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

Global Covid-19 Data

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

By Valtteri Kurkela [source]

About this dataset

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

Some of the key metrics covered in the dataset include:

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

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

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

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

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

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

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

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

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

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

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

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

How to use the dataset

Introduction:

• Understanding the Basic Structure:

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

• Selecting Desired Columns:

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

• Filtering Data:

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

• Analyzing Vaccination Metrics:

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

• Investigating Testing Information:

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

• Exploring Hospitalization and ICU Data:

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

• Assessing Covid-19 Cases and Deaths:

  • Analyze variables like total_cases, new_ca...

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:

Context

All about an attempt to end the pandemic across the globe with the help of vaccinations for COVID-19. It is important to track and understand the effort that is in progress across the globe to administer doses of vaccinations. There could be many sources of information. This is one of the sources from Bloomberg that is captured and presented here. Additionally, I have tried to include the GDP per capita per country from Wiki so that we can see how that is influencing the vaccination progress.

Content

There are two files. a) Latest Global Covid-19 Vaccine tracker of all the countries and regions in the World as of September 11, 2021 b) GDP information per capita per country

Attribute Information (COVID19 vaccination Tracker file)

• Countries and regions - Name of countries
• Doses administered - Number of vaccine doses administered
• Enough for % of people - Number of vaccine doses administered as a % of population
• Percentage of population with 1+ dose - Percentage of the population vaccinated with at least 1+ dose
• Percentage of the population fully vaccinated - Percentage of the population fully vaccinated
• Daily rate of doses administered - Daily rate of doses administered

Attribute Information (for GDP file per country per capita)

• Country
• Subregion (Western Europe, Northern Europe etc.)
• Region (Europe, Asia etc.)
• GDP estimate $ as per IMF
• Year for IMF
• GDP estimate $ as per UN
• Year for UN
• GDP estimate $ as per World Bank
• Year for World Bank

Source

URL1: https://www.bloomberg.com/graphics/covid-vaccine-tracker-global-distribution/ URL2: https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal)_per_capita

Inspiration

The path to immunity and hope to get back to normalcy by tracking and analyzing the latest updates on vaccinations across the globe. As we gear up to end the pandemic, the vaccination tracker can help us answer the following questions.

• What are the Top N countries/regions where vaccinations are administered?
• What are the Top N countries/regions with fully vaccinated people?
• What are the Top N countries/regions with at least 1+ doses administered?
• What is the access to vaccines - by least wealthy and most wealthy countries? (based on GDP per capita per country data)
• What is the average daily rate of the dose administered? Which countries are Top N and Bottom N? Which countries are above and below the World average? and many more...

Thank you for reading.

Please give your feedback/upvote/comments if you find this useful and download.

Background COVID-19 pandemic had unprecedented global impact on health and society, highlighting the need for a detailed understanding of SARS-CoV-2 evolution in response to host and environmental factors. This study investigates the evolution of SARS-CoV-2 via mutation dynamics, focusing on distinct age cohorts, geographical location, and vaccination status within the Indian population, one of the nations most affected by COVID-19. Methodology Comprehensive dataset, across diverse time points during the Alpha, Delta, and Omicron variant waves, captured essential phases of the pandemic’s footprint in India. By leveraging genomic data from Global Initiative on Sharing Avian Influenza Data (GISAID), we examined the substitution mutation landscape of SARS-CoV-2 in three demographic segments: children (1–17 years), working-age adults (18–64 years), and elderly individuals (65+ years). A balanced dataset of 69,975 samples was used for the study, comprising 23,325 samples from each group. This design ensured high statistical power, as confirmed by power analysis. We employed bioinformatics and statistical analyses, to explore genetic diversity patterns and substitution frequencies across the age groups. Principal findings The working-age group exhibited a notably high frequency of unique substitutions, suggesting that immune pressures within highly interactive populations may accelerate viral adaptation. Geographic analysis emphasizes notable regional variation in substitution rates, potentially driven by population density and local transmission dynamics, while regions with more homogeneous strain circulation show relatively lower substitution rates. The analysis also revealed a significant surge in unique substitutions across all age groups during the vaccination period, with substitution rates remaining elevated even after widespread vaccination, compared to pre-vaccination levels. This trend supports the virus's adaptive response to heightened immune pressures from vaccination, as observed through the increased prevalence of substitutions in important regions of SARS-CoV-2 genome like ORF1ab and Spike, potentially contributing to immune escape and transmissibility. Conclusion Our findings affirm the importance of continuous surveillance on viral evolution, particularly in countries with high transmission rates. This research provides insights for anticipating future viral outbreaks and refining pandemic preparedness strategies, thus enhancing our capacity for proactive global health responses.

Dataset 1: Clinical Symptom Profile of Community COVID-19 CasesScope of the Data:This research project involves a comprehensive analysis of the clinical profiles of COVID-19 infections within a community-based population. The primary objective is to characterize the clinical profiles associated with SARS-CoV-2 infection in a non-hospitalized setting. The study comprises a substantial sample size of over 10,000 participants. The target participants are individuals aged ≥5 years or older from the community in Hong Kong. This study captured a wide range of demographics and disease severities, from asymptomatic to severe cases typically managed outside of a hospital environment.Description of the Data Files:The data files contain systematically collected, de-identified information on the signs and symptoms reported or observed in confirmed COVID-19 patients. This dataset generally includes details on common symptoms. The data allows for the analysis of symptom patterns, duration, and severity within the large cohort. The information is structured to facilitate research into the symptomatic patterns of community-acquired COVID-19.Dataset 2: Side Effect Profiles Following First-Dose Influenza Vaccination in School-Aged Children1. Scope of the Data:This study focuses on the side effects following the first dose of seasonal influenza vaccination among children participating in Hong Kong's School Outreach Vaccination Program (SOVP), from 2018/2019 to 2023/2024. The data encompasses a large sample size of over 10,000 children who were enrolled in SOVP. The target participants are school-aged children who received their first dose of the seasonal influenza vaccine through this organized public health initiative.2. Description of the Data Files:The data files consist of de-identified records documenting the side effects reported following influenza vaccination. The data generally captures the spectrum of common reactions. The nature of the data across multiple seasons allows for the examination of consistency in the safety profile year-over-year. The information is valuable for understanding the typical reaction patterns to the first dose of the influenza vaccine in a pediatric population within a real-world, school-based vaccination setting.

The Registry has captured a consented research dataset from 9,800 patients with a further 30,000 ethically permissioned records for research related to IBD and COVID-19. This includes patient demographics, medications; plus vaccinations, responses and care received during COVID-19 period April 2020-June 2021. The original source of the data was the IBD Registry's COVID-19 IBD Risk Tool, which was launched at the start of the pandemic (1 April 2020) to allow people with IBD to self-assess their risk. It had a high uptake, with over 16,000 people completing it in the first week alone; by the first end of shielding in August 2020 ovr 37,000 people with IBD had completed it. Ethical permission was sought and received to re-contact participants for use of this data in research relate to IBD and COVID-19, along with a follow-on survey to give a second timepoint about one year later, which was by June 2021. 9,800 people consented and completed the follow-on (second timepoint) survey, with the ethical permissions allowing the original dataset (first timepoint only) to also be used in research under more restricted permissions providing all requests for withdrawal fulfilled.

CATCH-UP Focus group and interview participant demographics (N = 27).

Pairwise comparisons of age groups using chi-square statistics.

The aim of the publication is to inform users about activity and usage of GP appointments historically and how primary care is impacted by seasonal pressures, such as winter. NHS England publishes this information to support winter preparedness and provide information about some activity within primary care. The publication covers historic appointments, marked as attended or did not attend, from national to sub ICB location coverage. The aim is to inform users, who range from a healthcare professional to an inquiring citizen, about appointments within primary care. The publication includes data from participating practices using EMIS, TPP, Eva Health formerly known as Microtest (up until February 2021), Informatica, Cegedim (previously Vision) and Babylon (GP at Hand) GP systems. NHS England produce this information monthly, containing information about the most recent month and previous months. The publication includes important information, however it does not show the totality of GP activity/workload. The data presented only contains information which was captured on the GP practice systems. This limits the activity reported on and does not represent all work happening within a primary care setting or assess the complexity of activity. No patient identifiable information has been collected or is included in this release. Between December 2020 and present the data contained in this publication will no longer contain covid-19 vaccination activity collected from GP System Suppliers as part of the General Practice Appointments Data. These appointments have been removed using the methodology outlined in the supporting information. In order to gain a more complete picture of general practice activity we will publish covid-19 vaccination activity carried out by PCN’s or GP Practice’s from the NIMS (National Immunisation Management Service) vaccination dataset. This publication now includes statistics on the duration of appointments, SDS role and the recorded national category, service setting and context type of the appointment. Both HCP Type and SDS role are currently presented for comparison purposes, but moving forward the intention is to only publish SDS Role Groups and remove HCP Type. Further information can be found in the supporting guidance below.

Kolmogorov-Smirnov test results for temporal distribution.