Deaths counts for influenza, pneumonia, and COVID-19 reported to NCHS by week ending date, by state and HHS region, and age group.

Effective September 27, 2023, this dataset will no longer be updated. Similar data are accessible from wonder.cdc.gov.Deaths involving COVID-19, pneumonia, and influenza reported to NCHS by sex, age group, and jurisdiction of occurrence.

Effective June 28, 2023, this dataset will no longer be updated. Data deaths by place of death are available in this dataset https://data.cdc.gov/NCHS/d/4va6-ph5s. Deaths involving COVID-19, pneumonia and influenza reported to NCHS by place of death and state, United States.

Effective September 27, 2023, this dataset will be updated weekly on Thursdays. Deaths involving COVID-19, pneumonia, and influenza reported to NCHS by week ending date and by state

Context

Corona virus cases in the US is stacking up higher and higher. Understanding this virus is crucial to stopping it's spread.

Content

The dataset shows, deaths involving coronavirus disease 2019 (COVID-19), pneumonia, and influenza reported to NCHS by sex and age group and state.

Acknowledgements

Credits to this data set comes from : https://data.cdc.gov/NCHS/Provisional-COVID-19-Death-Counts-by-Sex-Age-and-S/9bhg-hcku

Statistical information on deaths of confirmed cases of Severe special infectious pneumonia(COVID-19) from 2020, sub-statistical tables stratified by region, age group, and gender. This data set is updated once a day according to the fixed schedule of the system. At present, there are more cases of severe special contagious pneumonia imported from abroad than those diagnosed at the airport or centralized quarantine center, and they are immediately isolated and treated, so the county and city information is not indicated.

Effective September 27, 2023, this dataset will no longer be updated. Similar data are accessible from wonder.cdc.gov.

Deaths involving COVID-19, pneumonia, and influenza reported to NCHS by race, age, and jurisdiction of occurrence.

Provisional COVID-19 Deaths by Sex and Age

  Description

Effective September 27, 2023, this dataset will no longer be updated. Similar data are accessible from wonder.cdc.gov. Deaths involving COVID-19, pneumonia, and influenza reported to NCHS by sex, age group, and jurisdiction of occurrence.

  Dataset Details

Publisher: Centers for Disease Control and Prevention Temporal Coverage: 2020-01-01/2023-07-29 Geographic Coverage: United States, Puerto Rico Last… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/provisional-covid-19-deaths-by-sex-and-age.

This dataset is a per-state amalgamation of demographic, public health and other relevant predictors for COVID-19.

Deaths, Infections and Tests by State

The COVID Tracking Project: https://covidtracking.com/data/api

Used positive, death and totalTestResults from the API for, respectively, Infected, Deaths and Tested in this dataset. Please read the documentation of the API for more context on those columns

Predictor Data and Sources

Population (2020)

Density is people per meter squared https://worldpopulationreview.com/states/

ICU Beds and Age 60+

https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/

GDP

https://worldpopulationreview.com/states/gdp-by-state/

Income per capita (2018)

https://worldpopulationreview.com/states/per-capita-income-by-state/

Gini

https://en.wikipedia.org/wiki/List_of_U.S._states_by_Gini_coefficient

Unemployment (2020)

Rates from Feb 2020 and are percentage of labor force
https://www.bls.gov/web/laus/laumstrk.htm

Sex (2017)

Ratio is Male / Female
https://www.kff.org/other/state-indicator/distribution-by-gender/

Smoking Percentage (2020)

https://worldpopulationreview.com/states/smoking-rates-by-state/

Influenza and Pneumonia Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/flu_pneumonia_mortality/flu_pneumonia.htm

Chronic Lower Respiratory Disease Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/lung_disease_mortality/lung_disease.htm

Active Physicians (2019)

https://www.kff.org/other/state-indicator/total-active-physicians/

Hospitals (2018)

https://www.kff.org/other/state-indicator/total-hospitals

Health spending per capita

Includes spending for all health care services and products by state of residence. Hospital spending is included and reflects the total net revenue. Costs such as insurance, administration, research, and construction expenses are not included.
https://www.kff.org/other/state-indicator/avg-annual-growth-per-capita/

Pollution (2019)

Pollution: Average exposure of the general public to particulate matter of 2.5 microns or less (PM2.5) measured in micrograms per cubic meter (3-year estimate)
https://www.americashealthrankings.org/explore/annual/measure/air/state/ALL

Medium and Large Airports

For each state, number of medium and large airports https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_the_United_States

Temperature (2019)

Note that FL was incorrect in the table, but is corrected in the Hottest States paragraph
https://worldpopulationreview.com/states/average-temperatures-by-state/
District of Columbia temperature computed as the average of Maryland and Virginia

Urbanization (2010)

Urbanization as a percentage of the population https://www.icip.iastate.edu/tables/population/urban-pct-states

Age Groups (2018)

https://www.kff.org/other/state-indicator/distribution-by-age/

School Closure Dates

Schools that haven't closed are marked NaN https://www.edweek.org/ew/section/multimedia/map-coronavirus-and-school-closures.html

Note that some datasets above did not contain data for District of Columbia, this missing data was found via Google searches manually entered.

Over 12 million people in the United States died from all causes between the beginning of January 2020 and August 21, 2023. Over 1.1 million of those deaths were with confirmed or presumed COVID-19.

Vaccine rollout in the United States Finding a safe and effective COVID-19 vaccine was an urgent health priority since the very start of the pandemic. In the United States, the first two vaccines were authorized and recommended for use in December 2020. One has been developed by Massachusetts-based biotech company Moderna, and the number of Moderna COVID-19 vaccines administered in the U.S. was over 250 million. Moderna has also said that its vaccine is effective against the coronavirus variants first identified in the UK and South Africa.

Deaths involving coronavirus disease 2019 (COVID-19) and pneumonia reported to NCHS by jurisdiction of occurrence, place of death, and age group.

Context

Dataset aims to facilitate a state by state comparison of potential risk factors that may heighten Covid 19 transmission rates or deaths. It includes state by state estimates of: covid 19 positives/deaths, flu/pneumonia deaths, major city population densities, available hospital resources, high risk health condition prevalance, population over 60, means of work transportation rates, housing characteristics (ie number of large apartment complexes/seniors living alone), and industry information.

Content

The Data Includes:

1) Covid 19 Outcome Stats:

Covid_Death : Covid Deaths by State

Covid_Positive : Covid Positive Tests by State

2) US Major City Population Density by State: CBSA_Major_City_max_weighted_density

3) KFF Estimates of Total Hospital Beds by State:

Kaiser_Total_Hospital_Beds

4) 2018 Season Flu and Pneumonia Death Stats:

FLUVIEW_TOTAL_PNEUMONIA_DEATHS_Season_2018

FLUVIEW_TOTAL_INFLUENZA_DEATHS_Season_2018

5)US Total Rates of Flu Hospitalization by Underlying Condition:

Fluview_US_FLU_Hospitalization_Rate_....

6) State by State BRFSS Prevalance Rates of Conditions Associated with Higher Flu Hospitalization Rates

BRFSS_Diabetes_Prevalance BRFSS_Asthma_Prevalance BRFSS_COPD_Prevalance
BRFSS_Obesity BMI Prevalance BRFSS_Other_Cancer_Prevalance BRFSS_Kidney_Disease_Prevalance BRFSS_Obesity BMI Prevalance BRFSS_2017_High_Cholestoral_Prevalance BRFSS_2017_High_Blood_Pressure_Prevalance Census_Population_Over_60

7)State by state breakdown of Means of Work Transpotation:

COMMUTE_Census_Worker_Public_Transportation_Rate

8) State by state breakdown of Housing Characteristics

9) State by State breakdown of Industry Information

Acknowledgements

Links to data sources:

https://worldpopulationreview.com/states/

https://covidtracking.com/data/

https://gis.cdc.gov/GRASP/Fluview/FluHospRates.html https://www.kff.org/health-costs/issue-brief/state-data-and-policy-actions-to-address-coronavirus/#stateleveldata

https://data.census.gov/cedsci/table?q=United%20States&tid=ACSDP1Y2018.DP05&hidePreview=true&vintage=2018&layer=VT_2018_040_00_PY_D1&cid=S0103_C01_001E

Census Tables: ACSST1Y2018.S1811 ACSST1Y2018.S0102 ACSST1Y2018.S2403 ACSST1Y2018.S2501 ACSST1Y2018.S2504

https://www.census.gov/library/visualizations/2012/dec/c2010sr-01-density.html

https://gis.cdc.gov/grasp/fluview/mortality.html

Inspiration

I hope to show the existence of correlations that warrant a deeper county by county analysis to identify areas of increased risk requiring increased resource allocation or increased attention to preventative measures.

Variety of data files supporting CDC vaccination dashboards, downloaded 2.4.25. Includes weekly vaccination data for children, adults;COVID vaccination coverage overall and for pregnant women, nursing home residents, adults;Laboratory-confirmed RSV, COVID-19, and flu hospitalizations (source: RESPNet);Deaths from COVID-19, influenza, and RSV overall, by state, by race and ethnicity;ED visits with COVID-19, influenza, RSV, by demographics;NIS-ACM data on COVID-19 for adults (source:RespVaxView);Cumulative COVID-19 vaccination by age, jurisdictionCDC wastewater surviellance data tablesFluView Phase 2 Data***For CDC Covid-19 Nursing Home Data:Microdata: YesLevel of Analysis: Nursing HomesVariables Present: YesFile Layout: .csvCodebook: Yes Methods: YesWeights (with appropriate documentation): NoPublications: NoAggregate Data: No***For CDC NHSN Report State HCP Influenza Vaccination:Microdata: NoLevel of Analysis: StateVariables Present: YesFile Layout: N/ACodebook: NoMethods: YesWeights (with appropriate documentation): NoPublications: NoAggregate Data: No***For CDC Adult Covid NIS-ACM RespVax Data: Microdata: YesLevel of Analysis: Local - county, cityVariables Present: YesFile Layout: .csvCodebook: YesMethods: YesWeights (with appropriate documentation): YesPublications: NoAggregate Data: No***For NSSP Emergency Department Visits - COVID-19, Flu, etc. Microdata: YesLevel of Analysis: AilmentsVariables Present: YesFile Layout: .csvCodebook: NoMethods: Yes (https://docs.google.com/spreadsheets/d/19Po9Ir57Q-81Q5DfE1yKnW9NDLHXqPXc2307QY1hq24/edit?gid=1803019...) Weights (with appropriate documentation): NoPublications: NoAggregate Data: Yes***For CDC Percentage of Emergency Department Visits with Diagnosed COVID-19 in US:Microdata: YesLevel of Analysis: Demographic GroupsVariables Present: YesFile Layout: .csvCodebook: NoMethods: Yes (https://archive.cdc.gov/www_cdc_gov/ncird/surveillance/respiratory-illnesses/index.html)Weights (with appropriate documentation): NoPublications: NoAggregate Data: Yes***CDC Provisional COVID-19, Flu, and Pneumonia Death Counts:Microdata: YesLevel of Analysis: State, Demographic GroupsVariables Present: YesFile Layout: .csvCodebook: Yes (https://www.cdc.gov/nchs/nvss/vsrr/covid19/index.htm)Methods: YesWeights (with appropriate documentation): NoPublications: NoAggregate Data: No***For CDC Rates of Laboratory Confirmed RSV, Covid Hospitalizations:Microdata: YesLevel of Analysis: Weekly Rates by StateVariables Present: YesFile Layout: .csvCodebook: YesMethods: YesWeights (with appropriate documentation): NoPublications: NoAggregate Data: No***For CDC Vaccination Rates Among Adults 18 Years and Older :Microdata: YesLevel of Analysis: Yearly State Rate by Demographic Variables Present: YesFile Layout: .csvCodebook: YesMethods: Yes https://www.cdc.gov/adultvaxview/publications-resources/vaccination-coverage-adults-2021.html Weights (with appropriate documentation): NoPublications: NoAggregate Data: No***For CDC Vaccination Rates Among Pregnant Women:Microdata: YesLevel of Analysis: Percent Vaccinated Per Year by Demographic Type and Vaccination StatusVariables Present: YesFile Layout: .csvCodebook: YesMethods: Yes https://www.cdc.gov/fluvaxview/coverage-by-season/pregnant-april-2024.htmlWeights (with appropriate documentation): NoPublications: NoAggregate Data: Yes***For CDC Weekly Cum. COVID-19 Vaccination Coverage by Season, Race and Ethnicity, Medicare FFS aged 65+:Microdata: YesLevel of Analysis: Demographic Groups Variables Present: YesFile Layout: .csvCodebook: Yes https://data.cdc.gov/Vaccinations/Weekly-Cumulative-COVID-19-Vaccination-Coverage-an/ksfb-ug5d/about...Methods: Yes (above link)Weights (with appropriate documentation): NoPublications: NoAggregate Data: Yes***CDC Weekly Cum. Est No COVID-19 Vax Admin in Pharmacy...:Microdata: YesLevel of Analysis: National (delineated by Age Group)Variables Present: Yes - separate document https://data.cdc.gov/Vaccinations/Weekly-Cumulative-Estimated-Number-of-COVID-19-Vac/ewpg-rz7g/about...File Layout: .csvCodebook: Yes (see above link)Methods: Yes (see above link)Weights (with appropriate documentation): NoPublications: NoAggregate Data: No***CDC Weekly Cum. Doses (in millions) of Influenza Vaccinations...:Microdata: YesLevel of Analysis: National Variables Present: Yes Fi

Dataset on deaths involving COVID-19, pneumonia, and influenza reported to NCHS by race, age, and jurisdiction of occurrence.

Context

Amidst the COVID-19 outbreak, the world is facing great crisis in every way. The value and things we built as a human race are going through tremendous challenges. It is a very small effort to bring curated data set on Novel Corona Virus to accelerate the forecasting and analytical experiments to cope up with this critical situation. It will help to visualize the country level out break and to keep track on regularly added new incidents.

COVID-19 Country Level Timeseries Dataset

This Dataset contains country wise public domain time series information on COVID-19 outbreak. The Data is sorted alphabetically on Country name and Date of Observation.

Column Descriptions

The data set contains the following columns:
ObservationDate: The date on which the incidents are observed country: Country of the Outbreak Confirmed: Number of confirmed cases till observation date Deaths: Number of death cases till observation date Recovered: Number of recovered cases till observation date New Confirmed: Number of new confirmed cases on observation date New Deaths: Number of New death cases on observation date New Recovered: Number of New recovered cases on observation date latitude: Latitude of the affected country longitude: Longitude of the affected country

Acknowledgements

This data set is a cleaner version of the https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset data set with added geo location information and regularly added incident counts. I would like to thank this great effort by SRK.

Original Data Source

Johns Hopkins University 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.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

Dataset includes the weekly provisional count of deaths in the United States due to COVID-19, deaths from all causes and percent of expected deaths (i.e., number of deaths received over number of deaths expected based on data from previous years), pneumonia deaths (excluding pneumonia deaths involving influenza), and pneumonia deaths involving COVID-19; (a) by week ending date, (b) by age at death, and (c) by specific jurisdictions.

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....

I continue to work on improving this Dataset and will upload as soon as I have an improved version of it. I don't own this dataset, I have merely tried to enrich the data that is gathered from multiple sources by John Hopkins CSSE.

Context

COVID-19 is perhaps the biggest historical event of our lifetime with the kind of destruction and disruption it has already caused to the people around the world. I wanted to build a dashboard summarizing the events from beginning to date and that's the reason I worked on combining all the daily reports into one file.

Content

This file consists of incidents reported from across the world Jan 22 onwards. Incidents are categorized into Confirmed, Deaths and Recovered. Country/Region and/or Province/State information is available. Geo-coordinates are available but these are missing for countries like China

Acknowledgements

This data belongs to John Hopkins CSSE which they gathered from multiple sources. Below is from JHU Github account, please read before using the dataset.

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).

Visual Dashboard (desktop): https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

Visual Dashboard (mobile): http://www.arcgis.com/apps/opsdashboard/index.html#/85320e2ea5424dfaaa75ae62e5c06e61

Lancet Article: An interactive web-based dashboard to track COVID-19 in real time

Provided by Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE): https://systems.jhu.edu/

Data Sources:

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/

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

Contact Us:

Email: jhusystems@gmail.com

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.

Inspiration

COVID-19 is perhaps the biggest historical event of our lifetime with the kind of destruction and disruption it has already caused to the people around the world. I wanted to build a dashboard summarizing the events from beginning to date and that's the reason I worked on combining all the daily reports into one file.

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.

Column Description

Main file in this dataset is covid_19_data.csv and the detailed descriptions are below.

covid_19_data.csv

• Sno - Serial number
• ObservationDate - Date of the observation in MM/DD/YYYY
• Province/State - Province or state of the observation (Could be empty when missing)
• Country/Region - Country of observation
• Last Update - Time in UTC at which the row is updated for the given province or country. (Not standardised and so please clean before using it)
• Confirmed - Cumulative number of confirmed cases till that date
• Deaths - Cumulative number of of deaths till that date
• Recovered - Cumulative number of recovered cases till that date

Apart from that these two files have individual level information

COVID_open_line_list_data.csv This file is originally obtained from this link

COVID19_line_list_data.csv This files is originally obtained from this link

Country level datasets If you are interested in knowing country level data, please refer to the following Kaggle datasets: South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset Italy -
https://www.kaggle.com/sudalairajkumar/covid19-in-italy

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.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

Inspiration

Some useful insi...

Context

Coronavirus disease 2019 (COVID-19) time series listing confirmed cases, reported deaths and reported recoveries. Data is disaggregated by country (and sometimes subregion). Coronavirus disease (COVID-19) is caused by the Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) and has had a worldwide effect. On March 11 2020, the World Health Organization (WHO) declared it a pandemic, pointing to the over 118,000 cases of the coronavirus illness in over 110 countries and territories around the world at the time.

Sources

1. World Health Organization (WHO): https://www.who.int/
2. DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia
3. BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/
4. National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml
5. China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm