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 late January, 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As of 10/22/2020, this dataset is no longer being updated and has been replaced with a new dataset, which can be accessed here: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 The 7-day metrics are no longer being updated as of 10/15/2020 but the archived dataset can be accessed here: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).

In a context that is more uncertain than ever and where restrictions have a great impact on the daily lives of restaurants, a new restaurant model is imposed. This model consists of online orders and leads restaurant managers to question the measures they would take, in case the COVID-19 crisis continues. Thus, we observe that in the perspective of a long-term crisis, ** percent of restaurant owners would continue with online delivery, while ** percent would close their establishment temporarily.

Meal deliveries before and during the COVID-19 crisis

According to data provided by YouGov France, ** percent of online meal delivery orders and takeaway clients were aged between 18 and 34. Thus, for students, the main reason to order online was to treat themselves from time to time, as well as to save time. Since the sanitary crisis, the number of online delivery orders has increased significantly on a European level as well as for France, reaching a growth of ** percent in 2020. With McDonald's at the top of the ranking for the most mentioned brands on social media, it was Uber Eats who marked the podium, with ** percent of orders made from the platform. And, even though the French showed their support for fast food chains by ordering burgers, pizzas remained at the top of delivered culinary experiences.

The future of online delivery after the crisis

After the end of the first confinement period, the percentage of people ordering online as "a little treat" had decreased by **** percent. However, acknowledging the comfort of having ready cooked meals delivered to the doorstep, ** percent of French people said that they would increase their online orders, spending on average ** to ** euros per orders. This model seems to be lucrative for people wanting to support local businesses who had to shut down their physical activity, even after lifting the lockdown. For those businesses who had been offering delivery services and for those who have started offering these services since the crisis, both are recognizing the prevailing trend of online delivery, even after the crisis.

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 into age groups in Jefferson County, KY. In addition, counts and percentages of Jefferson county vaccine recipients broken out into age groups, excluding doses administered by Walgreens and CVS clinics.

Fieldname

Definition

age_group

the lowest limit of the age group (ex. 0 represent 0 up to 5 and 5 represents 5 up to 10)

population_percent

proportion of population in the age group to total population

age_count

number of confirmed cases in the age group

total_Confirmed

number of all confirmed cases to date

age_group_percent

proportion of confirmed cases in the age group to total number of confirmed cases to date

age_group_deceased

number of deceased in the age group

total_deceased

number of all deceased cases to date

deceased_percent

proportion of deceased in the age group to total number of deceased to date

age_group_vaccinated

Number of Jefferson county residents that have received at least one vaccine dose identified by age group, 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 age group 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.

COVID-19 Stats & Trends

Context

This dataset seeks to provide insights into what has changed due to policies aimed at combating COVID-19 and evaluate the changes in community activities and its relation to reduced confirmed cases of COVID-19. The reports chart movement trends, compared to an expected baseline, over time (from 2020/02/15 to 2020/02/05) by geography (across 133 countries), as well as some other stats about the country that might help explain the evolution of the disease.

Content

1. Grocery & Pharmacy: Mobility trends for places like grocery markets, food warehouses, farmers' markets, specialty food shops, drug stores, and pharmacies.
2. Parks: Mobility trends for places like national parks, public beaches, marinas, dog parks, plazas, and public gardens.
3. Residential: Mobility trends for places of residence.
4. Retail & Recreation: Mobility trends for places like restaurants, cafes, shopping centers, theme parks, museums, libraries, and movie theaters.
5. Transit stations: Mobility trends for places like public transport hubs such as subway, bus, and train stations.
6. Workplaces: Mobility trends for places of work.
7. Total Cases: Total number of people infected with the SARS-CoV-2.
8. Fatalities: Total number of deaths caused by CoV-19.
9. Government Response Stringency Index: Additive score of nine indicators of government response to CoV-19: School closures, workplace closures, cancellation of public events, public information campaigns, stay at home policies, restrictions on internal movement, international travel controls, testing policy, and contact tracing.
10. COVID-19 Testing: Total number of tests performed.
11. Total Vaccinations: Total number of shots given.
12. Total People Vaccinated: Total number of people given a shot.
13. Total People Fully Vaccinated: Total number of people fully vaccinated (might require two shots of some vaccines).
14. Population: Total number of inhabitants.
15. Population Density per km2: Number of human inhabitants per square kilometer.
16. Health System Index: Overall performance of the health system.
17. Human Development Index (HDI): Summary index based on life expectancy at birth, expected years of schooling for children and mean years of schooling for adults, and GNI per capita.
18. GDP (PPP) per capita: Gross Domestic Product (GDP) per capita based on Purchasing Power Parity (PPP), taking into account the relative cost of local goods, services and inflation rates of the country, rather than using international market exchange rates, which may distort the real differences in per capita income.
19. Elderly Population (percentage): Percentage of the population above the age of 65 years old.

References & Acknowledgements

Bing COVID-19 data. Available at: https://github.com/microsoft/Bing-COVID-19-Data COVID-19 Community Mobility Report. Available at: https://www.google.com/covid19/mobility/ COVID-19: Government Response Stringency Index. Available at: https://ourworldindata.org/grapher/covid-stringency-index Coronavirus (COVID-19) Testing. Available at: https://github.com/owid/covid-19-data/blob/master/public/data/testing/covid-testing-all-observations.csv Coronavirus (COVID-19) Vaccination. Available at: https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/vaccinations/vaccinations.csv List of countries and dependencies by population. Available at: https://www.kaggle.com/tanuprabhu/population-by-country-2020 List of countries and dependencies by population density. Available at: https://www.kaggle.com/tanuprabhu/population-by-country-2020 List of countries by Human Development Index. Available at: http://hdr.undp.org/en/data Measuring Overall Health System Performance. Available at: https://www.who.int/healthinfo/paper30.pdf?ua=1 List of countries by GDP (PPP) per capita. Available at: https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD List of countries by age structure (65+). Available at: https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS

Authors

• Diogo Silva, up201706892@fe.up.pt

Reporting of Aggregate Case and Death Count data was discontinued on 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.

The surveillance case definition for COVID-19, a nationally notifiable disease, was first described in a position statement from the Council for State and Territorial Epidemiologists, which was later revised. However, there is some variation in how jurisdictions implemented these case definitions. More information on how CDC collects COVID-19 case surveillance data can be found at FAQ: COVID-19 Data and Surveillance.

Aggregate Data Collection Process Since the beginning of the COVID-19 pandemic, data were reported from state and local health departments through a robust process with the following steps:

• Aggregate county-level counts were obtained indirectly, via automated overnight web collection, or directly, via a data submission process.
• If more than one official county data source existed, CDC used a comprehensive data selection process comparing each official county data source to retrieve the highest case and death counts, unless otherwise specified by the state.
• A CDC data team reviewed counts for congruency prior to integration and set up alerts to monitor for discrepancies in the data.
• CDC routinely compiled these data and post the finalized information on COVID Data Tracker.
• County level data were aggregated to obtain state- and territory- specific totals.
• Counting of cases and deaths is based on date of report and not on the date of symptom onset. CDC calculates rates in these data by using population estimates provided by the US Census Bureau Population Estimates Program (2019 Vintage).
• COVID-19 aggregate case and death data are organized in a time series that includes cumulative number of cases and deaths as reported by a jurisdiction on a given date. New case and death counts are calculated as the week-to-week change in cumulative counts of cases and deaths reported (i.e., newly reported cases and deaths = cumulative number of cases/deaths reported this week minus the cumulative total reported the prior week.

This process was collaborative, with CDC and jurisdictions working together to ensure the accuracy of COVID-19 case and death numbers. County counts provided the most up-to-date numbers on cases and deaths by report date. Throughout data collection, CDC retrospectively updated counts to correct known data quality issues.

Description This archived public use dataset focuses on the cumulative and weekly case and death rates per 100,000 persons within various sociodemographic factors across all states and their counties. All resulting data are expressed as rates calculated as the number of cases or deaths per 100,000 persons in counties meeting various classification criteria using the US Census Bureau Population Estimates Program (2019 Vintage).

Each county within jurisdictions is classified into multiple categories for each factor. All rates in this dataset are based on classification of counties by the characteristics of their population, not individual-level factors. This applies to each of the available factors observed in this dataset. Specific factors and their corresponding categories are detailed below.

Population-level factors Each unique population factor is detailed below. Please note that the “Classification” column describes each of the 12 factors in the dataset, including a data dict

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 into age groups for the last 8 weeks. Fieldname Definition age_group the lowest limit of the age group (ex. 0 represent 0 up to 5 and 5 represents 5 up to 10) population_percent proportion of population in the age group to total population age_count number of confirmed cases in the age group total_Confirmed number of all confirmed cases where the form received date is within the reporting period age_group_percent proportion of confirmed cases in the age group to total number of confirmed cases to date age_group_deceased number of deceased in the age group total_deceased number of deceased where the date of death is within the reporting period deceased_percent proportion of deceased in the age group to total number of deceased to date REPORT_BEGIN_DATE The date calculated as 8 weeks before the report end date. The beginning date of the date range of data aggregated. REPORT_END_DATE The end date of the reporting period. Loaded Date the data was loaded into the system Note: This data is preliminary, routinely updated, and is subject to changeFor questions about this data please contact Angela Graham (Angela.Graham@louisvilleky.gov) or YuTing Chen (YuTing.Chen@louisvilleky.gov) or call (502) 574-8279.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

Between 2019 and 2020, the coronavirus (COVID-19) pandemic led to a growth in the use of digital restaurant delivery services. Digital delivery services are defined as meals or snacks ordered via mobile app, internet, or text message. As result of the pandemic, the share of consumer restaurant spending spent on takeout and delivery in the United States reached ** percent in 2020.

How did COVID-19 impact online food delivery in the U.S.?

Due to the pandemic, and a subsequent lack of in-house dining, digital restaurant food orders in the U.S. increased between March 2020 and March 2021. Delivery orders in particular increased by *** percent, while carry-out orders increased by *** percent. During that same year, the distribution of digital restaurant food orders in the U.S. was such that carry-out represented ** percent of all digital orders and delivery represented ** percent of all digital orders.

How big is the online restaurant delivery market worldwide?

In 2019, the global market size of the online food delivery sector reached ****** billion U.S. dollars. This figure was forecast to rise to as much as ****** billion U.S. dollars by 2023. The sector became especially relevant during the COVID-19 pandemic, where social distancing and hygiene measures caused many restaurants to close their doors to the public. At the start of 2020, there was a dramatic decline in sit-down dining worldwide However, this did not continue throughout the year, with the number of seated diners fluctuating depending on regulations and COVID-19 case numbers.

As of November 11, 2022, almost 96.8 million confirmed cases of COVID-19 had been reported by the World Health Organization (WHO) for the United States. The pandemic has impacted all 50 states, with vast numbers of cases recorded in California, Texas, and Florida.

The coronavirus in the U.S. The coronavirus hit the United States in mid-March 2020, and cases started to soar at an alarming rate. The country has performed a high number of COVID-19 tests, which is a necessary step to manage the outbreak, but new coronavirus cases in the U.S. have spiked several times since the pandemic began, most notably at the end of 2022. However, restrictions in many states have been eased as new cases have declined.

The origin of the coronavirus In December 2019, officials in Wuhan, China, were the first to report cases of pneumonia with an unknown cause. A new human coronavirus – SARS-CoV-2 – has since been discovered, and COVID-19 is the infectious disease it causes. All available evidence to date suggests that COVID-19 is a zoonotic disease, which means it can spread from animals to humans. The WHO says transmission is likely to have happened through an animal that is handled by humans. Researchers do not support the theory that the virus was developed in a laboratory.

Motivation

Why did I create this dataset? This is my first time creating a notebook in Kaggle and I am interested in learning more about COVID-19 and how different countries are affected by it and why. It might be useful to compare different metrics between different countries. And I also wanted to participate in a challenge, and I've decided to join the COVID-19 datasets challenge. While looking through the projects, I noticed https://www.kaggle.com/koryto/countryinfo and it inspired me to start this project.

Method

My approach is to scour the Internet and Kaggle looking for country data that can potentially have an impact on how the COVID-19 pandemic spreads. In the end, I ended up with the following for each country:

• Monthly temperature and precipitation from Worldbank
• Latitude and longitude
• Population, density, gender and age
• Airport traffic from Worldbank
• COVID-19 date of first case and number of cases and deaths as of March 26, 2020
• 2009 H1N1 flu pandemic cases and deaths obtained from Wikipedia
• Property affordability index and Health care index from Numbeo
• Number of hospital beds and ICU beds from Wikipedia
• Flu and pneumonia death rate from Worldlifeexpectancy.com (Age Adjusted Death Rate Estimates: 2017)
• School closures due to COVID-19
• Number of COVID-19 tests done
• Number of COVID-19 genetic strains
• US Social Distancing Policies from COVID19StatePolicy’s SocialDistancing repository on GitHub
• DHL Global Connectedness Index 2018 (People Breadth scores)
• Datasets have been merged by country name whenever possible. I needed to rename some countries by hand, e.g. US to United Sates, etc. but it's possible that I might have missed some. See the output file covid19_merged.csv for the merged result.

See covid19_data - data_sources.csv for data source details.

Notebook: https://www.kaggle.com/bitsnpieces/covid19-data

Caveats

Since I did not personally collect each datapoint, and because each datasource is different with different objectives, collected at different times, measured in different ways, any inferences from this dataset will need further investigation.

Other interesting sources of information

• IMF Policy Tracker
• nCov strain analysis across the globe!
• Google Mobility Report
• Oxford government response tracker
• Impact on aviation
• Impact on restaurants
• Harvard
• Tableau datahub
• Unemployment outlook from ILO

Acknowledgements

I want to acknowledge the authors of the datasets that made their data publicly available which has made this project possible. Banner image is by Brian.

I hope that the community finds this dataset useful. Feel free to recommend other datasets that you think will be useful / relevant! Thanks for looking.

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.

Between 2019 and 2020, the coronavirus (COVID-19) pandemic led to a growth in the use of digital restaurant delivery services across ** countries worldwide. Digital delivery services are defined as meals or snacks ordered via mobile app, internet, or text message. In total, digital restaurant delivery increased ** percent globally, with the United States increasing the most at *** percent. Online restaurant delivery market worldwide In 2019, the market size of the global online food delivery sector reached ****** billion U.S. dollars. This figure was forecast to rise to as much as ****** billion by 2023. The sector became especially relevant during the coronavirus (COVID-19) pandemic, where social distancing and hygiene measures caused many restaurants to have to close their doors to the public. At the start of 2020, there was a dramatic decline in sit-down dining worldwide. However, this did not continue throughout the year, with the number of seated diners fluctuating depending on regulations and COVID-19 case numbers. Impact of COVID-19 on U.S. online food delivery The United States saw an increase in digital restaurant food orders between March 2020 and March 2021. Delivery orders increased by *** percent, while carry-out orders increased by *** percent. During that same year, the distribution of digital restaurant food orders was such that carry-out represented ** percent of all digital orders and delivery represented ** percent of all digital orders.

In the state of New York, Richmond and Rockland have the highest coronavirus case rates when adjusted for the population of a county. Rockland County had around 1,404 positive cases per 10,000 people as of April 19, 2021.

The five boroughs of NYC With around 894,400 positive infections as of mid-April 2021, New York City has the highest number of coronavirus cases in New York State – this means that there were approximately 1,065 cases per 10,000 people. New York City is composed of five boroughs; each borough is coextensive with a county of New York State. Staten Island is the smallest in terms of population, but it is the borough with the highest rate of COVID-19 cases.

Public warned against complacency The number of new COVID-19 cases in New York City spiked for the second time as the winter holiday season led to an increase in social gatherings. New York State is slowly recovering – indoor dining reopened in February 2021 – but now is not the time for people to become complacent. Despite the positive rollout of vaccines, experts have urged citizens to adhere to guidelines and warned that face masks might have to be worn for at least another year.

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.

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

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.

Description:

This comprehensive dataset provides global information on both COVID-19 related deaths and vaccinations from January 5, 2020, to August 4, 2024. It consists of two parts: one tracking COVID-19 cases, deaths, and population statistics, and another monitoring vaccination progress worldwide. This dataset allows for an in-depth analysis of the pandemic’s spread, fatality rates, and the effectiveness of vaccination campaigns across various countries and regions.

Researchers and data analysts can use this dataset to study trends, compare countries, and evaluate public health responses throughout the COVID-19 pandemic.

Includes:

CovidDeaths Dataset: Records of total cases, deaths, and population.

CovidVaccinations Dataset: Records of daily vaccination counts and cumulative totals.

Use Cases:

Analyzing death rates relative to confirmed cases. Examining the percentage of population affected by COVID-19. Evaluating vaccination rates and coverage across different regions. This dataset is ideal for data exploration, statistical analysis, and visualizations related to the COVID-19 pandemic.

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

This dataset is a companion to the COVID-19 Daily Cases and Deaths dataset (https://data.cityofchicago.org/d/naz8-j4nc). The major difference in this dataset is that the case, death, and hospitalization corresponding rates per 100,000 population are not those for the single date indicated. They are rolling averages for the seven-day period ending on that date. This rolling average is used to account for fluctuations that may occur in the data, such as fewer cases being reported on weekends, and small numbers. The intent is to give a more representative view of the ongoing COVID-19 experience, less affected by what is essentially noise in the data.

All rates are per 100,000 population in the indicated group, or Chicago, as a whole, for “Total” columns.

Only Chicago residents are included based on the home address as provided by the medical provider.

Cases with a positive molecular (PCR) or antigen test are included in this dataset. Cases are counted based on the date the test specimen was collected. Deaths among cases are aggregated by day of death. Hospitalizations are reported by date of first hospital admission. Demographic data are based on what is reported by medical providers or collected by CDPH during follow-up investigation.

Denominators are from the U.S. Census Bureau American Community Survey 1-year estimate for 2018 and can be seen in the Citywide, 2018 row of the Chicago Population Counts dataset (https://data.cityofchicago.org/d/85cm-7uqa).

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. At any given time, this dataset reflects cases and deaths currently known to CDPH.

Numbers in this dataset may differ from other public sources due to definitions of COVID-19-related cases and deaths, sources used, how cases and deaths are associated to a specific date, and similar factors.

Data Source: Illinois National Electronic Disease Surveillance System, Cook County Medical Examiner’s Office, U.S. Census Bureau American Community Survey