Historical chart and dataset showing World death rate by year from 1950 to 2025.

This file contains COVID-19 death counts, death rates, and percent of total deaths by jurisdiction of residence. The data is grouped by different time periods including 3-month period, weekly, and total (cumulative since January 1, 2020). United States death counts and rates include the 50 states, plus the District of Columbia and New York City. New York state estimates exclude New York City. Puerto Rico is included in HHS Region 2 estimates. Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1. Number of deaths reported in this file are the total number of COVID-19 deaths received and coded as of the date of analysis and may not represent all deaths that occurred in that period. Counts of deaths occurring before or after the reporting period are not included in the file. Data during recent periods are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more, depending on the jurisdiction and cause of death. Death counts should not be compared across states. Data timeliness varies by state. Some states report deaths on a daily basis, while other states report deaths weekly or monthly. The ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York, New York City, Puerto Rico; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington. Rates were calculated using the population estimates for 2021, which are estimated as of July 1, 2021 based on the Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Blended Base consists of the blend of Vintage 2020 postcensal population estimates, 2020 Demographic Analysis Estimates, and 2020 Census PL 94-171 Redistricting File (see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2020-2021/methods-statement-v2021.pdf). Rates are based on deaths occurring in the specified week/month and are age-adjusted to the 2000 standard population using the direct method (see https://www.cdc.gov/nchs/data/nvsr/nvsr70/nvsr70-08-508.pdf). These rates differ from annual age-adjusted rates, typically presented in NCHS publications based on a full year of data and annualized weekly/monthly age-adjusted rates which have been adjusted to allow comparison with annual rates. Annualization rates presents deaths per year per 100,000 population that would be expected in a year if the observed period specific (weekly/monthly) rate prevailed for a full year. Sub-national death counts between 1-9 are suppressed in accordance with NCHS data confidentiality standards. Rates based on death counts less than 20 are suppressed in accordance with NCHS standards of reliability as specified in NCHS Data Presentation Standards for Proportions (available from: https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf.).

United States US: Suicide Mortality Rate: Male data was reported at 23.600 NA in 2016. This records an increase from the previous number of 23.000 NA for 2015. United States US: Suicide Mortality Rate: Male data is updated yearly, averaging 20.700 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 23.600 NA in 2016 and a record low of 17.900 NA in 2000. United States US: Suicide Mortality Rate: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

United States US: Maternal Mortality Ratio: Modeled Estimate: per 100,000 Live Births data was reported at 14.000 Ratio in 2015. This stayed constant from the previous number of 14.000 Ratio for 2014. United States US: Maternal Mortality Ratio: Modeled Estimate: per 100,000 Live Births data is updated yearly, averaging 13.000 Ratio from Dec 1990 (Median) to 2015, with 26 observations. The data reached an all-time high of 15.000 Ratio in 2009 and a record low of 11.000 Ratio in 1998. United States US: Maternal Mortality Ratio: Modeled Estimate: per 100,000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP.; ; WHO, UNICEF, UNFPA, World Bank Group, and the United Nations Population Division. Trends in Maternal Mortality: 1990 to 2015. Geneva, World Health Organization, 2015; Weighted average; This indicator represents the risk associated with each pregnancy and is also a Sustainable Development Goal Indicator for monitoring maternal health.

COVID-19 rate of death, or the known deaths divided by confirmed cases, was over ten percent in Yemen, the only country that has 1,000 or more cases. This according to a calculation that combines coronavirus stats on both deaths and registered cases for 221 different countries. Note that death rates are not the same as the chance of dying from an infection or the number of deaths based on an at-risk population. By April 26, 2022, the virus had infected over 510.2 million people worldwide, and led to a loss of 6.2 million. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. Note that Statista aims to also provide domestic source material for a more complete picture, and not to just look at one particular source. Examples are these statistics on the confirmed coronavirus cases in Russia or the COVID-19 cases in Italy, both of which are from domestic sources. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

A word on the flaws of numbers like this

People are right to ask whether these numbers are at all representative or not for several reasons. First, countries worldwide decide differently on who gets tested for the virus, meaning that comparing case numbers or death rates could to some extent be misleading. Germany, for example, started testing relatively early once the country’s first case was confirmed in Bavaria in January 2020, whereas Italy tests for the coronavirus postmortem. Second, not all people go to see (or can see, due to testing capacity) a doctor when they have mild symptoms. Countries like Norway and the Netherlands, for example, recommend people with non-severe symptoms to just stay at home. This means not all cases are known all the time, which could significantly alter the death rate as it is presented here. Third and finally, numbers like this change very frequently depending on how the pandemic spreads or the national healthcare capacity. It is therefore recommended to look at other (freely accessible) content that dives more into specifics, such as the coronavirus testing capacity in India or the number of hospital beds in the UK. Only with additional pieces of information can you get the full picture, something that this statistic in its current state simply cannot provide.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

Comprehensive time series data on the global spread of COVID-19, detailing the total number of confirmed cases, recoveries, and deaths by location. Compiled by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) in collaboration with the ESRI Living Atlas Team and JHU Applied Physics Lab (APL), this dataset offers insights into the pandemic’s progression across diverse regions and time periods.

Features:

• Date: Specific date of recorded data.
• Country/Region: Geographic location, typically at the country or region level.
• Province/State: Subdivision data, where available (e.g., states in the US, provinces in Canada).
• Confirmed Cases: Cumulative number of confirmed COVID-19 cases.
• Deaths: Cumulative number of deaths attributed to COVID-19.
• Recovered: Cumulative number of recoveries (where available).

Usage:

The dataset is useful for: - Statistical analysis of COVID-19 trends and patterns. - Forecasting models to estimate the spread and potential impact of pandemics. - Visualization projects to map and display the pandemic’s impact over time.

Coverage:

Data is provided globally, covering various countries, regions, and some states/provinces where available. The dataset's granularity allows for comprehensive insights into the pandemic’s reach and severity across different locations.

License:

CC BY 4.0 (Creative Commons)

Who can use it:

Epidemiologists, data scientists, public health researchers, and policy-makers can utilize this dataset for research, statistical modeling, and public health strategy development.

How to use it:

• Conduct historical analyses to understand the progression of COVID-19 cases across regions.
• Build predictive models to forecast infection rates and mortality trends.
• Develop visualization tools (e.g., heat maps, time-series graphs) to illustrate the pandemic's evolution over time.

Iraq IQ: Lifetime Risk of Maternal Death: 1 in: Rate Varies by Country data was reported at 420.000 NA in 2015. This records an increase from the previous number of 410.000 NA for 2014. Iraq IQ: Lifetime Risk of Maternal Death: 1 in: Rate Varies by Country data is updated yearly, averaging 340.000 NA from Dec 1990 (Median) to 2015, with 26 observations. The data reached an all-time high of 420.000 NA in 2015 and a record low of 160.000 NA in 1990. Iraq IQ: Lifetime Risk of Maternal Death: 1 in: Rate Varies by Country data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Iraq – Table IQ.World Bank: Health Statistics. Life time risk of maternal death is the probability that a 15-year-old female will die eventually from a maternal cause assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future, taking into account competing causes of death.; ; WHO, UNICEF, UNFPA, World Bank Group, and the United Nations Population Division. Trends in Maternal Mortality: 1990 to 2015. Geneva, World Health Organization, 2015; Weighted average;

THIS DATASET WAS LAST UPDATED AT 2:11 AM EASTERN ON JULY 12

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

This dataset contains counts of deaths for California counties based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Today, globally, women of childbearing age have an average of approximately 2.2 children over the course of their lifetime. In pre-industrial times, most women could expect to have somewhere between five and ten live births throughout their lifetime; however, the demographic transition then sees fertility rates fall significantly. Looking ahead, it is believed that the global fertility rate will fall below replacement level in the 2050s, which will eventually lead to population decline when life expectancy plateaus. Recent decades Between the 1950s and 1970s, the global fertility rate was roughly five children per woman - this was partly due to the post-WWII baby boom in many countries, on top of already-high rates in less-developed countries. The drop around 1960 can be attributed to China's "Great Leap Forward", where famine and disease in the world's most populous country saw the global fertility rate drop by roughly 0.5 children per woman. Between the 1970s and today, fertility rates fell consistently, although the rate of decline noticeably slowed as the baby boomer generation then began having their own children. Replacement level fertility Replacement level fertility, i.e. the number of children born per woman that a population needs for long-term stability, is approximately 2.1 children per woman. Populations may continue to grow naturally despite below-replacement level fertility, due to reduced mortality and increased life expectancy, however, these will plateau with time and then population decline will occur. It is believed that the global fertility rate will drop below replacement level in the mid-2050s, although improvements in healthcare and living standards will see population growth continue into the 2080s when the global population will then start falling.

From 1950 to 1955, the worldwide crude birth rate was just under 37 births per thousand people, which means that 3.7 percent of the population, who were alive during this time had been born in this five year period. Between this five year period, and the time between 2015 and 2020, the crude birth rate has dropped to 18.5 births per thousand people, which is fifty percent of what the birth rate was seventy years ago. This change has come as a result of increased access and reliability of contraception, a huge reduction in infant and child mortality rate, and increased educational and vocational opportunities for women. The continents that have felt the greatest change over this seventy year period are Asia and Latin America, which fell below the global average in the 1990s and early 2000s, and are estimated to have fallen below the crude birth rate of Oceania in the current five-year period. Europe has consistently had the lowest crude birth rate of all continents during the past seventy years, particularly in the 1990s and 2000s, when it fell to just over ten births per thousand, as the end of communism in Europe caused sweeping demographic change across Europe. The only continent that still remains above the global average is Africa, whose crude birth rate is fifteen births per thousand more than the world average, although the rate of decrease is higher than it was in previous decades.

Brazil, 2020.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretabilty. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datsets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of aquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis:

• Analyze missing data: Project Tycho datasets do not inlcude time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported.
• Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exxclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

COVID19-Algeria-and-World-Dataset

A coronavirus dataset with 104 countries constructed from different reliable sources, where each row represents a country, and the columns represent geographic, climate, healthcare, economic, and demographic factors that may contribute to accelerate/slow the spread of the COVID-19. The assumptions for the different factors are as follows:

• Geography: some continents/areas may be more affected by the disease
• Climate: cold temperatures may promote the spread of the virus
• Healthcare: lack of hospital beds/doctors may lead to more human losses
• Economy: weak economies (GDP) have fewer means to fight the disease
• Demography: older populations may be at higher risk of the disease

The last column represents the number of daily tests performed and the total number of cases and deaths reported each day.

Data description

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Countries in the dataset by geographic coordinates

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• Europe: 33 countries
• Asia: 28 countries
• Africa: 21 countries
• North America: 11 countries
• South America: 8 countries
• Oceania: 3 countries

Statistical description of the data

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Data distribution

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Download

The dataset is available in an encoded CSV form on GitHub.

Python code

The Python Jupyter Notebook to read and visualize the data is available on nbviewer.

Data update

The dataset is updated every month with the latest numbers of COVID-19 cases, deaths, and tests. The last update was on March 01, 2021.

Data construction

The dataset is constructed from different reliable sources, where each row represents a country, and the columns represent geographic, climate, healthcare, economic, and demographic factors that may contribute to accelerate/slow the spread of the coronavirus. Note that we selected only the main factors for which we found data and that other factors can be used. All data were retrieved from the reliable Our World in Data website, except for data on:

• Continents: www.kaggle.com/statchaitya/country-to-continent
• Geographic-coordinates: www.kaggle.com/eidanch/counties-geographic-coordinates
• Temperatures: www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data
• Share of the population over 65 years old: https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS
• GDP/Capita: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD

Citation

If you want to use the dataset please cite the following arXiv paper, more details about the data construction are provided in it.

@article{belkacem_covid-19_2020,
  title = {COVID-19 data analysis and forecasting: Algeria and the world},
  shorttitle = {COVID-19 data analysis and forecasting},
  journal = {arXiv preprint arXiv:2007.09755},
  author = {Belkacem, Sami},
  year = {2020}
}

Contact

If you have any question or suggestion, please contact me at this email address: s.belkacem@usthb.dz

This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

United States US: Lifetime Risk of Maternal Death: 1 in: Rate Varies by Country data was reported at 3,800.000 NA in 2015. This stayed constant from the previous number of 3,800.000 NA for 2014. United States US: Lifetime Risk of Maternal Death: 1 in: Rate Varies by Country data is updated yearly, averaging 3,950.000 NA from Dec 1990 (Median) to 2015, with 26 observations. The data reached an all-time high of 4,700.000 NA in 1998 and a record low of 3,500.000 NA in 2009. United States US: Lifetime Risk of Maternal Death: 1 in: Rate Varies by Country data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Life time risk of maternal death is the probability that a 15-year-old female will die eventually from a maternal cause assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future, taking into account competing causes of death.; ; WHO, UNICEF, UNFPA, World Bank Group, and the United Nations Population Division. Trends in Maternal Mortality: 1990 to 2015. Geneva, World Health Organization, 2015; Weighted average;

Estimates for the total death count of the Second World War generally range somewhere between 70 and 85 million people. The Soviet Union suffered the highest number of fatalities of any single nation, with estimates mostly falling between 22 and 27 million deaths. China then suffered the second greatest, at around 20 million, although these figures are less certain and often overlap with the Chinese Civil War. Over 80 percent of all deaths were of those from Allied countries, and the majority of these were civilians. In contrast, 15 to 20 percent were among the Axis powers, and the majority of these were military deaths, as shown in the death ratios of Germany and Japan. Civilian deaths and atrocities It is believed that 60 to 67 percent of all deaths were civilian fatalities, largely resulting from war-related famine or disease, and war crimes or atrocities. Systematic genocide, extermination campaigns, and forced labor, particularly by the Germans, Japanese, and Soviets, led to the deaths of millions. In this regard, Nazi activities alone resulted in 17 million deaths, including six million Jews in what is now known as The Holocaust. Not only was the scale of the conflict larger than any that had come before, but the nature of and reasoning behind this loss make the Second World War stand out as one of the most devastating and cruelest conflicts in history. Problems with these statistics Although the war is considered by many to be the defining event of the 20th century, exact figures for death tolls have proven impossible to determine, for a variety of reasons. Countries such as the U.S. have fairly consistent estimates due to preserved military records and comparatively few civilian casualties, although figures still vary by source. For most of Europe, records are less accurate. Border fluctuations and the upheaval of the interwar period mean that pre-war records were already poor or non-existent for many regions. The rapid and chaotic nature of the war then meant that deaths could not be accurately recorded at the time, and mass displacement or forced relocation resulted in the deaths of many civilians outside of their homeland, which makes country-specific figures more difficult to find. Early estimates of the war’s fatalities were also taken at face value and formed the basis of many historical works; these were often very inaccurate, but the validity of the source means that the figures continue to be cited today, despite contrary evidence.

In comparison to Europe, estimate ranges are often greater across Asia, where populations were larger but pre-war data was in short supply. Many of the Asian countries with high death tolls were European colonies, and the actions of authorities in the metropoles, such as the diversion of resources from Asia to Europe, led to millions of deaths through famine and disease. Additionally, over one million African soldiers were drafted into Europe’s armies during the war, yet individual statistics are unavailable for most of these colonies or successor states (notably Algeria and Libya). Thousands of Asian and African military deaths went unrecorded or are included with European or Japanese figures, and there are no reliable figures for deaths of millions from countries across North Africa or East Asia. Additionally, many concentration camp records were destroyed, and such records in Africa and Asia were even sparser than in Europe. While the Second World War is one of the most studied academic topics of the past century, it is unlikely that we will ever have a clear number for the lives lost in the conflict.

Context

Late in December 2019, the World Health Organisation (WHO) China Country Office obtained information about severe pneumonia of an unknown cause, detected in the city of Wuhan in Hubei province, China. This later turned out to be the novel coronavirus disease (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) of the coronavirus family. The disease causes respiratory illness characterized by primary symptoms like cough, fever, and in more acute cases, difficulty in breathing. WHO later declared COVID-19 as a Pandemic because of its fast rate of spread across the Globe.

Content

The COVID-19 datasets organized by continent contain daily level information about the COVID-19 cases in the different continents of the world. It is a time-series data and the number of cases on any given day is cumulative. The original datasets can be found on this John Hopkins University Github repository. I will be updating the COVID-19 datasets on a regular basis with every update from John Hopkins University. I have also included the World COVID-19 tests data scraped from Worldometer and 2020 world population also scraped from worldometer.

The datasets

COVID-19 cases covid19_world.csv. It contains the cumulative number of COVID-19 cases from around the world since January 22, 2020, as compiled by John Hopkins University. covid19_asia.csv, covid19_africa.csv, covid19_europe.csv, covid19_northamerica.csv, covid19.southamerica.csv, covid19_oceania.csv, and covid19_others.csv. These contain the cumulative number of COVID-19 cases organized by the continent.

Field description - ObservationDate: Date of observation in YY/MM/DD - Country_Region: name of Country or Region - Province_State: name of Province or State - Confirmed: the number of COVID-19 confirmed cases - Deaths: the number of deaths from COVID-19 - Recovered: the number of recovered cases - Active: the number of people still infected with COVID-19 Note: Active = Confirmed - (Deaths + Recovered)

COVID-19 tests covid19_tests.csv. It contains the cumulative number of COVID tests data from worldometer conducted since the onset of the pandemic. Data available from June 01, 2020.

Field description Date: date in YY/MM/DD Country, Other: Country, Region, or dependency TotalTests: cumulative number of tests up till that date Population: population of Country, Region, or dependency Tests/1M pop: tests per 1 million of the population 1 Testevery X ppl: 1 test for every X number of people

2020 world population world_population(2020).csv. It contains the 2020 world population as reported by woldometer.

Field description Country (or dependency): Country or dependency Population (2020): population in 2020 Yearly Change: yearly change in population as a percentage Net Change: the net change in population Density(P/km2): population density Land Area(km2): land area Migrants(net): net number of migrants Fert. Rate: Fertility Rate Med. Age: median age Urban pop: urban population World Share: share of the world population as a percentage

Acknowledgements

1. John Hopkins University for making COVID-19 datasets available to the public: https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports
2. John Hopkins University COVID-19 Dashboard: https://coronavirus.jhu.edu/map.html
3. COVID-19 Africa dashboard: http://covid-19-africa.sen.ovh/
4. Worldometer: https://www.worldometers.info/
5. United Nations Department of General Assembly and Conference Management: https://www.un.org/depts/DGACM/RegionalGroups.shtml
6. wallpapercave.com: https://wallpapercave.com/covid-19-wallpapers

The world's population first reached one billion people in 1803, and reach eight billion in 2023, and will peak at almost 11 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two thirds of the world's population live in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a decade later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.