled between 2009/11 and 2015/17, and have even gone into decline since 2020. Between 2020 and 2022, for example, life expectancy at birth fell by 23 weeks for females, and 37 weeks for males. COVID-19 in the UK The first cases of COVID-19 in the United Kingdom were recorded on January 31, 2020, but it was not until a month later that cases began to rise exponentially. By March 5 of this year there were more than 100 cases, rising to 1,000 days later and passing 10,000 cumulative cases by March 26. At the height of the pandemic in late April and early May, there were around six thousand new cases being recorded daily. As of January 2023, there were more than 24.2 million confirmed cumulative cases of COVID-19 recorded in the United Kingdom, resulting in 202,156 deaths.

In 2022, the most common cause of death in Sweden was diseases of the circulatory system. More than 28,000 people died because of these diseases. Cancer was the second most common cause of death in Sweden. Furthermore, diseases of the respiratory system caused over 6,000 deaths in Sweden in 2022.

Ischemic heart disease most common cause

Chronic ischemic heart disease is the circulation system disease that causes the most number of deaths. When ischemic heart disease occurs, the arteries of the heart are blocked and the blood flow to the heart muscle is reduced. Heart attacks caused the second most deaths of the circulatory system diseases.

 COVID-19

From 2019 to 2020, the total number of deaths in Sweden increased by around 10,000, almost reaching 100,000 in total. This can be explained by the more than 9,400 deaths caused by the coronavirus (COVID-19). At the beginning of the pandemic, the Swedish government tried a different approach than most other European countries, avoiding strict lockdowns and regulations. However, it has recorded a higher number of deaths and cases than the other Nordic countries. As of January 2023, nearly 23,000 people had died of COVID-19 in Sweden.

In Italy, approximately ******* deaths were registered in 2022. According to the data, ischemic heart diseases were the most common cause of death in the country, with ****** cases registered, closely followed by cerebrovascular diseases. COVID-19 was the third illness causing the largest number of deaths in Italy. COVID-19 death comorbidities Most patients admitted to the hospital and later deceased with the coronavirus (COVID-19) infection showed one or more comorbidities. Hypertension was the most common pre-existing health condition, detected in **** percent of patients who died after contracting the virus. Type 2-diabetes, ischemic heart disease, and atrial fibrillation were also among the most common comorbidities in COVID-19 patients who lost their lives. Cancer deaths The number of people who died from a tumor in Italy decreased constantly between 2006 and 2021. Indeed, the rate of deaths due to cancer among Italians dropped from **** deaths per 10,000 inhabitants in 2006 to **** in 2021. The Italian region with the highest cancer mortality rate was Campania, followed by Sardinia, and Sicily.