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Excess mortality: Deaths from all causes compared to projection

What you should know about this indicator

  • Excess deaths is estimated as Excess deaths = Number of reported deaths - Number of expected deaths. Excess mortality goes beyond confirmed COVID-19 fatalities by capturing all deaths above a projected baseline, including indirect deaths from pandemic-related disruptions.
  • All-cause mortality data is from the Human Mortality Database (HMD) Short-term Mortality Fluctuations project and the World Mortality Dataset (WMD). Both sources are updated weekly.
  • We use the baseline estimates by Ariel Karlinsky and Dmitry Kobak (2021) as part of their World Mortality Dataset (WMD).
  • We do not use the data from some countries in WMD because they fail to meet the following data quality criteria: 1) at least three years of historical data; and 2) data published either weekly or monthly. The full list of excluded countries and reasons for exclusion can be found in this spreadsheet.

Additional information about this data

All-cause mortality data is from the Human Mortality Database (HMD) Short-term Mortality Fluctuations project and the World Mortality Dataset (WMD). Both sources are updated weekly.

We do not use the data from some countries in WMD because they fail to meet the following data quality criteria: 1) at least three years of historical data; and 2) data published either weekly or monthly. The full list of excluded countries and reasons for exclusion can be found in this spreadsheet: https://docs.google.com/spreadsheets/d/1JPMtzsx-smO3_K4ReK_HMeuVLEzVZ71qHghSuAfG788/edit?usp=sharing.

For a full list of source information (i.e., HMD or WMD) country by country, see: https://ourworldindata.org/excess-mortality-covid#source-information-country-by-country.

We calculate P-scores using the reported deaths data from HMD and WMD and the projected deaths since 2020 from WMD (which we use for all countries and regions, including for deaths broken down by age group). The P-score is the percentage difference between the reported number of weekly or monthly deaths since 2020 and the projected number of deaths for the same period based on previous years (years available from 2015 until 2019).

We calculate the number of weekly deaths for the United Kingdom by summing the weekly deaths from England and Wales, Scotland, and Northern Ireland.

For important issues and caveats to understand when interpreting excess mortality data, see our excess mortality page at https://ourworldindata.org/excess-mortality-covid.

For a more detailed description_short of the HMD data, including week date definitions, the coverage (of individuals, locations, and time), whether dates are for death occurrence or registration, the original national source information, and important caveats, see the HMD metadata file at https://www.mortality.org/Public/STMF_DOC/STMFmetadata.pdf.

For a more detailed description_short of the WMD data, including original source information, see their GitHub page at https://github.com/akarlinsky/world_mortality. In response to the COVID-19 pandemic, the HMD team decided to establish a new data resource: Short-term Mortality Fluctuations (STMF) data series. Objective and internationally comparable data are crucial to determine the effectiveness of different strategies used to address epidemics. Weekly death counts provide the most objective and comparable way of assessing the scale of short-term mortality elevations across countries and time. More details about this data project can be found in the recently published paper (https://www.nature.com/articles/s41597-021-01019-1).

Before using the data, please consult the STMF Methodological Note (https://www.mortality.org/File/GetDocument/Public/STMF_DOC/STMFNote.pdf), which provides a more comprehensive description of this data project, including important aspects related to data collection and data processing. We also recommend that you read the STMF Metadata (https://www.mortality.org/File/GetDocument/Public/STMF_DOC/STMFmetadata.pdf). This document includes country-specific information about data availability, completeness, data sources, as well as specific features of included data.

Data will be frequently updated and new countries will be added. Data are published under CC BY 4.0 license.

For citing STMF data, please follow the HMD data citation guidelines (https://www.mortality.org/Research/CitationGuidelines).

HMD provides an online STMF visualization toolkit (https://mpidr.shinyapps.io/stmortality). World Mortality Dataset: international data on all-cause mortality.

This dataset contains country-level data on all-cause mortality in 2015–2024 collected from various sources. They are currently providing data for 122 countries and territories.

For a complete and up-to-date list of notes on the dataset, please refer to their GitHub page at https://github.com/akarlinsky/world_mortality/.

For the list of sources that they use, please go to https://github.com/akarlinsky/world_mortality/#sou rces.

Published paper available at https://elifesciences.org/articles/69336. The data are sourced from the World Mortality Dataset (https://github.com/akarlinsky/world_mortality). Excess mortality is computed relative to the baseline obtained using linear extrapolation of the 2015–19 trend (different baselines for 2020, 2021, and 2022). In each subplot in the figure below, gray lines are 2015–19, black line is baseline for 2020, red line is 2020, blue line is 2021, orange line is 2022. Countries are sorted by the total excess mortality as % of the 2020 baseline.

For more details, refer to https://github.com/dkobak/excess-mortality#excess-mortality-during-the-covid-19-pandemic.

Excess mortality: Deaths from all causes compared to projection
P-scores using projected baseline for all ages
Source
Human Mortality Database; World Mortality Dataset (2024); Karlinsky and Kobak (2021); Human Mortality Database (2025); World Mortality Database (2024); Karlinsky & Kobak (2024) – processed by Our World in Data
Last updated
August 20, 2024
Unit
%
Links
https://www.mortality.org/Data/STMF
https://github.com/akarlinsky/world_mortality/
https://github.com/dkobak/excess-mortality

Related research and writing

Excess mortality during the Coronavirus pandemic (COVID-19)

Edouard Mathieu, Hannah Ritchie, Lucas Rodés-Guirao, Cameron Appel, Daniel Gavrilov, Charlie Giattino, Joe Hasell, Bobbie Macdonald, Saloni Dattani, Diana Beltekian, Esteban Ortiz-Ospina, and Max Roser

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Sources and processing

This data is based on the following sources

Human Mortality Database; World Mortality Dataset (2024); Karlinsky and Kobak (2021)

Data published by

HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org.; Karlinsky & Kobak 2021, Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset, eLife https://doi.org/10.7554/eLife.69336; Karlinsky & Kobak, 2021, Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset. eLife 10:e69336. https://elifesciences.org/articles/69336

Retrieved on
March 31, 2026
Retrieved from
https://www.mortality.org/Data/STMF
https://github.com/akarlinsky/world_mortality/
https://github.com/dkobak/excess-mortality

Human Mortality Database

Retrieved on
August 20, 2024
Retrieved from
https://www.mortality.org
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org.
Retrieved on
August 20, 2024
Retrieved from
https://www.mortality.org
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org.

World Mortality Database

Retrieved on
August 20, 2024
Retrieved from
https://github.com/akarlinsky/world_mortality
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Karlinsky & Kobak 2021, Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset, eLife https://doi.org/10.7554/eLife.69336
Retrieved on
August 20, 2024
Retrieved from
https://github.com/akarlinsky/world_mortality
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Karlinsky & Kobak 2021, Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset, eLife https://doi.org/10.7554/eLife.69336

Karlinsky & Kobak – Excess mortality during the COVID-19 pandemic

Retrieved on
August 20, 2024
Retrieved from
https://github.com/dkobak/excess-mortality
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Karlinsky & Kobak, 2021, Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset. eLife 10:e69336. https://elifesciences.org/articles/69336.
Retrieved on
August 20, 2024
Retrieved from
https://github.com/dkobak/excess-mortality
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Karlinsky & Kobak, 2021, Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset. eLife 10:e69336. https://elifesciences.org/articles/69336.

How we process data at Our World in Data

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Read about our data pipeline

Reuse this work

  • All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
  • All data, visualizations, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Excess mortality: Deaths from all causes compared to projection”. Our World in Data (2026). Data adapted from Human Mortality Database, World Mortality Database, Karlinsky & Kobak. Retrieved from https://archive.ourworldindata.org/20260331-072833/grapher/excess-mortality-p-scores-projected-baseline.html [online resource] (archived on March 31, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Human Mortality Database; World Mortality Dataset (2024); Karlinsky and Kobak (2021) and other sources – processed by Our World in Data

Full citation

Human Mortality Database; World Mortality Dataset (2024); Karlinsky and Kobak (2021); Human Mortality Database (2025); World Mortality Database (2024); Karlinsky & Kobak (2024) – processed by Our World in Data. “Excess mortality: Deaths from all causes compared to projection” [dataset]. Human Mortality Database, “Human Mortality Database”; World Mortality Database, “World Mortality Database”; Karlinsky & Kobak, “Excess mortality during the COVID-19 pandemic” [original data]. Retrieved April 1, 2026 from https://archive.ourworldindata.org/20260331-072833/grapher/excess-mortality-p-scores-projected-baseline.html (archived on March 31, 2026).

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Download the data shown in this chart as a ZIP file containing a CSV file, metadata in JSON format, and a README. The CSV file can be opened in Excel, Google Sheets, and other data analysis tools.

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Data API

Use these URLs to programmatically access this chart's data and configure your requests with the options below. Our documentation provides more information on how to use the API, and you can find a few code examples below.

Data URL (CSV format)
https://ourworldindata.org/grapher/excess-mortality-p-scores-projected-baseline.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/excess-mortality-p-scores-projected-baseline.metadata.json?v=1&csvType=full&useColumnShortNames=false

Code examples

Examples of how to load this data into different data analysis tools.

Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/excess-mortality-p-scores-projected-baseline.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://ourworldindata.org/grapher/excess-mortality-p-scores-projected-baseline.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

# Fetch the metadata
metadata = requests.get("https://ourworldindata.org/grapher/excess-mortality-p-scores-projected-baseline.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/excess-mortality-p-scores-projected-baseline.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/excess-mortality-p-scores-projected-baseline.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/excess-mortality-p-scores-projected-baseline.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear