As part of an ongoing partnership with the Census Bureau, the National Center for Health Statistics (NCHS) recently added questions to assess the prevalence of post-COVID-19 conditions (long COVID), on the experimental Household Pulse Survey. This 20-minute online survey was designed to complement the ability of the federal statistical system to rapidly respond and provide relevant information about the impact of the coronavirus pandemic in the U.S. Data collection began on April 23, 2020. Beginning in Phase 3.5 (on June 1, 2022), NCHS included questions about the presence of symptoms of COVID that lasted three months or longer. Phase 3.5 will continue with a two-weeks on, two-weeks off collection and dissemination approach. Estimates on this page are derived from the Household Pulse Survey and show the percentage of adults aged 18 and over who a) as a proportion of the U.S. population, the percentage of adults who EVER experienced post-COVID conditions (long COVID). These adults had COVID and had some symptoms that lasted three months or longer; b) as a proportion of adults who said they ever had COVID, the percentage who EVER experienced post-COVID conditions; c) as a proportion of the U.S. population, the percentage of adults who are CURRENTLY experiencing post-COVID conditions. These adults had COVID, had long-term symptoms, and are still experiencing symptoms; d) as a proportion of adults who said they ever had COVID, the percentage who are CURRENTLY experiencing post-COVID conditions; and e) as a proportion of the U.S. population, the percentage of adults who said they ever had COVID.

This dataset provides detailed estimates on Post-COVID Conditions, often referred to as "Long COVID," across the United States. The data is sourced from the Household Pulse Survey, a rapid response survey from the U.S. Census Bureau in partnership with the National Center for Health Statistics (NCHS) and other federal agencies. It presents the percentage of adults who have ever experienced Long COVID, who are currently experiencing it, and the extent to which these persistent symptoms limit their daily activities. The data is broken down by various demographic factors including age, sex, gender identity, sexual orientation, race/ethnicity, and education level, as well as by state.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since the first reported coronavirus case in Washington State on Jan. 21, 2020, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

As of 10/22/2020, this dataset is no longer being updated and has been replaced with a new dataset, which can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/hree-nys2 This dataset includes a count and rate per 100,000 population for COVID-19 cases, a count of COVID-19 PCR 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. 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).

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

Global Covid-19 Data

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

By Valtteri Kurkela [source]

About this dataset

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

Some of the key metrics covered in the dataset include:

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

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

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

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

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

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

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

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

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

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

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

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

How to use the dataset

Introduction:

• Understanding the Basic Structure:

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

• Selecting Desired Columns:

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

• Filtering Data:

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

• Analyzing Vaccination Metrics:

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

• Investigating Testing Information:

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

• Exploring Hospitalization and ICU Data:

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

• Assessing Covid-19 Cases and Deaths:

  • Analyze variables like total_cases, new_ca...

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

This archived public use dataset has 11 data elements reflecting United States COVID-19 community levels for all available counties.

The COVID-19 community levels were developed using a combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days. The COVID-19 community level was determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge.

Using these data, the COVID-19 community level was classified as low, medium, or high.

COVID-19 Community Levels were used to help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals.

For the most accurate and up-to-date data for any county or state, visit the relevant health department website. COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.

Archived Data Notes:

This dataset was renamed from "United States COVID-19 Community Levels by County as Originally Posted" to "United States COVID-19 Community Levels by County" on March 31, 2022.

March 31, 2022: Column name for county population was changed to “county_population”. No change was made to the data points previous released.

March 31, 2022: New column, “health_service_area_population”, was added to the dataset to denote the total population in the designated Health Service Area based on 2019 Census estimate.

March 31, 2022: FIPS codes for territories American Samoa, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands were re-formatted to 5-digit numeric for records released on 3/3/2022 to be consistent with other records in the dataset.

March 31, 2022: Changes were made to the text fields in variables “county”, “state”, and “health_service_area” so the formats are consistent across releases.

March 31, 2022: The “%” sign was removed from the text field in column “covid_inpatient_bed_utilization”. No change was made to the data. As indicated in the column description, values in this column represent the percentage of staffed inpatient beds occupied by COVID-19 patients (7-day average).

March 31, 2022: Data values for columns, “county_population”, “health_service_area_number”, and “health_service_area” were backfilled for records released on 2/24/2022. These columns were added since the week of 3/3/2022, thus the values were previously missing for records released the week prior.

April 7, 2022: Updates made to data released on 3/24/2022 for Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands to correct a data mapping error.

April 21, 2022: COVID-19 Community Level (CCL) data released for counties in Nebraska for the week of April 21, 2022 have 3 counties identified in the high category and 37 in the medium category. CDC has been working with state officials t

Description Source data: https://www.chicago.gov/city/en/sites/covid-19/home/latest-data.html.

Only Chicago residents are included based on the home ZIP Code as provided by the medical provider. If a ZIP was missing or was not valid, it is displayed as "Unknown".

Cases with a positive molecular (PCR) or antigen test are included in this dataset. Cases are counted based on the week the test specimen was collected. For privacy reasons, until a ZIP Code reaches five cumulative cases, both the weekly and cumulative case counts will be blank. Therefore, summing the “Cases - Weekly” column is not a reliable way to determine case totals. Deaths are those that have occurred among cases based on the week of death.

For tests, each test is counted once, based on the week the test specimen was collected. Tests performed prior to 3/1/2020 are not included. Test counts include multiple tests for the same person (a change made on 10/29/2020). PCR and antigen tests reported to Chicago Department of Public Health (CDPH) through electronic lab reporting are included. Electronic lab reporting has taken time to onboard and testing availability has shifted over time, so these counts are likely an underestimate of community infection.

The “Percent Tested Positive” columns are calculated by dividing the number of positive tests by the number of total tests . Because of the data limitations for the Tests columns, such as persons being tested multiple times as a requirement for employment, these percentages may vary in either direction from the actual disease prevalence in the ZIP Code.

All data are provisional and subject to change. Information is updated as additional details are received.

To compare ZIP Codes to Chicago Community Areas, please see http://data.cmap.illinois.gov/opendata/uploads/CKAN/NONCENSUS/ADMINISTRATIVE_POLITICAL_BOUNDARIES/CCAzip.pdf. Both ZIP Codes and Community Areas are also geographic datasets on this data portal.

Data Source: Illinois National Electronic Disease Surveillance System, Cook County Medical Examiner’s Office, Illinois Vital Records, American Community Survey (2018)

Note: After May 3, 2024, this dataset will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, hospital capacity, or occupancy data to HHS through CDC’s National Healthcare Safety Network (NHSN). The related CDC COVID Data Tracker site was revised or retired on May 10, 2023.

This dataset represents weekly COVID-19 hospitalization data and metrics aggregated to national, state/territory, and regional levels. COVID-19 hospitalization data are reported to CDC’s National Healthcare Safety Network, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN and included in this dataset represent aggregated counts and include metrics capturing information specific to COVID-19 hospital admissions, and inpatient and ICU bed capacity occupancy.

Reporting information:

• As of December 15, 2022, COVID-19 hospital data are required to be reported to NHSN, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Prior to December 15, 2022, hospitals reported data directly to the U.S. Department of Health and Human Services (HHS) or via a state submission for collection in the HHS Unified Hospital Data Surveillance System (UHDSS).
• While CDC reviews these data for errors and corrects those found, some reporting errors might still exist within the data. To minimize errors and inconsistencies in data reported, CDC removes outliers before calculating the metrics. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks.
• Many hospital subtypes, including acute care and critical access hospitals, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are included in the metric calculations provided in this report. Psychiatric, rehabilitation, and religious non-medical hospital types are excluded from calculations.
• Data are aggregated and displayed for hospitals with the same Centers for Medicare and Medicaid Services (CMS) Certification Number (CCN), which are assigned by CMS to counties based on the CMS Provider of Services files.
• Full details on COVID-19 hospital data reporting guidance can be found here: https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf

Metric details:

• Time Period: timeseries data will update weekly on Mondays as soon as they are reviewed and verified, usually before 8 pm ET. Updates will occur the following day when reporting coincides with a federal holiday. Note: Weekly updates might be delayed due to delays in reporting. All data are provisional. Because these provisional counts are subject to change, including updates to data reported previously, adjustments can occur. Data may be updated since original publication due to delays in reporting (to account for data received after a given Thursday publication) or data quality corrections.
• New COVID-19 Hospital Admissions (count): Number of new admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction.
• New COVID-19 Hospital Admissions (7-Day Average): 7-day average of new admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction.
• Cumulative COVID-19 Hospital Admissions: Cumulative total number of admissions of patients with laboratory-confirmed COVID-19 (including both adult and pediatric admissions) in the entire jurisdiction since August 1, 2020.
• Cumulative COVID-19 Hospital Admissions Rate: Cumulative total number of admissions of patients with laboratory-confirmed COVID-19 (including both adult and pediatric admissions) in the entire jurisdiction since August 1, 2020 divided by 2019 intercensal population estimate for that jurisdiction multiplied by 100,000.
• New COVID-19 Hospital Admissions Rate (7-day average) percent change from prior week: Percent change in the 7-day average new admissions of patients with laboratory-confirmed COVID-19 per 100,000 population compared with the prior week.
• New COVID-19 Hospital Admissions (7-Day Total): 7-day total number of new admissions of patients with laboratory-confirmed COVID-19 (including both adult and pediatric admissions) in the entire jurisdiction.
• New COVID-19 Hospital Admissions Rate (7-Day Total): 7-day total number of new admissions of patients with laboratory-confirmed COVID-19 (including both adult and pediatric admissions) for the entire jurisdiction divided by 2019 intercensal population estimate for that jurisdiction multiplied by 100,000.
• Total Hospitalized COVID-19 Patients: 7-day total number of patients currently hospitalized with laboratory-confirmed COVID-19 (including both adult and pediatric patients) for the entire jurisdiction.
• Total Hospitalized COVID-19 Patients (7-Day Average): 7-day average of the number of patients currently hospitalized with laboratory-confirmed COVID-19 (including both adult and pediatric patients) for the entire jurisdiction.
• COVID-19 Inpatient Bed Occupancy (7-Day Average): Percentage of all staffed inpatient beds occupied by patients with laboratory-confirmed COVID-19 (including both adult and pediatric patients) within the entire jurisdiction is calculated as an average of valid daily values within the past 7 days (e.g., if only three valid values, the average of those three is taken). Averages are separately calculated for the daily numerators (patients hospitalized with confirmed COVID-19) and denominators (staffed inpatient beds). The average percentage can then be taken as the ratio of these two values for the entire jurisdiction.
• COVID-19 Inpatient Bed Occupancy absolute change from prior week: The absolute change in the percent of staffed inpatient beds occupied by patients with laboratory-confirmed COVID-19 represents the week-over-week absolute difference between the 7-day average occupancy of patients with confirmed COVID-19 in staffed inpatient beds in the past 7 days, compared with the prior week, in the entire jurisdiction.
• COVID-19 ICU Bed Occupancy (7-Day Average): Percentage of all staffed inpatient beds occupied by adult patients with confirmed COVID-19 within the entire jurisdiction is calculated as a 7-day average of valid daily values within the past 7 days (e.g., if only three valid values, the average of those three is taken). Averages are separately calculated for the daily numerators (adult patients hospitalized with confirmed COVID-19) and denominators (staffed adult ICU beds). The average percentage can then be taken as the ratio of these two values for the entire jurisdiction.
• COVID-19 ICU Bed Occupancy absolute change from prior week: The absolute change in the percent of staffed ICU beds occupied by patients with laboratory-confirmed COVID-19 represents the week-over-week absolute difference between the average occupancy of patients with confirmed COVID-19 in staffed adult ICU beds for the past 7 days, compared with the prior week, in the in the entire jurisdiction.

Note: October 27, 2023: Due to a data processing error, reported values for avg_percent_inpatient_beds_occupied_covid_confirmed will appear lower than previously reported values by an average difference of less than 1%. Therefore, previously reported values for avg_percent_inpatient_beds_occupied_covid_confirmed may have been overestimated and should be interpreted with caution.

October 27, 2023: Due to a data processing error, reported values for abs_chg_avg_percent_inpatient_beds_occupied_covid_confirmed will differ from previously reported values by an average absolute difference of less than 1%. Therefore, previously reported values for abs_chg_avg_percent_inpatient_beds_occupied_covid_confirmed should be interpreted with caution.

December 29, 2023: Hospitalization data reported to CDC’s National Healthcare Safety Network (NHSN) through December 23, 2023, should be interpreted with caution due to potential reporting delays that are impacted by Christmas and New Years holidays. As a result, metrics including new hospital admissions for COVID-19 and influenza and hospital occupancy may be underestimated for the week ending December 23, 2023.

January 5, 2024: Hospitalization data reported to CDC’s National Healthcare Safety Network (NHSN) through December 30, 2023 should be interpreted with caution due to potential reporting delays that are impacted by Christmas and New Years holidays. As a result, metrics including new hospital admissions for COVID-19 and influenza and hospital occupancy may be underestimated for the week ending December 30, 2023.

Regarding all Vaccination Data The date of Last Update is 4/21/2023. Additionally on 4/27/2023 several COVID-19 datasets were retired and no longer included in public COVID-19 data dissemination. See this link for more information https://imap.maryland.gov/pages/covid-data Summary The cumulative number of COVID-19 vaccinations percent age group population: 16-17; 18-49; 50-64; 65 Plus. Description COVID-19 - Vaccination Percent Age Group Population data layer is a collection of COVID-19 vaccinations that have been reported each day into ImmuNet. COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county. Terms of Use The Spatial Data, and the information therein, (collectively the Data) is provided as is without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata. This map is for planning purposes only. MEMA does not guarantee the accuracy of any forecast or predictive elements.

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

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

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

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

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

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

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

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

Note: In these datasets, a person is defined as up to date if they have received at least one dose of an updated COVID-19 vaccine. The Centers for Disease Control and Prevention (CDC) recommends that certain groups, including adults ages 65 years and older, receive additional doses.

On 6/16/2023 CDPH replaced the booster measures with a new “Up to Date” measure based on CDC’s new recommendations, replacing the primary series, boosted, and bivalent booster metrics The definition of “primary series complete” has not changed and is based on previous recommendations that CDC has since simplified. A person cannot complete their primary series with a single dose of an updated vaccine. Whereas the booster measures were calculated using the eligible population as the denominator, the new up to date measure uses the total estimated population. Please note that the rates for some groups may change since the up to date measure is calculated differently than the previous booster and bivalent measures.

This data is from the same source as the Vaccine Progress Dashboard at https://covid19.ca.gov/vaccination-progress-data/ which summarizes vaccination data at the county level by county of residence. Where county of residence was not reported in a vaccination record, the county of provider that vaccinated the resident is included. This applies to less than 1% of vaccination records. The sum of county-level vaccinations does not equal statewide total vaccinations due to out-of-state residents vaccinated in California.

These data do not include doses administered by the following federal agencies who received vaccine allocated directly from CDC: Indian Health Service, Veterans Health Administration, Department of Defense, and the Federal Bureau of Prisons.

Totals for the Vaccine Progress Dashboard and this dataset may not match, as the Dashboard totals doses by Report Date and this dataset totals doses by Administration Date. Dose numbers may also change for a particular Administration Date as data is updated.

Previous updates:

• On March 3, 2023, with the release of HPI 3.0 in 2022, the previous equity scores have been updated to reflect more recent community survey information. This change represents an improvement to the way CDPH monitors health equity by using the latest and most accurate community data available. The HPI uses a collection of data sources and indicators to calculate a measure of community conditions ranging from the most to the least healthy based on economic, housing, and environmental measures.

• Starting on July 13, 2022, the denominator for calculating vaccine coverage has been changed from age 5+ to all ages to reflect new vaccine eligibility criteria. Previously the denominator was changed from age 16+ to age 12+ on May 18, 2021, then changed from age 12+ to age 5+ on November 10, 2021, to reflect previous changes in vaccine eligibility criteria. The previous datasets based on age 16+ and age 5+ denominators have been uploaded as archived tables.

• Starting on May 29, 2021 the methodology for calculating on-hand inventory in the shipped/delivered/on-hand dataset has changed. Please see the accompanying data dictionary for details. In addition, this dataset is now down to the ZIP code level.

**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 **

As of January 26, 2023, the population counts are based on Statistics Canada’s 2021 estimates. The coverage methodology has been revised to calculate age based on the current date and deceased individuals are no longer included. The method used to count daily dose administrations has changed is now based on the date delivered versus the day entered into the data system. Historical data has been updated.

Please note that Cases by Vaccination Status data will no longer be published as of June 30, 2022.

Please note that case rates by vaccination status and age group data will no longer be published as of July 13, 2022.

Please note that Hospitalization by Vaccination Status data will no longer be published as of June 30, 2022.

Learn more about COVID-19 vaccines.

Data includes:

• daily and total doses administered
• individuals with at least one dose
• individuals fully vaccinated
• total doses given to fully vaccinated individuals
• vaccinations by age
• percentage of age group
• individuals with at least one dose, by PHU, by age group
• individuals fully vaccinated, by PHU, by age group
• COVID-19 cases by status: not fully vaccinated, fully vaccinated, vaccinated with booster
• individuals in hospital due to COVID-19 (excluding ICU) by status: unvaccinated, partially vaccinated, fully vaccinated
• individuals in ICU due to COVID-19 by status: unvaccinated, partially vaccinated, fully vaccinated, unknown
• rate of COVID-19 cases per 100,000 by status and age group
• rate per 100,000 (7-day average) by status and age group

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

This dataset is subject to change.

Additional notes

• Data entry of vaccination records is still in progress, therefore the dosage data may not be a full representation of all vaccination doses administered in Ontario.
• The data does not include dosage data where consent was not provided for vaccination records to be entered into the provincial CoVax system. This includes individual records as well as records from some Indigenous communities where those communities have not consented to including vaccination information into CoVax.

Hospitalizations and cases by vaccination status

Hospitalizations

• This is a new data collection and the data quality will continue to improve as hospitals continue to submit data.
• In order to understand the vaccination status of patients currently hospitalized, a new data collection process was developed and this may cause discrepancies between other hospitalization numbers being collected using a different data collection process.
• Data on patients in ICU are being collected from two different data sources with different extraction times and public reporting cycles. The existing data source (Critical Care Information System, CCIS) does not have vaccination status.
• Historical data for hospitalizations by region may change over time as hospitals update previously entered data.
• Due to incomplete weekend and holiday reporting, vaccination status data for hospital and ICU admissions is not updated on Sundays, Mondays and the day after holidays
• Unvaccinated is defined as not having any dose, or between 0-13 days after administration of the first dose of a COVID-19 vaccine.
• Partially vaccinated is defined as 14 days or more after the first dose of a 2-dose series COVID-19 vaccine, or between 0-13 days after administration of the second dose
• Fully vaccinated is defined as 14 days or more after receipt of the second dose of a 2-dose series COVID-19 vaccine

Cases

• The cases by vaccination status may not match the daily COVID-19 case count because records with a missing or invalid health card number cannot be linked.

On October 20, 2022, CDC began retrieving aggregate case and death data from jurisdictional and state partners weekly instead of daily. This dataset contains archived community transmission and related data elements by county as originally displayed on the COVID Data Tracker. Although these data will continue to be publicly available, this dataset has not been updated since October 20, 2022. An archived dataset containing weekly community transmission data by county as originally posted can also be found here: Weekly COVID-19 County Level of Community Transmission as Originally Posted | Data | Centers for Disease Control and Prevention (cdc.gov).

Related data CDC has been providing the public with two versions of COVID-19 county-level community transmission level data: this dataset with the daily values as originally posted on the COVID Data Tracker, and an historical dataset with daily data as well as the updates and corrections from state and local health departments. Similar to this dataset, the original historical dataset is archived on 10/20/2022. It will continue to be publicly available but will no longer be updated. A new dataset containing historical community transmission data by county is now published weekly and can be found at: Weekly COVID-19 County Level of Community Transmission Historical Changes | Data | Centers for Disease Control and Prevention (cdc.gov).

This public use dataset has 7 data elements reflecting community transmission levels for all available counties and jurisdictions. It contains reported daily transmission levels at the county level with the same values used to display transmission maps on the COVID Data Tracker. Each day, the dataset is appended to contain the most recent day's data. Transmission level is set to low, moderate, substantial, or high using the calculation rules below.

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

CDC core metrics of and thresholds for community transmission levels of SARS-CoV-2

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

Test Percent Positivity Metric: "Percentage of positive NAAT in the past 7 days" is calculated by dividing the number of positive tests in the county (or other administrative level) during the last 7 days by the total number of tests conducted over the last 7 days. "Percentage of positive NAAT in the past 7 days" is considered to have a transmission level of Low (less than 5.00); Moderate (5.00-7.99); Substantial (8.00-9.99); and High (greater than or equal to 10.00).

If the two metrics suggest different transmission levels, the higher level is selected.

The reported transmission categories include:

Low Transmission Threshold: Counties with fewer than 10 total cases per 100,000 population in the past 7 days, and a NAAT percent test positivity in the past 7 days below 5%;

Moderate Transmission Threshold: Counties with 10-49 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 5.0-7.99%;

Substantial Transmission Threshold: Counties with 50-99 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 8.0-9.99%;

High Transmission Threshold: Counties with 100 or more total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 10.0% or greater.

Blank: total new cases in the past 7 days are not reported (county data known to be unavailable) and the percentage of positive NAATs tests during the past 7 days (blank) are not reported.

Data Suppression To prevent the release of data that could be used to identify people, data cells are suppressed for low frequency. When the case counts used to calculate the total new case rate metric ("cases_per_100K_7_day_count_change") is greater than zero and less than 10, this metric is set to "suppressed" to protect individual privacy. If the case count is 0, the total new case rate metric is still displayed.

The data in this dataset are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers. This dataset is created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access.

This data set is no longer being updated and is historical, last update 10/10/2022.Counts and percentages of confirmed and deceased COVID cases broken out by sex. In addition, counts and percentages of Jefferson county vaccine recipients broken out by gender, excluding doses administered by Walgreens and CVS clinics.

Fieldname

Description

sex

description of gender

unk_Gender

number of cases not identified as either male or female

population_percent

proportion of population in identified by gender to total population

by_gender

Number of confirmed cases identified by gender

total_confirmed

number of all confirmed cases to date

gender_percent

Proportion of confirmed cases identified by gender to total number of confirmed cases to date

by_gender_deceased

Number of deceased cased identified by gender

total_deceased

number of all deceased cases to date

deceased_percent

Proportion of deceased cases identified by gender to total number of deceased cases to date

by_gender_vaccinated

Number of Jefferson county residents that have received at least one vaccine dose identified by gender, excluding doses administered by Walgreens and CVS clinics

total_vaccinated

Total number of all first doses administered to Jefferson county residents, excluding doses administered by Walgreens and CVS clinics

vaccinated_percent

Proportion of Jefferson county vaccine recipients identified by gender to total number of Jefferson county vaccine recipients, excluding doses administered by Walgreens and CVS clinics

LOADED

Date the data was loaded into the system

Note: This data is preliminary, routinely updated, and is subject to change

For questions about this data please contact Angela Graham (Angela.Graham@louisvilleky.gov) or YuTing Chen (YuTing.Chen@louisvilleky.gov) or call (502) 574-8279.

CDC COVID-19 Vaccine Tracker

Cumulative and Daily Counts of COVID-19 Vaccine Doses in the United States

By Nicky Forster [source]

About this dataset

The dataset contains data points such as the cumulative count of people who have received at least one dose of the vaccine, new doses administered on a specific date, cumulative count of doses distributed in the country, percentage of population that has completed the full vaccine series, cumulative count of Pfizer and Moderna vaccine doses administered in each state, seven-day rolling averages for new doses administered and distributed, among others.

It also provides insights into the vaccination status at both national and state levels. The dataset includes information on the percentage of population that has received at least one dose of the vaccine, percentage of population that has completed the full vaccine series, cumulative counts per 100k population for both distributed and administered doses.

Additionally, it presents data specific to each state, including their abbreviation and name. It outlines details such as cumulative counts per 100k population for both distributed and administered doses in each state. Furthermore, it indicates if there were instances where corrections resulted in single-day negative counts.

The dataset is compiled from daily snapshots obtained from CDC's COVID Data Tracker. Please note that there may be reporting delays by healthcare providers up to 72 hours after administering a dose.

This comprehensive dataset serves various purposes including tracking vaccination progress over time across different locations within the United States. It can be used by researchers, policymakers or anyone interested in analyzing trends related to COVID-19 vaccination efforts at both national and state levels

How to use the dataset

• Familiarize Yourself with the Columns: Take a look at the available columns in this dataset to understand what information is included. These columns provide details such as state abbreviations, state names, dates of data snapshots, cumulative counts of doses distributed and administered, people who have received at least one dose or completed the vaccine series, percentages of population coverage, manufacturer-specific data, and seven-day rolling averages.

• Explore Cumulative Counts: The dataset includes cumulative counts that show the total number of doses distributed or administered over time. You can analyze these numbers to track trends in vaccination progress in different states or regions.

• Analyze Daily Counts: The dataset also provides daily counts of new vaccine doses distributed and administered on specific dates. By examining these numbers, you can gain insights into vaccination rates on a day-to-day basis.

• Study Population Coverage Metrics: Metrics such as pct_population_received_at_least_one_dose and pct_population_series_complete give you an understanding of how much of each state's population has received at least one dose or completed their vaccine series respectively.

• Utilize Manufacturer Data: The columns related to Pfizer and Moderna provide information about the number of doses administered for each manufacturer separately. By analyzing this data, you can compare vaccination rates between different vaccines.

• Consider Rolling Averages: The seven-day rolling average columns allow you to smooth out fluctuations in daily counts by calculating an average over a week's time window. This can help identify long-term trends more accurately.

• Compare States: You can compare vaccination progress between different states by filtering the dataset based on state names or abbreviations. This way, you can observe variations in distribution and administration rates among different regions.

• Visualize the Data: Creating charts and graphs will help you visualize the data more effectively. Plotting trends over time or comparing different metrics for various states can provide powerful visual representations of vaccination progress.

• Stay Informed: Keep in mind that this dataset is continuously updated as new data becomes available. Make sure to check for any updates or refreshed datasets to obtain the most recent information on COVID-19 vaccine distributions and administrations

Research Ideas

• Vaccination Analysis: This dataset can be used to analyze the progress of COVID-19 vaccinations in the United States. By examining the cumulative counts of doses distributed and administered, as well as the number of people who have received at least one dose or completed the vaccine series, researchers and policymakers can assess how effectively vaccines are being rolled out and monitor...

Context

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

Content

Dataset Attribute Details:

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

On October 20, 2022, CDC began retrieving aggregate case and death data from jurisdictional and state partners weekly instead of daily. This dataset contains archived historical community transmission and related data elements by county. Although these data will continue to be publicly available, this dataset has not been updated since October 20, 2022. An archived dataset containing weekly historical community transmission data by county can also be found here: Weekly COVID-19 County Level of Community Transmission Historical Changes | Data | Centers for Disease Control and Prevention (cdc.gov).

Related data CDC has been providing the public with two versions of COVID-19 county-level community transmission level data: this historical dataset with the daily county-level transmission data from January 22, 2020, and a dataset with the daily values as originally posted on the COVID Data Tracker. Similar to this dataset, the original dataset with daily data as posted is archived on 10/20/2022. It will continue to be publicly available but will no longer be updated. A new dataset containing community transmission data by county as originally posted is now published weekly and can be found at: Weekly COVID-19 County Level of Community Transmission as Originally Posted | Data | Centers for Disease Control and Prevention (cdc.gov).

This public use dataset has 7 data elements reflecting historical data for community transmission levels for all available counties and jurisdictions. It contains historical data for the county level of community transmission and includes updated data submitted by states and jurisdictions. Each day, the dataset was updated to include the most recent days’ data and incorporate any historical changes made by jurisdictions. This dataset includes data since January 22, 2020. Transmission level is set to low, moderate, substantial, or high using the calculation rules below.

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

CDC core metrics of and thresholds for community transmission levels of SARS-CoV-2

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

Test Percent Positivity Metric: "Percentage of positive NAAT in the past 7 days" is calculated by dividing the number of positive tests in the county (or other administrative level) during the last 7 days by the total number of tests resulted over the last 7 days. "Percentage of positive NAAT in the past 7 days" is considered to have transmission level of Low (less than 5.00); Moderate (5.00-7.99); Substantial (8.00-9.99); and High (greater than or equal to 10.00).

If the two metrics suggest different transmission levels, the higher level is selected. If one metric is missing, the other metric is used for the indicator.

The reported transmission categories include:

Low Transmission Threshold: Counties with fewer than 10 total cases per 100,000 population in the past 7 days, and a NAAT percent test positivity in the past 7 days below 5%;

Moderate Transmission Threshold: Counties with 10-49 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 5.0-7.99%;

Substantial Transmission Threshold: Counties with 50-99 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 8.0-9.99%;

High Transmission Threshold: Counties with 100 or more total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 10.0% or greater.

Blank: total new cases in the past 7 days are not reported (county data known to be unavailable) and the percentage of positive NAATs tests during the past 7 days (blank) are not reported.

Data Suppression To prevent the release of data that could be used to identify people, data cells are suppressed for low frequency. When the case counts used to calculate the total new case rate metric ("cases_per_100K_7_day_count_change") is greater than zero and less than 10, this metric is set to "suppressed" to protect individual privacy. If the case count is 0, the total new case rate metric is still displayed.

The data in this dataset are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers. This datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access.

Duplicate Records Issue A bug was found on 12/28/2021 that caused many records in the dataset to be duplicated. This issue was resolved on 01/06/2022.

Data exploration, cleaning, and arrangement with Covid Death and Covid Vaccination which is involved:

1. Data that going to be using

2. Shows the likelihood of dying if you contract covid in your country

3. Show what percentage of the population got Covid

4. Looking at Countries with the Highest Infection Rate compared to the Population

5. Showing the Country with the Highest Death Count per Population

6. Break things down by continent

7. Continents with the Highest death count per population

8. Looking at Total Population vs Vaccinations

9. Used CTE and Temp Table

10. Creating View to store data for later visualizations

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

Only Chicago residents are included based on the home ZIP Code as provided by the medical provider. If a ZIP was missing or was not valid, it is displayed as "Unknown".

Cases with a positive molecular (PCR) or antigen test are included in this dataset. Cases are counted based on the week the test specimen was collected. For privacy reasons, until a ZIP Code reaches five cumulative cases, both the weekly and cumulative case counts will be blank. Therefore, summing the “Cases - Weekly” column is not a reliable way to determine case totals. Deaths are those that have occurred among cases based on the week of death.

For tests, each test is counted once, based on the week the test specimen was collected. Tests performed prior to 3/1/2020 are not included. Test counts include multiple tests for the same person (a change made on 10/29/2020). PCR and antigen tests reported to Chicago Department of Public Health (CDPH) through electronic lab reporting are included. Electronic lab reporting has taken time to onboard and testing availability has shifted over time, so these counts are likely an underestimate of community infection.

The “Percent Tested Positive” columns are calculated by dividing the number of positive tests by the number of total tests . Because of the data limitations for the Tests columns, such as persons being tested multiple times as a requirement for employment, these percentages may vary in either direction from the actual disease prevalence in the ZIP Code.