In 1919, there were almost 13 deaths from measles per 100,000 population in the United States. However, this rate had dropped to zero by the year 2021. In early 2025, an outbreak of measles in Texas resulted in the death of a child. This was the first measles death in the United States since 2015. Measles is a highly contagious disease, that is especially dangerous for children. However, vaccines have significantly decreased the rate of cases and deaths in the United States.

In the year 2000, there were around 3,397 deaths due to measles in Europe. This number dropped to just 222 in 2023. This statistic shows the estimated number of deaths due to measles worldwide in 2000 and 2023, by region.

There were 285 new cases of measles in the U.S. in 2024. Measles, also known as rubeola, is an infectious disease that is highly contagious and affects mostly children. Common symptoms of measles include fever, runny nose, sore throat, cough, and a rash. Although death rates from measles have decreased around the world, it is still responsible for around 81,000 deaths worldwide per year. Measles vaccination The main reason for the decrease in measles cases and deaths is due to high vaccination rates. The widely used MMR vaccine protects against measles, mumps, and rubella and is safe and effective. In 2023, around 91 percent of adolescents in the U.S. aged 13 to 17 years had received an MMR vaccination. However, in recent years there has been a rise in measles cases in many parts of the world due to vaccine hesitancy. Vaccine hesitancy Vaccine hesitancy refers to a refusal or reluctance to have children vaccinated, despite the overwhelming evidence that vaccines are safe and effective. This hesitancy comes from a misunderstanding of the ingredients in vaccines and how they work, a mistrust of doctors and pharmaceutical companies, and belief in the unfounded associations of vaccines with other diseases and disorders.

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".

From 2000 to 2023, measles mortality in Africa reduced by 79 percent. Over this period, every region saw a decrease in measles mortality. This statistic shows the percentage change in measles mortality worldwide from 2000 to 2023, by region.

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".

BackgroundDespite the comprehensive World Health Organization (WHO)/United Nations Children's Fund (UNICEF) measles mortality–reduction strategy and the Measles Initiative, a partnership of international organizations supporting measles mortality reduction in Africa, certain high-burden countries continue to face recurrent epidemics. To our knowledge, few recent studies have documented measles mortality in sub-Saharan Africa. The objective of our study was to investigate measles mortality in three recent epidemics in Niamey (Niger), N'Djamena (Chad), and Adamawa State (Nigeria). Methods and FindingsWe conducted three exhaustive household retrospective mortality surveys in one neighbourhood of each of the three affected areas: Boukoki, Niamey, Niger (April 2004, n = 26,795); Moursal, N'Djamena, Chad (June 2005, n = 21,812); and Dong District, Adamawa State, Nigeria (April 2005, n = 16,249), where n is the total surveyed population in each of the respective areas. Study populations included all persons resident for at least 2 wk prior to the study, a duration encompassing the measles incubation period. Heads of households provided information on measles cases, clinical outcomes up to 30 d after rash onset, and health-seeking behaviour during the epidemic. Measles cases and deaths were ascertained using standard WHO surveillance-case definitions. Our main outcome measures were measles attack rates (ARs) and case fatality ratios (CFRs) by age group, and descriptions of measles complications and health-seeking behaviour. Measles ARs were the highest in children under 5 y old (under 5 y): 17.1% in Boukoki, 17.2% in Moursal, and 24.3% in Dong District. CFRs in under 5-y-olds were 4.6%, 4.0%, and 10.8% in Boukoki, Moursal, and Dong District, respectively. In all sites, more than half of measles cases in children aged under 5 y experienced acute respiratory infection and/or diarrhoea in the 30 d following rash onset. Of measles cases, it was reported that 85.7% (979/1,142) of patients visited a health-care facility within 30 d after rash onset in Boukoki, 73.5% (519/706) in Moursal, and 52.8% (603/1,142) in Dong District. ConclusionsChildren in these countries still face unacceptably high mortality from a completely preventable disease. While the successes of measles mortality–reduction strategies and progress observed in measles control in other countries of the region are laudable and evident, they should not overshadow the need for intensive efforts in countries that have just begun implementation of the WHO/UNICEF comprehensive strategy.

In 2024, there were a total of 285 cases of measles in the United States, with 120 of these cases among children aged under five years. From January 1 to April 3, 2025, there were 607 cases of measles. There were also two reported deaths from the disease during this time, the first since 2015. Measles is a highly contagious disease that can be especially dangerous for young children. Vaccines against measles resulted in a significant decrease in cases in the United States over the last few decades; however, increasing vaccine hesitancy and skepticism has been blamed for recent outbreaks.

The death rate in Indonesia saw no significant changes in 2023 in comparison to the previous year 2022 and remained at around 7.53 deaths per 1,000 inhabitants. The crude death rate refers to the number of deaths in a given year, expressed per 1,000 population. When studied in combination with the crude birth rate, the rate of natural population increase can be determined.Find more statistics on other topics about Indonesia with key insights such as rate of children immunized against measles in the age group of 12 to 23 months, infant mortality rate, share of children aged 12-23 months immunized against diphtheria, and pertussis and tetanus (DPT).

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.

The data shows the percentage of Infant deaths due to Measles to the total reported Infant deaths

In 2022, the death rate in Indonesia decreased by 0.5 deaths per 1,000 inhabitants (-4.97 percent) compared to 2021. Nevertheless, the last two years recorded a significantly higher death rate than the preceding years.The crude death rate is the annual number of deaths in a given population, expressed per 1,000 people. When looked at in unison with the crude birth rate, the rate of natural increase can be determined.Find more statistics on other topics about Indonesia with key insights such as rate of children immunized against measles in the age group of 12 to 23 months, infant mortality rate, share of children aged 12-23 months immunized against diphtheria, and pertussis and tetanus (DPT).

The infant mortality rate in Indonesia decreased to 17 deaths per 1,000 live births compared to the previous year. As a result, the infant mortality rate in Indonesia saw its lowest number in 2023 with 17 deaths per 1,000 live births. The infant mortality rate is the number of newborns who do not survive past the first 12 months of life. This is generally expressed as a value per 1,000 live births, and also includes neonatal mortality (deaths within the first 28 days of life).Find more statistics on other topics about Indonesia with key insights such as fertility rate of women aged between 15 and 19 years old, death rate, and rate of children immunized against measles in the age group of 12 to 23 months.

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".

The association between armed conflict and maternal and child mortality (adjusted beta coefficients, 95% confidence intervals).

Number of cases and deaths during the 1916–17 measles epidemicb'*' [65,66].

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".

Description of data used in the study.

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".

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".