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.

Between January and August 2020, there has been approximately 48.2 thousand deaths in England and Wales with COVID-19 as an underlying cause. As illustrated in the table, the number of deaths as a result of COVID-19 are much higher than from either pneumonia or influenza. There has been over three times the number of deaths from COVID-19 than pneumonia and influenza so far in 2020. The overall number of confirmed COVID-19 cases in the UK can be found here. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

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.

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 COVID-19 reported to NCHS by week ending date, by state and HHS region, and age group.

No. of Deaths: Caused by: Pneumonia data was reported at 2,811.000 Person in Sep 2024. This records an increase from the previous number of 2,735.000 Person for Jun 2024. No. of Deaths: Caused by: Pneumonia data is updated quarterly, averaging 2,093.500 Person from Mar 2017 (Median) to Sep 2024, with 30 observations. The data reached an all-time high of 3,695.000 Person in Jun 2021 and a record low of 1,402.000 Person in Dec 2020. No. of Deaths: Caused by: Pneumonia data remains active status in CEIC and is reported by National Administrative Department of Statistics. The data is categorized under Global Database’s Colombia – Table CO.G012: Number of Deaths: Cause of Death.

In 2023, there were **** deaths from influenza and pneumonia in Canada per 100,000 population. Influenza, more commonly known as the flu, is a highly contagious viral infection and frequent cause of pneumonia. Pneumonia is a more serious infection of the lungs and is particularly deadly among young children, the elderly, and those with certain chronic conditions. Vaccination There exist vaccines for both influenza and pneumonia, and although effectiveness varies, vaccination remains one of the best ways to prevent these illnesses. Nevertheless, only around ** percent of Canadians received an influenza vaccination in the past year in 2022. The most common reason why Canadian adults received the influenza vaccination was to prevent infection or because they did not want to get sick. Pneumonia hospitalization Every year tens of thousand of people in Canada are hospitalized for pneumonia. In *********, there were over ****** emergency room visits for pneumonia in Canada, a substantial decrease from the numbers recorded from 2010 to 2020. Perhaps unsurprisingly, those aged 65 years and older account for the highest number of emergency room visits for pneumonia. The median length of stay for emergency department visits for pneumonia in Canada has increased in recent years, with the median length of stay around *** minutes in *********.

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.

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.

Statistical information on confirmed cases and deaths of severe special infectious pneumonia starting in 2020, with secondary statistical tables stratified by region, age group, and gender. This data set is updated once a day according to the system's fixed schedule. At present, there are more cases of severe special infectious pneumonia imported from overseas than those confirmed by tests at airports or centralized quarantine stations and immediately isolated and treated, so their county and city information is not marked.

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

In 2020, approximately ** men and ** women per 100,000 population died as a result of pneumonia in England. The North West had the highest mortality rate for both genders in this year, with ***** men per 100,000 population dying from pneumonia and ***** women per 100,000.

This record contains raw data related to article "High mortality in COVID-19 patients with mild respiratory disease"

Introduction: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected 189 000 people in Italy, with more than 25 000 deaths. Several predictive factors of mortality have been identified; however, none has been validated in patients presenting with mild disease.

Methods: Patients with a diagnosis of interstitial pneumonia caused by SARS-CoV-2, presenting with mild symptoms, and requiring hospitalization in a non-intensive care unit with known discharge status were prospectively collected and retrospectively analysed. Demographical, clinical and biochemical parameters were recorded, as need for non-invasive mechanical ventilation and admission in intensive care unit. Univariate and multivariate logistic regression analyses were used to identify independent predictors of death.

Results: Between 28 February and 10 April 2020, 229 consecutive patients were included in the study cohort; the majority were males with a mean age of 60 years. 54% of patients had at least one comorbidity, with hypertension being the most commonly represented, followed by diabetes mellitus. 196 patients were discharged after a mean of 9 days, while 14.4% died during hospitalization because of respiratory failure. Age higher than 75 years, low platelet count (<150 × 10³ /mm³ ) and higher ferritin levels (>750 ng/mL) were independent predictors of death. Comorbidities were not independently associated with in-hospital mortality.

Conclusions: In-hospital mortality of patients with COVID-19 presenting with mild symptoms is high and is associated with older age, platelet count and ferritin levels. Identifying early predictors of outcome can be useful in the clinical practice to better stratify and manage patients with COVID-19.

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

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....

Poland Deaths: Rural: RS: ow Pneumonia data was reported at 6,653.000 Person in 2023. This records a decrease from the previous number of 6,915.000 Person for 2022. Poland Deaths: Rural: RS: ow Pneumonia data is updated yearly, averaging 4,740.000 Person from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 7,089.000 Person in 2020 and a record low of 3,106.000 Person in 2006. Poland Deaths: Rural: RS: ow Pneumonia data remains active status in CEIC and is reported by Statistics Poland. The data is categorized under Global Database’s Poland – Table PL.G006: Deaths: By Cause.

The COVID-19 pandemic brings to light the reality of the Brazilian health system. The underreporting of COVID-19 deaths in the state of Minas Gerais (MG), where the second largest population of the country is concentrated, reveals government unpreparedness, as there is a low capacity of testing in the population, which prevents the real understanding of the general panorama of SARS-CoV-2 dissemination. The goals of this research are to analyze the causes of deaths in different Brazilian government databases (Civil Registry Transparency Portal and InfoGripe) and to assess whether there are sub-records showing an unexpected increase in the frequency of deaths from causes clinically similar to COVID-19. A descriptive and quantitative analysis of the number of deaths by COVID-19 and similar causes was performed in different databases. Our results demonstrate that different official sources had a discrepancy of 109.45% between these data referring to the same period. There was also a 758.57% increase in SARI deaths in 2020, when compared to the average of previous years. Finally, it was shown that there was an increase in the rate of pneumonia and respiratory insufficiency (RI) by 6.34 and 6.25%, respectively. In conclusion, there is an underreporting of COVID-19 deaths in MG due to the unexplained excess of deaths caused by SARI, respiratory insufficiency, and pneumonia compared to previous years.

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

Context - Covid data falsification discussion:

An active discussion about the mortality data in Moscow has erupted in the days. The Moscow Times newspaper drew attention to a significant increase in official mortality rates in April 2020: "Moscow recorded 20% more fatalities in April 2020 compared to its average April mortality total over the past decade, according to newly published preliminary data from Moscow’s civil registry office. The data comes as Russia sees the fastest growth in coronavirus infections in Europe, while its mortality rate remains much lower than in many countries. Moscow, the epicenter of Russia’s coronavirus outbreak, has continued to see daily spikes in new cases despite being under lockdown since March 30. According to the official data, 11,846 people died in Russia’s capital in April of this year, roughly a 20% increase from the 10-year average for April deaths, which is 9,866. The numbers suggest that the city’s statistics of coronavirus deaths may be higher in reality than official numbers indicate. Russia boasts a relatively low coronavirus mortality rate of 0.9%, which experts believe is linked to the way coronavirus-related deaths are counted."

After this publication have been realesed The Moscow Department of Health has denied the statement of the inaccuracy of counting.:

First, Moscow is a region that openly publishes mortality data on its websites. Moscow on an initiative basis published data for April before the federal structures did it. Secondly, the comparison of mortality rates in the monthly dynamics is incorrect and is not a clear evidence of any trends. In April 2020, indeed, according to the Civil Registry Office in Moscow, 11,846 death certificates were issued. So, the increase compared to April 2019 amounted to 1841 people, and compared to the same month of 2018 - 985 people, i.e. 2 times less. Thirdly, the diagnosis of coronavirus-infected deaths in Moscow is established after a mandatory autopsy is performed in strict accordance with the Provisional Guidelines of the Russian Ministry of Health.Of the total number of deaths in April 2020, 639 are people whose cause of death is coronavirus infection and its complications, most often pneumonia.It should be emphasized that the pathological autopsy of the dead with suspected CoV-19 in Russia and Moscow is carried out in 100% of cases, unlike most other countries.It is impossible to name the cause of death of COVID-19 in other cases. For example, over 60% of deaths occurred from obvious alternative causes, such as vascular accidents (myocardial infarction and stroke), stage 4 malignant diseases (essentially palliative patients), leukemia, systemic diseases with the development of organ failure (e.g. amyloidosis and terminal renal insufficiency) and other non-curable deadly diseases. Fourth, any seasonal increase in the incidence of SARS, not to mention the pandemic caused by the spread of the new coronavirus, is always accompanied by an increase in mortality. This is due to the appearance of the dead directly from an infectious disease, but to an even greater extent from other diseases, the exacerbation of which and the decompensation of the condition of patients suffering from these diseases also leads to death. In these cases, the infectious onset is a catalyst for the rapid progression of chronic diseases and the manifestation of new diseases. Fifthly, a similar situation with statistics is observed in other countries - mortality from COVID-19 is lower than the overall increase in mortality. According to the official sites of cities:In New York, mortality from coronavirus in April amounted to 11,861 people. At the same time, the total increase in mortality compared to the same period in 2019 is 15709.In London, in April, 3,589 people died with a diagnosis of coronavirus, while the total increase was 5531 Sixth, even if all the additional mortality for April in Moscow is attributed to coronavirus, the mortality from COVID will be slightly more than 3%, which is lower than the official mortality in New York and London (10% and 23%, respectively). Moreover, if you make such a recount in these cities, the mortality rate in them will be 13% and 32%, respectively. Seventh, Moscow is open for discussion and is ready to share experience with both Russian and foreign experts.

Content

I think community members would be interested in studying the data on mortality in the Russian capital themselves and conducting a competent statistical check.

This may be of particular interest in connection with that he [US announced a grant of $ 250 thousand to "expose the disinformation of health care" in Russia](https://www....