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

2019 Novel Coronavirus COVID-19 (2019-nCoV) Visual Dashboard and Map:
https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

• Confirmed Cases by Country/Region/Sovereignty
• Confirmed Cases by Province/State/Dependency
• Deaths
• Recovered

Downloadable data:
https://github.com/CSSEGISandData/COVID-19

Additional Information about the Visual Dashboard:
https://systems.jhu.edu/research/public-health/ncov

Global Map of Coronavirus cases

This feature layer contains the most up-to-date COVID-19 cases and latest trend plot. It covers China (in province level), the US, Canada, Australia (in city level), and the rest of the world (in country level).Data sources are WHO, US CDC, China NHC, ECDC, and DXY. The China data is automatically updating at least once per hour, and non China data is updating manually. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. The source feature layer is supported by Esri Living Atlas team and JHU Data Services. This layer is opened to the public and free to share. Contact us.

Context

The United States have recently become the country with the most reported cases of 2019 Novel Coronavirus (COVID-19). This dataset contains daily updated number of reported cases & deaths in the US on the state and county level, as provided by the Johns Hopkins University. In addition, I provide matching demographic information for US counties.

Content

The dataset consists of two main csv files: covid_us_county.csv and us_county.csv. See the column descriptions below for more detailed information. In addition, I've added US county shape files for geospatial plots: us_county.shp/dbf/prj/shx.

• covid_us_county.csv: COVID-19 cases and deaths which will be updated daily. The data is provided by the Johns Hopkins University through their excellent github repo. I combined the separate "confirmed cases" and "deaths" files into a single table, removed a few (I think to be) redundant geo identifier columns, and reshaped the data into long format with a single date column. The earliest recorded cases are from 2020-01-22.

• us_counties.csv: Demographic information on the US county level based on the (most recent) 2014-18 release of the Amercian Community Survey. Derived via the great tidycensus package.

Column Description

COVID-19 dataset covid_us_county.csv:

• fips: County code in numeric format (i.e. no leading zeros). A small number of cases have NA values here, but can still be used for state-wise aggregation. Currently, this only affect the states of Massachusetts and Missouri.

• county: Name of the US county. This is NA for the (aggregated counts of the) territories of American Samoa, Guam, Northern Mariana Islands, Puerto Rico, and Virgin Islands.

• state: Name of US state or territory.

• state_code: Two letter abbreviation of US state (e.g. "CA" for "California"). This feature has NA values for the territories listed above.

• lat and long: coordinates of the county or territory.

• date: Reporting date.

• cases & deaths: Cumulative numbers for cases & deaths.

Demographic dataset us_counties.csv:

• fips, county, state, state_code: same as above. The county names are slightly different, but mostly the difference is that this dataset has the word "County" added. I recommend to join on fips.

• male & female: Population numbers for male and female.

• population: Total population for the county. Provided as convenience feature; is always the sum of male + female.

• female_percentage: Another convenience feature: female / population in percent.

• median_age: Overall median age for the county.

Acknowledgements

Data provided for educational and academic research purposes by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE).

Licence

The github repo states that:

This GitHub repo and its contents herein, including all data, mapping, and analysis, copyright 2020 Johns Hopkins University, all rights reserved, is provided to the public strictly for educational and academic research purposes. The Website relies upon publicly available data from multiple sources, that do not always agree. The Johns Hopkins University hereby disclaims any and all representations and warranties with respect to the Website, including accuracy, fitness for use, and merchantability. Reliance on the Website for medical guidance or use of the Website in commerce is strictly prohibited.

Version history

• In version 1, a small number of cases had values of `county == "Unassigned". Those have been superseded.
• Version 5: added US county shape files

Overview

This is the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). This database was created in response to the Coronavirus public health emergency to track reported cases in real-time. The data include the location and number of confirmed COVID-19 cases, deaths and recoveries for all affected countries, aggregated at the appropriate province or state. It was developed to enable researchers, public health authorities and the general public to track the outbreak as it unfolds. Additional information is available in the blog post, Mapping 2019-nCoV (https://systems.jhu.edu/research/public-health/ncov/), and included data sources are listed here: https://github.com/CSSEGISandData/COVID-19

Sample Query 1

How many confirmed COVID-19 cases were there in the US, by state? This query determines the total number of cases by province in February. A "province_state" can refer to any subset of the US in this particular dataset, including a county or state. SELECT province_state, confirmed AS feb_confirmed_cases, FROM bigquery-public-data.covid19_jhu_csse.summary WHERE country_region = "US" AND date = '2020-02-29' ORDER BY feb_confirmed_cases desc

Sample Query 2

Which countries with the highest number of confirmed cases have the most per capita? This query joins the Johns Hopkins dataset with the World Bank's global population data to determine which countries among those with the highest total number of confirmed cases have the most confirmed cases per capita.

with country_pop AS( SELECT IF(country = "United States","US",IF(country="Iran, Islamic Rep.","Iran",country)) AS country, year_2018 FROM bigquery-public-data.world_bank_global_population.population_by_country)

SELECT cases.date AS date, cases.country_region AS country_region, SUM(cases.confirmed) AS total_confirmed_cases, SUM(cases.confirmed)/AVG(country_pop.year_2018) * 100000 AS confirmed_cases_per_100000 FROM bigquery-public-data.covid19_jhu_csse.summary cases JOIN country_pop ON cases.country_region LIKE CONCAT('%',country_pop.country,'%') WHERE cases.country_region = "US" AND country_pop.country = "US" AND cases.date = DATE_SUB(current_date(),INTERVAL 1 day) GROUP BY country_region, date

UNION ALL

SELECT cases.date AS date, cases.country_region AS country_region, SUM(cases.confirmed) AS total_confirmed_cases, SUM(cases.confirmed)/AVG(country_pop.year_2018) * 100000 AS confirmed_cases_per_100000 FROM bigquery-public-data.covid19_jhu_csse.summary cases JOIN country_pop ON cases.country_region LIKE CONCAT('%',country_pop.country,'%') WHERE cases.country_region = "France" AND country_pop.country = "France" AND cases.date = DATE_SUB(current_date(),INTERVAL 1 day) GROUP BY country_region, date

UNION ALL

SELECT cases.date AS date, cases.country_region AS country_region, SUM(cases.confirmed) AS total_confirmed_cases, SUM(cases.confirmed)/AVG(country_pop.year_2018) * 100000 AS confirmed_cases_per_100000 FROM bigquery-public-data.covid19_jhu_csse.summary cases JOIN country_pop ON cases.country_region LIKE CONCAT('%',country_pop.country,'%') WHERE cases.country_region = "China" AND country_pop.country = "China" AND cases.date = DATE_SUB(current_date(),INTERVAL 1 day)

GROUP BY country_region, date

UNION ALL

SELECT cases.date AS date, cases.country_region AS country_region, cases.confirmed AS total_confirmed_cases, cases.confirmed/country_pop.year_2018 * 100000 AS confirmed_cases_per_100000 FROM bigquery-public-data.covid19_jhu_csse.summary cases JOIN country_pop ON cases.country_region LIKE CONCAT('%',country_pop.country,'%') WHERE cases.country_region IN ("Italy", "Spain", "Germany", "Iran") AND cases.date = DATE_SUB(current_date(),INTERVAL 1 day) ORDER BY confirmed_cases_per_100000 desc

Dataset source

JHU CSSE

Update frequency

Daily

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit the following sources:Global: World Health Organization (WHO)U.S.: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.This feature layer contains the most up-to-date COVID-19 cases and latest trend plot. It covers China, Canada, Australia (at province/state level), and the rest of the world (at country level, represented by either the country centroids or their capitals)and the US at county-level. Data sources: WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, BNO, state and national government health departments, and local media reports. . The China data is automatically updating at least once per hour, and non-China data is updating hourly. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This feature layer is supported by Esri Living Atlas team and JHU Data Services. This layer is opened to the public and free to share. Contact us.

Context

This is the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Also, Supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL).

Data Source

• World Health Organization (WHO): https://www.who.int/
• DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia.
• BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/
• National Health Commission of the People’s Republic of China (NHC):
• http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml
• China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm
• Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html
• Macau Government: https://www.ssm.gov.mo/portal/
• Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0
• US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html
• Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html
• Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance
• European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases
• Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19
• Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus
• 1Point3Arces: https://coronavirus.1point3acres.com/en
• WorldoMeters: https://www.worldometers.info/coronavirus/
• COVID Tracking Project: https://covidtracking.com/data. (US Testing and Hospitalization Data. We use the maximum reported value from "Currently" and - - - "Cumulative" Hospitalized for our hospitalization number reported for each state.)
• French Government: https://dashboard.covid19.data.gouv.fr/

Terms of Use

This GitHub repo and its contents herein, including all data, mapping, and analysis, copyright 2020 Johns Hopkins University, all rights reserved, is provided to the public strictly for educational and academic research purposes. The Website relies upon publicly available data from multiple sources, that do not always agree. The Johns Hopkins University hereby disclaims any and all representations and warranties with respect to the Website, including accuracy, fitness for use, and merchantability. Reliance on the Website for medical guidance or use of the Website in commerce is strictly prohibited.

Content

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.

Acknowledgements

John Hopkins University The Associated Press

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: World Health Organization (WHO)For more information, visit the Johns Hopkins Coronavirus Resource Center.COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.DOI: https://doi.org/10.6084/m9.figshare.125529863/7/2022 - Adjusted the rate of active cases calculation in the U.S. to reflect the rates of serious and severe cases due nearly completely dominant Omicron variant.6/24/2020 - Expanded Case Rates discussion to include fix on 6/23 for calculating active cases.6/22/2020 - Added Executive Summary and Subsequent Outbreaks sectionsRevisions on 6/10/2020 based on updated CDC reporting. This affects the estimate of active cases by revising the average duration of cases with hospital stays downward from 30 days to 25 days. The result shifted 76 U.S. counties out of Epidemic to Spreading trend and no change for national level trends.Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Correction on 6/1/2020Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Revisions added on 4/30/2020 are highlighted.Revisions added on 4/23/2020 are highlighted.Executive SummaryCOVID-19 Trends is a methodology for characterizing the current trend for places during the COVID-19 global pandemic. Each day we assign one of five trends: Emergent, Spreading, Epidemic, Controlled, or End Stage to geographic areas to geographic areas based on the number of new cases, the number of active cases, the total population, and an algorithm (described below) that contextualize the most recent fourteen days with the overall COVID-19 case history. Currently we analyze the countries of the world and the U.S. Counties. The purpose is to give policymakers, citizens, and analysts a fact-based data driven sense for the direction each place is currently going. When a place has the initial cases, they are assigned Emergent, and if that place controls the rate of new cases, they can move directly to Controlled, and even to End Stage in a short time. However, if the reporting or measures to curtail spread are not adequate and significant numbers of new cases continue, they are assigned to Spreading, and in cases where the spread is clearly uncontrolled, Epidemic trend.We analyze the data reported by Johns Hopkins University to produce the trends, and we report the rates of cases, spikes of new cases, the number of days since the last reported case, and number of deaths. We also make adjustments to the assignments based on population so rural areas are not assigned trends based solely on case rates, which can be quite high relative to local populations.Two key factors are not consistently known or available and should be taken into consideration with the assigned trend. First is the amount of resources, e.g., hospital beds, physicians, etc.that are currently available in each area. Second is the number of recoveries, which are often not tested or reported. On the latter, we provide a probable number of active cases based on CDC guidance for the typical duration of mild to severe cases.Reasons for undertaking this work in March of 2020:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-25 days + 5% from past 26-49 days - total deaths. On 3/17/2022, the U.S. calculation was adjusted to: Active Cases = 100% of new cases in past 14 days + 6% from past 15-25 days + 3% from past 26-49 days - total deaths. Sources: https://www.cdc.gov/mmwr/volumes/71/wr/mm7104e4.htm https://covid.cdc.gov/covid-data-tracker/#variant-proportions If a new variant arrives and appears to cause higher rates of serious cases, we will roll back this adjustment. We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source

This dataset tracks the updates made on the dataset "Coronavirus COVID-19 Global Cases Dashboard from Johns Hopkins University" as a repository for previous versions of the data and metadata.

This dataset tracks the updates made on the dataset "Coronavirus COVID-19 Global Cases Dashboard from Johns Hopkins University" as a repository for previous versions of the data and metadata.

February 23rd, 2020 - Present. Daily reports of Covid-19 confirmed cases, deaths, and recoveries for the Commonwealth of Virginia. Updated daily at 10:00 a.m.

This data comes from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. This is the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Also, Supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL).

https://github.com/CSSEGISandData/COVID-19#covid-19-data-repository-by-the-center-for-systems-science-and-engineering-csse-at-johns-hopkins-university

COVID-19 data available by county from Johns Hopkins University (ArcGIS Blog).Johns Hopkins University is now providing data in a map layer by county for COVID-19 cases and deaths. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This feature layer is supported by Esri Living Atlas team and JHU Data Services. See the FAQ or contact Johns Hopkins for more information._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

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COVID-19 Worldwide Daily Data

Daily global COVID-19 data for all countries, provided by Johns Hopkins University (JHU) Center for Systems Science and Engineering (CSSE). If you want to use the update version of the data, you can use our daily updated data with the help of api key by entering it via Altadata.

Overview

In this data product, you may find the latest and historical global daily data on the COVID-19 pandemic for all countries.

The COVID‑19 pandemic, also known as the coronavirus pandemic, is an ongoing global pandemic of coronavirus disease 2019 (COVID‑19), caused by severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2). The outbreak was first identified in December 2019 in Wuhan, China. The World Health Organization declared the outbreak a Public Health Emergency of International Concern on 30 January 2020 and a pandemic on 11 March. As of 12 August 2020, more than 20.2 million cases of COVID‑19 have been reported in more than 188 countries and territories, resulting in more than 741,000 deaths; more than 12.5 million people have recovered.

The Johns Hopkins Coronavirus Resource Center is a continuously updated source of COVID-19 data and expert guidance. They aggregate and analyze the best data available on COVID-19 - including cases, as well as testing, contact tracing and vaccine efforts - to help the public, policymakers and healthcare professionals worldwide respond to the pandemic.

Methodology

• Cases and Death counts include confirmed and probable (where reported)
• Recovered cases are estimates based on local media reports, and state and local reporting when available, and therefore may be substantially lower than the true number. US state-level recovered cases are from COVID Tracking Project.
• Active cases = total cases - total recovered - total deaths
• Incidence Rate = cases per 100,000 persons
• Case-Fatality Ratio (%) = Number recorded deaths / Number cases
• Country Population represents 2019 projections by UN Population Division, integrated to the JHU CSSE's COVID-19 data by ALTADATA

Data Source

• Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE)
• United Nations Population Division

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Data Dictionary

• Reported Date (reported_date) : Covid-19 Report Date
• Country_Region (country_region) : Country, region or sovereignty name
• Population (population) : Country populations as per United Nations Population Division
• Confirmed Case (confirmed) : Confirmed cases include presumptive positive cases and probable cases
• Active cases (active) : Active cases = total confirmed - total recovered - total deaths
• Deaths (deaths) : Death cases counts
• Recovered (recovered) : Recovered cases counts
• Mortality Rate (mortality_rate) : Number of recorded deaths * 100 / Number of confirmed cases
• Incident Rate (incident_rate) : Confirmed cases per 100,000 persons

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.This map is updated weekly. It shows COVID-19 Trend for the most recent Monday with a colored dot for each county. The larger the dot, the longer the county has had this trend. Includes Puerto Rico, Guam, Northern Marianas, U.S. Virgin Islands.Note: Nebraska Stopped reporting county level-results on 5/25/2021 and re-started on 9/26/21 with a lump-sum representing the previous four months - this impacted the weekly sum of cases fields.The intent of this map is to give more context than just the current day of new data because daily data for COVID-19 cases is volatile and can be unreliable on the day it is first reported. Weekly summaries in the counts of new cases smooth out this volatility.Click or tap on a county to see a history of trend changes and a weekly graph of new cases going back to February 1, 2020.For more information about COVID-19 trends, see the full methodology.Data Source: Johns Hopkins University CSSE US Cases by County dashboard.

Context:

From World Health Organization - On 31 December 2019, WHO was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The virus did not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people.

So daily level information on the affected people can give some interesting insights when it is made available to the broader data science community.

Johns Hopkins University has made an excellent dashboard using the affected cases data. Data is extracted from the google sheets associated and made available here.

Edited:

Now data is available as csv files in the Johns Hopkins Github repository. Please refer to the github repository for the Terms of Use details. Uploading it here for using it in Kaggle kernels and getting insights from the broader DS community.

Content

2019 Novel Coronavirus (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. Early on, many of the patients in the outbreak in Wuhan, China reportedly had some link to a large seafood and animal market, suggesting animal-to-person spread. However, a growing number of patients reportedly have not had exposure to animal markets, indicating person-to-person spread is occurring. At this time, it’s unclear how easily or sustainably this virus is spreading between people - CDC

This dataset has daily level information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this is a time series data and so the number of cases on any given day is the cumulative number.

The data is available from 22 Jan, 2020.

Here’s a polished version suitable for a professional Kaggle dataset description:

Dataset Description

This dataset contains time-series and case-level records of the COVID-19 pandemic. The primary file is covid_19_data.csv, with supporting files for earlier records and individual-level line list data.

Files and Columns

1. covid_19_data.csv (Main File)

This is the primary dataset and contains aggregated COVID-19 statistics by location and date.

• Sno – Serial number of the record
• ObservationDate – Date of the observation (MM/DD/YYYY)
• Province/State – Province or state of the observation (may be missing for some entries)
• Country/Region – Country of the observation
• Last Update – Timestamp (UTC) when the record was last updated (not standardized, requires cleaning before use)
• Confirmed – Cumulative number of confirmed cases on that date
• Deaths – Cumulative number of deaths on that date
• Recovered – Cumulative number of recoveries on that date

2. 2019_ncov_data.csv (Legacy File)

This file contains earlier COVID-19 records. It is no longer updated and is provided only for historical reference. For current analysis, please use covid_19_data.csv.

3. COVID_open_line_list_data.csv

This file provides individual-level case information, obtained from an open data source. It includes patient demographics, travel history, and case outcomes.

4. COVID19_line_list_data.csv

Another individual-level case dataset, also obtained from public sources, with detailed patient-level information useful for micro-level epidemiological analysis.

✅ Use covid_19_data.csv for up-to-date aggregated global trends.

✅ Use the line list datasets for detailed, individual-level case analysis.

Country level datasets:

If you are interested in knowing country level data, please refer to the following Kaggle datasets:

India - https://www.kaggle.com/sudalairajkumar/covid19-in-india

South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset

Italy - https://www.kaggle.com/sudalairajkumar/covid19-in-italy

Brazil - https://www.kaggle.com/unanimad/corona-virus-brazil

USA - https://www.kaggle.com/sudalairajkumar/covid19-in-usa

Switzerland - https://www.kaggle.com/daenuprobst/covid19-cases-switzerland

Indonesia - https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases

Acknowledgements :

Johns Hopkins University for making the data available for educational and academic research purposes

MoBS lab - https://www.mobs-lab.org/2019ncov.html

World Health Organization (WHO): https://www.who.int/

DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia.

BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/

National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml

China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm

Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html

Macau Government: https://www.ssm.gov.mo/portal/

Taiwan CDC: https://sites.google....

Has the curve flattened?

Countries around the world are working to “flatten the curve” of the coronavirus pandemic. Flattening the curve involves reducing the number of new COVID-19 cases from one day to the next. This helps prevent healthcare systems from becoming overwhelmed. When a country has fewer new COVID-19 cases emerging today than it did on a previous day, that’s a sign that the country is flattening the curve.

On a trend line of total cases, a flattened curve looks how it sounds: flat. On the charts on this page, which show new cases per day, a flattened curve will show a downward trend in the number of daily new cases.

This analysis uses a 5-day moving average to visualize the number of new COVID-19 cases and calculate the rate of change. This is calculated for each day by averaging the values of that day, the two days before, and the two next days. This approach helps prevent major events (such as a change in reporting methods) from skewing the data. The interactive charts below show the daily number of new cases for the 10 most affected countries, based on the reported number of deaths by COVID-19.

This datas were last updated on Saturday, April 25, 2020 at 11:51 PM EDT.

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.Trends represent the day-to-day rate of new cases with a focus on the most recent 10 to 14 days. Includes Puerto Rico, Guam, Northern Marianas, and U.S. Virgin Islands. Daily new case counts are volatile for many reasons and sometimes the trends reflect that volatility. Thus, we decided to include longer-term summaries here. County Trends as of 9 Mar 20230 (-0) in Emergent1135 (+51) in Spreading1664 (-63) in Epidemic230 (+10) in Controlled110 (+2) in End StageNotes: Many states now only report once per week, and FL only once every two weeks. On 3/7/2022 we adjusted the formula for active cases to reflect the Omicron Variant which is documented to cause lower rates of serious and severe illness. To produce these trends we analyze daily updates from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.For more information about COVID-19 trends, see our country level trends story map and the full methodology.Data Source: Johns Hopkins University CSSE US Cases by County dashboard and USAFacts for Utah County level Data.Feature layer generated from running the Join Features solution that is the basis for daily updates for the U.S. County COVID-19 Tends Story Map.

This feature layer contains the most up-to-date COVID-19 cases for the US. Data is pulled from the Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, the Red Cross, the Census American Community Survey, and the Bureau of Labor and Statistics, and aggregated at the US county level.