The COVID-19 pandemic increased the global death rate, reaching *** in 2021, but had little to no significant impact on birth rates, causing population growth to dip slightly. On a global level, population growth is determined by the difference between the birth and death rates, known as the rate of natural change. On a national or regional level, migration also affects population change. Ongoing trends Since the middle of the 20th century, the global birth rate has been well above the global death rate; however, the gap between these figures has grown closer in recent years. The death rate is projected to overtake the birth rate in the 2080s, which means that the world's population will then go into decline. In the future, death rates will increase due to ageing populations across the world and a plateau in life expectancy. Why does this change? There are many reasons for the decline in death and birth rates in recent decades. Falling death rates have been driven by a reduction in infant and child mortality, as well as increased life expectancy. Falling birth rates were also driven by the reduction in child mortality, whereby mothers would have fewer children as survival rates rose - other factors include the drop in child marriage, improved contraception access and efficacy, and women choosing to have children later in life.

The average for 2022 based on 196 countries was 18.19 births per 1000 people. The highest value was in the Central African Republic: 45.42 births per 1000 people and the lowest value was in Hong Kong: 4.4 births per 1000 people. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

Niger had the highest birth rate in the world in 2024, with a birth rate of 46.6 births per 1,000 inhabitants. Angola, Benin, Mali, and Uganda followed. Except for Afghanistan, all 20 countries with the highest birth rates in the world were located in Sub-Saharan Africa. High infant mortality The reasons behind the high birth rates in many Sub-Saharan African countries are manyfold, but a major reason is that infant mortality remains high on the continent, despite decreasing steadily over the past decades, resulting in high birth rates to counter death rates. Moreover, many nations in Sub-Saharan Africa are highly reliant on small-scale farming, meaning that more hands are of importance. Additionally, polygamy is not uncommon in the region, and having many children is often seen as a symbol of status. Fastest-growing populations As the high fertility rates coincide with decreasing death rates, countries in Sub-Saharan Africa have the highest population growth rates in the world. As a result, Africa's population is forecast to increase from 1.4 billion in 2022 to over 3.9 billion by 2100.

Real-time data on births per day worldwide

In 2023, there were five countries, where the average woman of childbearing age can expect to have over six children throughout their lifetime. In fact, of the 20 countries in the world with the highest fertility rates, Afghanistan and Yemen are the only countries not found in Sub-Saharan Africa. High fertility rates in Africa With a fertility rate of 6.13 and 6.12 children per woman, Somalia and Chad were the countries with the highest fertility rate in the world. Population growth in Chad is among the highest in the world. Lack of healthcare access, as well as food instability, political instability, and climate change, are all exacerbating conditions that keep Chad's infant mortality rates high, which is generally the driver behind high fertility rates. This situation is common across much of the continent, and, although there has been considerable progress in recent decades, development in Sub-Saharan Africa is not moving as quickly as it did in other regions. Demographic transition While these countries have the highest fertility rates in the world, their rates are all on a generally downward trajectory due to a phenomenon known as the demographic transition. The third stage (of five) of this transition sees birth rates drop in response to decreased infant and child mortality, as families no longer feel the need to compensate for lost children. Eventually, fertility rates fall below replacement level (approximately 2.1 children per woman), which eventually leads to natural population decline once life expectancy plateaus. In some of the most developed countries today, low fertility rates are creating severe econoic and societal challenges as workforces are shrinking while aging populations are placin a greater burden on both public and personal resources.

Number and percentage of live births, by month of birth, 1991 to most recent year.

The statistic shows the 20 countries with the lowest fertility rates in 2024. All figures are estimates. In 2024, the fertility rate in Taiwan was estimated to be at 1.11 children per woman, making it the lowest fertility rate worldwide. Fertility rate The fertility rate is the average number of children born per woman of child-bearing age in a country. Usually, a woman aged between 15 and 45 is considered to be in her child-bearing years. The fertility rate of a country provides an insight into its economic state, as well as the level of health and education of its population. Developing countries usually have a higher fertility rate due to lack of access to birth control and contraception, and to women usually foregoing a higher education, or even any education at all, in favor of taking care of housework. Many families in poorer countries also need their children to help provide for the family by starting to work early and/or as caretakers for their parents in old age. In developed countries, fertility rates and birth rates are usually much lower, as birth control is easier to obtain and women often choose a career before becoming a mother. Additionally, if the number of women of child-bearing age declines, so does the fertility rate of a country. As can be seen above, countries like Hong Kong are a good example for women leaving the patriarchal structures and focusing on their own career instead of becoming a mother at a young age, causing a decline of the country’s fertility rate. A look at the fertility rate per woman worldwide by income group also shows that women with a low income tend to have more children than those with a high income. The United States are neither among the countries with the lowest, nor among those with the highest fertility rate, by the way. At 2.08 children per woman, the fertility rate in the US has been continuously slightly below the global average of about 2.4 children per woman over the last decade.

Vital Statistics: Birth Rate: per 1000 Population: Uttar Pradesh data was reported at 25.100 NA in 2020. This records a decrease from the previous number of 25.400 NA for 2019. Vital Statistics: Birth Rate: per 1000 Population: Uttar Pradesh data is updated yearly, averaging 28.700 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 32.800 NA in 2000 and a record low of 25.100 NA in 2020. Vital Statistics: Birth Rate: per 1000 Population: Uttar Pradesh data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH002: Vital Statistics: Birth Rate: by States.

Vital Statistics: Birth Rate: per 1000 Population: West Bengal data was reported at 14.600 NA in 2020. This records a decrease from the previous number of 14.900 NA for 2019. Vital Statistics: Birth Rate: per 1000 Population: West Bengal data is updated yearly, averaging 17.200 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 21.300 NA in 1998 and a record low of 14.600 NA in 2020. Vital Statistics: Birth Rate: per 1000 Population: West Bengal data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH002: Vital Statistics: Birth Rate: by States.

Vital Statistics: Birth Rate: per 1000 Population: Punjab data was reported at 14.300 NA in 2020. This records a decrease from the previous number of 14.500 NA for 2019. Vital Statistics: Birth Rate: per 1000 Population: Punjab data is updated yearly, averaging 17.000 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 22.400 NA in 1998 and a record low of 14.300 NA in 2020. Vital Statistics: Birth Rate: per 1000 Population: Punjab data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH002: Vital Statistics: Birth Rate: by States.

Real-time data on deaths per day worldwide

Vital Statistics: Birth Rate: per 1000 Population: Telangana data was reported at 16.400 NA in 2020. This records a decrease from the previous number of 16.700 NA for 2019. Vital Statistics: Birth Rate: per 1000 Population: Telangana data is updated yearly, averaging 17.200 NA from Dec 2014 (Median) to 2020, with 7 observations. The data reached an all-time high of 18.000 NA in 2014 and a record low of 16.400 NA in 2020. Vital Statistics: Birth Rate: per 1000 Population: Telangana data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH002: Vital Statistics: Birth Rate: by States.

Vital Statistics: Birth Rate: per 1000 Population: Gujarat data was reported at 19.300 NA in 2020. This records a decrease from the previous number of 19.500 NA for 2019. Vital Statistics: Birth Rate: per 1000 Population: Gujarat data is updated yearly, averaging 22.300 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 25.500 NA in 1998 and a record low of 19.300 NA in 2020. Vital Statistics: Birth Rate: per 1000 Population: Gujarat data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH002: Vital Statistics: Birth Rate: by States.

Over the past decade, the birth rate in Italy has constantly decreased – in 2024, 6.3 children were estimated to be born per 1,000 inhabitants, three infants less than in 2002. The region with the highest birth rate in the country was Trentino-South Tyrol, where 7.6 children were born per 1,000 residents. Italian mothers are older and older Similar to citizens of other European countries, Italians also postpone parenthood to a later age. While the average age of an Italian mother at childbirth in the 1990s was 29.9 years, in 2024 females giving birth were roughly 32.6 years. Italy, a country with one of the lowest fertility rates in the world If compared with the fertility rates around the world, Italy was one of the 20 countries which registered the lowest fertility rate in 2024. The leader of the global ranking was Taiwan, where only 1.11 babies were born per woman.

For most of human history, pregnancy and childbirth were very risky; mothers would die in at least 1 in 100 pregnancies.1

Since the average woman would have at least four or five children, the lifetime risk of dying from maternal causes would be at least 1 in 25.2 This was true everywhere.

Thankfully, that’s no longer the case. We’ve made huge strides in not only protecting infants in childbirth and the early stages of their lives, but we’ve also made it much safer for women.

But we’re not done yet. There are still huge inequalities in the risks of pregnancy across the world. Pregnant women in countries like Sierra Leone and Kenya are around 100 times more likely to die during pregnancy or childbirth than those in countries like Norway, Sweden, or Germany.3 But it doesn’t have to be this way. We could save hundreds of thousands of lives a year by closing these gaps.

I’ve compared three scenarios in the chart below to clarify these points.

First, we can see that the situation today is awful. 286,000 women died from maternal causes in 2020.4 That’s 784 deaths per day on average, or one mother dying every two minutes.5

Second, we can consider the very high maternal mortality rates of the past. Particularly good long-term data is available for Finland or Sweden, which shows that in 1750, around 900 women died per 100,000 live births.6 Since there were 135 million births in 2020, I calculate that 1.2 million women would have died from maternal causes that year if these rates hadn’t improved.7 Things are much, much better than they used to be.

Finally, things can still be much better. We know this because some countries have maternal mortality rates that are far lower than the global average. And they all used to be in a similar position to the worst-off countries today. In Europe, the maternal mortality rate was 8 deaths per 100,000 live births in 2020. That’s around 25 times lower than the global average.8 If all countries could achieve the same outcomes as Europe, 11,000 women would have died from maternal causes in 2020 — a small fraction of the 286,000 deaths that occurred.9

Providing the best conditions for women everywhere would reduce the global death toll by 275,000 maternal deaths a year.

Vital Statistics: Birth Rate: per 1000 Population: Haryana data was reported at 19.900 NA in 2020. This records a decrease from the previous number of 20.100 NA for 2019. Vital Statistics: Birth Rate: per 1000 Population: Haryana data is updated yearly, averaging 23.000 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 27.600 NA in 1998 and a record low of 19.900 NA in 2020. Vital Statistics: Birth Rate: per 1000 Population: Haryana data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH002: Vital Statistics: Birth Rate: by States.

The average for 2022 based on 196 countries was 8.24 deaths per 1000 people. The highest value was in the Central African Republic: 55.13 deaths per 1000 people and the lowest value was in Qatar: 0.93 deaths per 1000 people. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

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.

In the United States, the crude birth rate in 1800 was 48.3 live births per thousand people, meaning that 4.8 percent of the population had been born in that year. Between 1815 and 1825 the crude birth rate jumped from 46.5 to 54.7 (possibly due to Florida becoming a part of the US, but this is unclear), but from this point until the Second World War the crude birth rate dropped gradually, reaching 19.2 in 1935. Through the 1940s, 50s and 60s the US experienced it's baby boom, and the birth rate reached 24.1 in 1955, before dropping again until 1980. From the 1980s until today the birth rate's decline has slowed, and is expected to reach twelve in 2020, meaning that just over 1 percent of the population will be born in 2020.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

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Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project