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.

Influenza and pneumonia caused around 10.9 deaths in the U.S. per 100,000 population in 2023. Influenza, or the flu, is a viral infection that is highly contagious and especially common in the winter season. Influenza is a common cause of pneumonia, although most cases of the flu do not develop into pneumonia. Pneumonia is an infection or inflammation of the lungs and is particularly deadly among young children and the elderly. Influenza cases Influenza is very common in the United States, with an estimated 40 million cases reported in 2023-2024. Common symptoms of the flu include cough, fever, runny or stuffy nose, sore throat and headache. Symptoms can be mild but can also be severe enough to require medical attention. In 2023-2024, there were around 18 million influenza-related medical visits in the United States. Prevention To prevent contracting the flu, people can take everyday precautions such as regularly washing their hands and avoiding those who are sick, but the best way to prevent the flu is by receiving the flu vaccination every year. Receiving a flu vaccination is especially important for young children and the elderly, as they are most susceptible to flu complications and associated death. In 2024, around 70 percent of those aged 65 years and older received a flu vaccine, while only 33 percent of those aged 18 to 49 years had done so.

Pneumonia death rates by county, all races (includes Hispanic/Latino), all sexes, all ages, 2019-2023. Death data were provided by the National Vital Statistics System. Death rates (deaths per 100,000 population per year) are age-adjusted to the 2000 US standard population (20 age groups: <1, 1-4, 5-9, ... , 80-84, 85-89, 90+). Rates calculated using SEER*Stat. Population counts for denominators are based on Census populations as modified by the National Cancer Institute. The US Population Data File is used for mortality data. The Average Annual Percent Change is based onthe APCs calculated by the Joinpoint Regression Program (Version 4.9.0.0). Due to data availability issues, the time period used in the calculation of the joinpoint regression model may differ for selected counties. Counties with a (3) after their name may have their joinpoint regresssion model calculated using a different time period due to data availability issues.

In 2020, approximately ** men and ** women per 100,000 population died as a result of pneumonia in England and Wales. In every year in the provided time interval the mortality rate was higher among men, although both genders have experienced a general decline in deaths from pneumonia. Regionally, the North West had the highest mortality rate for both genders.

Pneumonia risk groups

The age groups most at risk from pneumonia is undoubtedly the older age groups. In 2021, in England and Wales, pneumonia was the cause of death for approximately *** thousand over ** year olds, of which *** thousand were women. Furthermore, around *** thousand individuals aged between 80 and 89 years lost their lives due to pneumonia in 2021.

Prevalence of other lung diseases

In England and Wales in 2019, the mortality rate from bronchitis for men was around ** per 100,000 population, while the rate for women was approximately **. The mortality rate for bronchitis was higher than pneumonia, this is caused in part by the large decline in the mortality rate of pneumonia since the year 2000.

Deaths counts for influenza, pneumonia, and coronavirus disease 2019 (COVID-19) reported to NCHS by week ending date, by state and HHS region, and age group.

According to preliminary data between January and October 2024, *** percent of deaths in the Philippines were caused by pneumonia. Deaths from such illnesses significantly dropped from 2020 onwards, from its peak share of **** percent in 2019.

Legacy unique identifier: P00597

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

Legacy unique identifier: P00598

In 2022, the total number of deaths from influenza and pneumonia in Canada amounted to 5,985, an increase from 4,115 deaths the year before. From 2000 to 2022, Canada registered the highest number of deaths due to influenza and pneumonia in 2018, when 8,594 people died from these diseases. This statistic shows the number of deaths from influenza and pneumonia in Canada from 2000 to 2022.

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

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.

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.

In 2019, an estimated ******* cases of pneumonia were recorded in Mexico. The country's oldest population had the highest amount of cases, with over ****** (or about ** percent) of them occurring in adults aged 65 and older. Young people between the ages 11 and ** had the least amount of pneumonia cases, with only about *** percent (or *****) of the total 2019 cases. In 2018, more than ** thousand people in Mexico died due to pneumonia and influenza, making these illnesses the seventh most common cause of death that year in the North American country.

Background: This study aimed to present and analyze the causes of death in the Korean population in 2019. Methods: Based on the Korean Standard Classification of Diseases and Causes of Death and the International Statistical Classification of Diseases and Related Health Problems, the 10th revision, cause-of-death data for 2019 from Statistics Korea, were examined. Results: There was a total of 295,110 deaths, dropping 3,710 (-1.27%) from 2018. The crude death rate (the number of death per 100,000 people) was 574.8, a 7.6 (-1.3%) reduction from 2018. The 10 leading causes of death, in order, were malignant neoplasms, heart diseases, pneumonia, cerebrovascular diseases, intentional self-harm, diabetes mellitus, Alzheimer's disease, liver diseases, chronic lower respiratory diseases, and hypertensive diseases. Within the category of malignant neoplasms, the top five leading organs of involvement were the lung, liver, colon, stomach, and pancreas, which were the same to order in 2018. Alzheimer's disease rose to the seventh leading cause of death from the ninth in 2018. It ranked as the female's fifth leading cause of death. Pneumonia became the female's third leading cause of death Conclusion: These changes reflect the increase of female people over 65 years of age, who are vulnerable to cognitive disorders and infectious diseases. The Korean government has to take urgent preventive and therapeutic action against dementia, particularly Alzheimer's disease.

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

This dataset explores the relationship between the Air Quality Index (AQI) and death rates from respiratory illnesses in the United States from 2000 to 2019. It includes detailed data on AQI levels, ranging from good to hazardous, alongside corresponding mortality rates caused by conditions such as asthma, chronic obstructive pulmonary disease (COPD), and pneumonia. The dataset covers counties across the entire United States, providing a geographically comprehensive analysis. By examining trends over two decades, this dataset offers valuable insights into how air quality impacts respiratory health outcomes nationwide.

Abstract Background: This study analyzed the causes of death in the Korean population in 2020. Methods: Cause-of-death data for 2020 from Statistics Korea were examined based on the Korean Standard Classification of Diseases and Causes of Death, 7th revision and the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Results: In total, 304,948 deaths occurred, reflecting an increase of 9,838 (3.3%) from 2019. The crude death rate (the number of deaths per 100,000 people) was 593.9, corresponding to an increase of 19.0 (3.3%) from 2019. The 10 leading causes of death, in descending order, were malignant neoplasms, heart diseases, pneumonia, cerebrovascular diseases, intentional self-harm, diabetes mellitus, Alzheimer’s disease, liver diseases, hypertensive diseases, and sepsis. Cancer accounted for 27.0% of deaths. Within the category of malignant neoplasms, the top 5 leading organs of involvement were the lung, liver, colon, stomach, and pancreas. Sepsis was included in the 10 leading causes of death for the first time. Mortality due to pneumonia decreased to 43.3 (per 100,000 people) from 45.1 in 2019. The number of deaths due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was 950, of which 54.5% were in people aged 80 or older. Conclusion: These changes reflect the continuing increase in deaths due to diseases of old age, including sepsis. The decrease in deaths due to pneumonia may have been due to protective measures against SARS-CoV-2. With the concomitant decrease in fertility, 2020 became the first year in which Korea’s natural total population decreased.

The Coronavirus Disease 2019 (COVID-19) has spread all over the world and impacted many people’s lives. The characteristics of COVID-19 and other types of pneumonia have both similarities and differences, which confused doctors initially to separate and understand them. Here we presented a retrospective analysis for both COVID-19 and other types of pneumonia by combining the COVID-19 clinical data, eICU and MIMIC-III databases. Machine learning models, including logistic regression, random forest, XGBoost and deep learning neural networks, were developed to predict the severity of COVID-19 infections as well as the mortality of pneumonia patients in intensive care units (ICU). Statistical analysis and feature interpretation, including the analysis of two-level attention mechanisms on both temporal and non-temporal features, were utilized to understand the associations between different clinical variables and disease outcomes. For the COVID-19 data, the XGBoost model obtained the best performance on the test set (AUROC = 1.000 and AUPRC = 0.833). On the MIMIC-III and eICU pneumonia datasets, our deep learning model (Bi-LSTM_Attn) was able to identify clinical variables associated with death of pneumonia patients (AUROC = 0.924 and AUPRC = 0.802 for 24-hour observation window and 12-hour prediction window). The results highlighted clinical indicators, such as the lymphocyte counts, that may help the doctors to predict the disease progression and outcomes for both COVID-19 and other types of pneumonia.

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....