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.

Game Of Thrones Deaths Dataset

All of Game Of Thrones Deaths

By Makeover Monday [source]

About this dataset

This file contains data on the deaths that have occurred in the Game of Thrones TV series. The data includes the name of the character who died, how they died, and who was responsible for their death

How to use the dataset

This dataset is a collection of data on the deaths that have occurred in the Game of Thrones TV series. The data includes information on the character, how they died, and who killed them. This dataset can be used to research various aspects of the show, such as which characters are most likely to die, or who is responsible for the most deaths

Research Ideas

• Predicting which characters are most likely to die in future seasons of the show
• Determining which actors/actresses are most likely to be killed off
• Analyzing the impact of major character deaths on viewership

Acknowledgements

Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: game-of-thones-deaths.csv | Column name | Description | |:--------------------------|:--------------------------------------------------------------| | game-of-thones-deaths | The number of deaths in each episode of the series. (Numeric) |

Acknowledgements

If you use this dataset in your research, please credit Makeover Monday.

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.

Mortality Rates for Lake County, Illinois. Explanation of field attributes: Average Age of Death – The average age at which a people in the given zip code die. Cancer Deaths – Cancer deaths refers to individuals who have died of cancer as the underlying cause. This is a rate per 100,000. Heart Disease Related Deaths – Heart Disease Related Deaths refers to individuals who have died of heart disease as the underlying cause. This is a rate per 100,000. COPD Related Deaths – COPD Related Deaths refers to individuals who have died of chronic obstructive pulmonary disease (COPD) as the underlying cause. This is a rate per 100,000.

The leading causes of death by sex and ethnicity in New York City in since 2007. Cause of death is derived from the NYC death certificate which is issued for every death that occurs in New York City.

Note: Note: Starting October 10th, 2025 this dataset is deprecated and is no longer being updated. As of April 27, 2023 updates changed from daily to weekly. Summary The cumulative number of confirmed COVID-19 deaths among Maryland residents by age: 0-9; 10-19; 20-29; 30-39; 40-49; 50-59; 60-69; 70-79; 80+; Unknown. Description The MD COVID-19 - Confirmed Deaths by Age Distribution data layer is a collection of the statewide confirmed COVID-19 related deaths that have been reported each day by the Vital Statistics Administration by designated age ranges. A death is classified as confirmed if the person had a laboratory-confirmed positive COVID-19 test result. Some data on deaths may be unavailable due to the time lag between the death, typically reported by a hospital or other facility, and the submission of the complete death certificate. Probable deaths are available from the MD COVID-19 - Probable Deaths by Age Distribution data layer. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Annual data on death registrations by single year of age for the UK (1974 onwards) and England and Wales (1963 onwards).

The number of deaths registered in England and Wales due to and involving coronavirus (COVID-19). Breakdowns include age, sex, region, local authority, Middle-layer Super Output Area (MSOA), indices of deprivation and place of death. Includes age-specific and age-standardised mortality rates.

This dataset reports the daily reported number of deaths involving COVID-19 by fatality type.

Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak.

Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool

Data includes:

• Date on which the death occurred
• Total number of deaths involving COVID-19
• Number of deaths with “COVID-19 as the underlying cause of death”
• Number of deaths with “COVID-19 contributed but not underlying cause”
• Number of deaths where the “Cause of death unknown” or “Cause of death missing”

Additional Notes

The method used to count COVID-19 deaths has changed, effective December 1, 2022. Prior to December 1 2022, deaths were counted based on the date the death was updated in the public health unit’s system. Going forward, deaths are counted on the date they occurred.

On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023.

CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags.

As of December 1, 2022, data are based on the date on which the death occurred. This reporting method differs from the prior method which is based on net change in COVID-19 deaths reported day over day.

Data are based on net change in COVID-19 deaths for which COVID-19 caused the death reported day over day. Deaths are not reported by the date on which death happened as reporting may include deaths that happened on previous dates.

Spikes, negative numbers and other data anomalies: Due to ongoing data entry and data quality assurance activities in Case and Contact Management system (CCM) file, Public Health Units continually clean up COVID-19, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes, negative numbers and current totals being different from previously reported case and death counts.

Public Health Units report cause of death in the CCM based on information available to them at the time of reporting and in accordance with definitions provided by Public Health Ontario. The medical certificate of death is the official record and the cause of death could be different.

Deaths are defined per the outcome field in CCM marked as “Fatal”. Deaths in COVID-19 cases identified as unrelated to COVID-19 are not included in the number of deaths involving COVID-19 reported.

"_Cause of death unknown_" is the category of death for COVID-19 positive individuals with cause of death still under investigation, or for which the public health unit was unable to determine cause of death. The category may change later when the cause of death is confirmed either as “COVID-19 as the underlying cause of death”, “COVID-19 contributed but not underlying cause,” or “COVID-19 unrelated”.

"_Cause of death missing_" is the category of death for COVID-19 positive individuals with the cause of death missing in CCM.

Rates for the most recent days are subject to reporting lags

All data reflects totals from 8 p.m. the previous day.

This dataset is subject to change.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update.

The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf

Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic.

Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More information on COVID-19 mortality can be found at the following link: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Mortality/Mortality-Statistics

Data are subject to future revision as reporting changes.

Starting in July 2020, this dataset will be updated every weekday.

Additional notes: A delay in the data pull schedule occurred on 06/23/2020. Data from 06/22/2020 was processed on 06/23/2020 at 3:30 PM. The normal data cycle resumed with the data for 06/23/2020.

A network outage on 05/19/2020 resulted in a change in the data pull schedule. Data from 5/19/2020 was processed on 05/20/2020 at 12:00 PM. Data from 5/20/2020 was processed on 5/20/2020 8:30 PM. The normal data cycle resumed on 05/20/2020 with the 8:30 PM data pull. As a result of the network outage, the timestamp on the datasets on the Open Data Portal differ from the timestamp in DPH's daily PDF reports.

Starting 5/10/2021, the date field will represent the date this data was updated on data.ct.gov. Previously the date the data was pulled by DPH was listed, which typically coincided with the date before the data was published on data.ct.gov. This change was made to standardize the COVID-19 data sets on data.ct.gov.

Mortality Statistics in US Cities

Deaths by Age and Cause of Death in 2016

By Health [source]

About this dataset

This dataset contains mortality statistics for 122 U.S. cities in 2016, providing detailed information about all deaths that occurred due to any cause, including pneumonia and influenza. The data is voluntarily reported from cities with populations of 100,000 or more, and it includes the place of death and the week during which the death certificate was filed. Data is provided broken down by age group and includes a flag indicating the reliability of each data set to help inform analysis. Each row also provides longitude and latitude information for each reporting area in order to make further analysis easier. These comprehensive mortality statistics are invaluable resources for tracking disease trends as well as making comparisons between different areas across the country in order to identify public health risks quickly and effectively

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains mortality rates for 122 U.S. cities in 2016, including deaths by age group and cause of death. The data can be used to study various trends in mortality and contribute to the understanding of how different diseases impact different age groups across the country.

In order to use the data, firstly one has to identify which variables they would like to use from this dataset. These include: reporting area; MMWR week; All causes by age greater than 65 years; All causes by age 45-64 years; All causes by age 25-44 years; All causes by age 1-24 years; All causes less than 1 year old; Pneumonia and Influenza total fatalities; Location (1 & 2); flag indicating reliability of data.

Once you have identified the variables that you are interested in,you will need to filter the dataset so that it only includes relevant information for your analysis or research purposes. For example, if you are looking at trends between different ages, then all you would need is information on those 3 specific cause groups (greater than 65, 45-64 and 25-44). You can do this using a selection tool that allows you to pick only certain columns from your data set or an excel filter tool if your data is stored as a csv file type .

Next step is preparing your data - it’s important for efficient analysis also helpful when there are too many variables/columns which can confuse our analysis process – eliminate unnecessary columns, rename column labels where needed etc ... In addition , make sure we clean up any missing values / outliers / incorrect entries before further investigation .Remember , outliers or corrupt entries may lead us into incorrect conclusions upon analyzing our set ! Once we complete the cleaning steps , now its safe enough transit into drawing insights !

The last step involves using statistical methods such as linear regression with multiple predictors or descriptive statistical measures such as mean/median etc ..to draw key insights based on analysis done so far and generate some actionable points !

With these steps taken care off , now its easier for anyone who decides dive into another project involving this particular dataset with added advantage formulated out of existing work done over our previous investigations!

Research Ideas

• Creating population health profiles for cities in the U.S.
• Tracking public health trends across different age groups
• Analyzing correlations between mortality and geographical locations

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: rows.csv | Column name | Description | |:--------------------------------------------|:-----------------------------------...

Provisional counts of the number of deaths registered in England and Wales, by age, sex, region and Index of Multiple Deprivation (IMD), in the latest weeks for which data are available.

This dataset provides values for CORONAVIRUS DEATHS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Provisional deaths registration data for single year of age and average age of death (median and mean) of persons whose death involved coronavirus (COVID-19), England and Wales. Includes deaths due to COVID-19 and breakdowns by sex.

Note: This COVID-19 data set is no longer being updated as of December 1, 2023. Access current COVID-19 data on the CDPH respiratory virus dashboard (https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/Respiratory-Viruses/RespiratoryDashboard.aspx) or in open data format (https://data.chhs.ca.gov/dataset/respiratory-virus-dashboard-metrics).

As of August 17, 2023, data is being updated each Friday.

For death data after December 31, 2022, California uses Provisional Deaths from the Center for Disease Control and Prevention’s National Center for Health Statistics (NCHS) National Vital Statistics System (NVSS). Prior to January 1, 2023, death data was sourced from the COVID-19 registry. The change in data source occurred in July 2023 and was applied retroactively to all 2023 data to provide a consistent source of death data for the year of 2023.

As of May 11, 2023, data on cases, deaths, and testing is being updated each Thursday. Metrics by report date have been removed, but previous versions of files with report date metrics are archived below.

All metrics include people in state and federal prisons, US Immigration and Customs Enforcement facilities, US Marshal detention facilities, and Department of State Hospitals facilities. Members of California's tribal communities are also included.

The "Total Tests" and "Positive Tests" columns show totals based on the collection date. There is a lag between when a specimen is collected and when it is reported in this dataset. As a result, the most recent dates on the table will temporarily show NONE in the "Total Tests" and "Positive Tests" columns. This should not be interpreted as no tests being conducted on these dates. Instead, these values will be updated with the number of tests conducted as data is received.

Effect of suicide rates on life expectancy dataset

Abstract
In 2015, approximately 55 million people died worldwide, of which 8 million committed suicide. In the USA, one of the main causes of death is the aforementioned suicide, therefore, this experiment is dealing with the question of how much suicide rates affects the statistics of average life expectancy.
The experiment takes two datasets, one with the number of suicides and life expectancy in the second one and combine data into one dataset. Subsequently, I try to find any patterns and correlations among the variables and perform statistical test using simple regression to confirm my assumptions.

Data

The experiment uses two datasets - WHO Suicide Statistics[1] and WHO Life Expectancy[2], which were firstly appropriately preprocessed. The final merged dataset to the experiment has 13 variables, where country and year are used as index: Country, Year, Suicides number, Life expectancy, Adult Mortality, which is probability of dying between 15 and 60 years per 1000 population, Infant deaths, which is number of Infant Deaths per 1000 population, Alcohol, which is alcohol, recorded per capita (15+) consumption, Under-five deaths, which is number of under-five deaths per 1000 population, HIV/AIDS, which is deaths per 1 000 live births HIV/AIDS, GDP, which is Gross Domestic Product per capita, Population, Income composition of resources, which is Human Development Index in terms of income composition of resources, and Schooling, which is number of years of schooling.

LICENSE

THE EXPERIMENT USES TWO DATASET - WHO SUICIDE STATISTICS AND WHO LIFE EXPECTANCY, WHICH WERE COLLEECTED FROM WHO AND UNITED NATIONS WEBSITE. THEREFORE, ALL DATASETS ARE UNDER THE LICENSE ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 3.0 IGO (https://creativecommons.org/licenses/by-nc-sa/3.0/igo/).

[1] https://www.kaggle.com/szamil/who-suicide-statistics

[2] https://www.kaggle.com/kumarajarshi/life-expectancy-who

This dataset contains counts of deaths for California residents by ZIP Code based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths of California residents. The data tables include deaths of residents of California by ZIP Code of residence (by residence). The data are reported as totals, as well as stratified by age and gender. 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.

Number of deaths and age-specific mortality rates for selected grouped causes, by age group and sex, 2000 to most recent year.

Daily updates of Covid-19 Global Excess Deaths from the Economist's GitHub repository: https://github.com/TheEconomist/covid-19-the-economist-global-excess-deaths-model

Interpreting estimates

Estimating excess deaths for every country every day since the pandemic began is a complex and difficult task. Rather than being overly confident in a single number, limited data means that we can often only give a very very wide range of plausible values. Focusing on central estimates in such cases would be misleading: unless ranges are very narrow, the 95% range should be reported when possible. The ranges assume that the conditions for bootstrap confidence intervals are met. Please see our tracker page and methodology for more information.

New variants

The Omicron variant, first detected in southern Africa in November 2021, appears to have characteristics that are different to earlier versions of sars-cov-2. Where this variant is now dominant, this change makes estimates uncertain beyond the ranges indicated. Other new variants may do the same. As more data is incorporated from places where new variants are dominant, predictions improve.

Non-reporting countries

Turkmenistan and the Democratic People's Republic of Korea have not reported any covid-19 figures since the start of the pandemic. They also have not published all-cause mortality data. Exports of estimates for the Democratic People's Republic of Korea have been temporarily disabled as it now issues contradictory data: reporting a significant outbreak through its state media, but zero confirmed covid-19 cases/deaths to the WHO.

Acknowledgements

A special thanks to all our sources and to those who have made the data to create these estimates available. We list all our sources in our methodology. Within script 1, the source for each variable is also given as the data is loaded, with the exception of our sources for excess deaths data, which we detail in on our free-to-read excess deaths tracker as well as on GitHub. The gradient booster implementation used to fit the models is aGTBoost, detailed here.

Calculating excess deaths for the entire world over multiple years is both complex and imprecise. We welcome any suggestions on how to improve the model, be it data, algorithm, or logic. If you have one, please open an issue.

The Economist would also like to acknowledge the many people who have helped us refine the model so far, be it through discussions, facilitating data access, or offering coding assistance. A special thanks to Ariel Karlinsky, Philip Schellekens, Oliver Watson, Lukas Appelhans, Berent Å. S. Lunde, Gideon Wakefield, Johannes Hunger, Carol D'Souza, Yun Wei, Mehran Hosseini, Samantha Dolan, Mollie Van Gordon, Rahul Arora, Austin Teda Atmaja, Dirk Eddelbuettel and Tom Wenseleers.

All coding and data collection to construct these models (and make them update dynamically) was done by Sondre Ulvund Solstad. Should you have any questions about them after reading the methodology, please open an issue or contact him at sondresolstad@economist.com.

Suggested citation The Economist and Solstad, S. (corresponding author), 2021. The pandemic’s true death toll. [online] The Economist. Available at: https://www.economist.com/graphic-detail/coronavirus-excess-deaths-estimates [Accessed ---]. First published in the article "Counting the dead", The Economist, issue 20, 2021.

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.