In 2025, Italy’s resident population is estimated to be almost 59 million inhabitants. About one-sixth of them lived in Lombardy, the most populous region in the country. Lazio and Campania followed, with roughly 5.7 million and 5.6 million inhabitants, respectively. These figures are mainly driven by Rome and Naples, the administrative capitals of these regions, and two of the largest metropolitan areas in the country. Which region has the oldest population? The population in Italy has become older and older over the last years. The average age in the country is equal to 46.8 years, but in some regions this figure is even higher. Liguria records an average age of 49.6 years and has one of the lowest birth rates in the country. Demographic trends for the future Liguria’s case, however, is not an outlier. Italy is already the country with the highest share of old people in Europe. At the same time, the very low number of new births means that, despite an always-increasing life expectancy, the Italian population is declining. Indeed, projections estimate that the country will have five million fewer inhabitants by 2050.

The population of Italy is getting older every year, becoming one of the oldest ones in the world. In 2025, the average age of the Italian population was 46.8 years, 3.4 years more than the average age registered in 2010. However, the age differs significantly depending on the region. According to the most recent data for 2025, the “oldest” citizens of the Italian peninsula live in the region of Liguria, with an average age 49.6 years, whereas the youngest are in Campania, 44.5 years on average. Women live longer than men The difference in the average age of the population can be observed not only on a regional basis, but also between genders. In 2021, Italian women were on average roughly three years older than men. When it comes to the life expectancy, data confirm the longevity of Italian women. In fact, females in Italy are expected to live on average about four years longer than men. The Old Continent In 2024, Europe was the continent with the highest share of population older than 65 years. Whereas the worldwide percentage of the population over 65 years was of ten percent, the percentage of elderly people in the Old Continent reached 20 percent.

Italy population density for 2021 was 199.97, a 0.51% decline from 2020.

<ul style='margin-top:20px;'>

<li>Italy population density for 2020 was <strong>201.00</strong>, a <strong>0.49% decline</strong> from 2019.</li>
<li>Italy population density for 2019 was <strong>201.98</strong>, a <strong>1.15% decline</strong> from 2018.</li>
<li>Italy population density for 2018 was <strong>204.32</strong>, a <strong>0.19% decline</strong> from 2017.</li>
</ul>Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.

The general version represents both the natural features of the territory and the main human settlements: reports on the main themes of transport (main road network, railways, ports, airports), hydrography (up to the 4th order of IGM, lakes and main reservoirs, glaciers), settlements (archaeological sites, historic buildings and imported religions), inhabited places distinct by inhabitants and administrative importance, as well as state and regional boundaries; the orography is represented with ipsometric shades, smoke and altitude points. The physical version represents the natural features of the territory: reports in more detail the themes of hydrography, the reliefs, the main geographical regions (masses and mountain ranges, highlands, valleys, plains, seas, gulfs, peaks, chiefs, islands and archipelagos) with their toponyomas, and reports the inhabited places up to the provincial capitals and state borders; the orography is represented with ipsometric shades, smoke and altitude points. The political version represents the main administrative aspects of the territory: reports the boundaries of State, Region and Province, the main inhabited areas up to the provincial capitals and municipalities with a population of more than 50 000 inhabitants (distinguished by administrative importance and by population groups), the place names for administrative units and the main geographical regions and natural forms of the territory; the orography is represented with smoke

Age and sex structures: WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. An overview of the data can be found in Tatem et al, and a description of the modelling methods used found in Tatem et al and Pezzulo et al. The 'Global per country 2000-2020' datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World for each year 2000-2020 structured by male/female and 5-year age classes (plus a <1 year class). These efforts necessarily involved some shortcuts for consistency. The 'individual countries' datasets represent older efforts to map population age and sex counts for each country separately, using a set of tailored geospatial inputs and differing methods and time periods. The 'whole continent' datasets are mosaics of the individual countries datasets. WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).

In 2023, the Italian region which registered the highest fertility rate was Trentino-South Tyrol, where the average number of children born per female reached 1.42 infants. Over the last years, the fertility rate in Italy has constantly decreased, except for 2021 when a slight increase by 0.01 points was recorded. Fewer and fewer children born per womanThe average number of children born per female significantly varied from the middle of the twentieth century to present days. In 2017, Italian women were on average a mother of one child, whereas about seven decades earlier, females had on average at least two kids. The lowest fertility rates worldwide From the global perspective, Italy was one of the world's twenty countries with the lowest fertility rate in 2023. This figure in Taiwan reached only 1.07 children per woman, placing the country on top of the ranking.

This is a sample dataset for the Biodemography Workshop. Within this dataset, input files related to demographic statistics will be considered, specifically population by gender and by Nuts2 in Italy, as well as shapefiles for map creation. The variables to be analyzed include the ratio between male and female, and vice versa. The final output consists of two maps. The data source is Istat, which provides these with a CC BY license: 1-https://demo.istat.it/app/?i=POS&l=it 2-https://www.istat.it/it/archivio/222527 To conduct the analysis, the open-source software R-Studio was used. The data management methodology will also be outlined in a Data Management Plan, written using Overleaf, in which we will provide more detailed information.

New-ID: NBI16

Agro-ecological zones datasets is made up of AEZBLL08, AEZBLL09, AEZBLL10.

The Africa Agro-ecological Zones Dataset documentation

Files: AEZBLL08.E00 Code: 100025-011 AEZBLL09.E00 100025-012 AEZBLL10.E00 100025-013

Vector Members The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename.

The Africa agro-ecological zones dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. The daset was developed by United Nations Environment Program (UNEP), Kenya. The base maps that were used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the Global Navigation and Planning Charts (various 1976-1982) and the National Geographic Atlas of the World (1975). basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. This edit step required appending the country boundaries from Administrative Unit map and then producing the computer plot.

Contact: UNEP/GRID-Nairobi, P O Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy ESRI, 380 New York Street, Redlands, CA 92373, USA

The AEZBLL08 data covers North-West of African continent The AEZBLL09 data covers North-East of African continent The AEZBLL10 data covers South of African continent

References:

ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP

FAO/UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris

Defence Mapping Agency. Global Navigation and Planning Charts for Africa (various dates:1976-1982). Scale 1:5000000. Washington DC.

G.M. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:8500000. National Geographic Society, Washington DC.

FAO. Statistical Data on Existing Animal Units by Agro-ecological Zones for Africa (1983). Prepared by Todor Boyadgiev of the Soil Resources, Management and Conservation Services Division.

FAO. Statistical Data on Existing and Potential Populations by Agro-ecological Zones for Africa (1983). Prepared by Marina Zanetti of the Soil Resources, Management and Conservation Services Division. FAO. Report on the Agro-ecological Zones Project. Vol.I (1978), Methodology & Result for Africa. World Soil Resources No.48.

Source : UNESCO/FAO Soil Map of the World, scale 1:5000000 Publication Date : Dec 1984 Projection : Miller Type : Polygon Format : Arc/Info Export non-compressed Related Datasets : All UNEP/FAO/ESRI Datasets, Landuse (100013/05, New-ID: 05 FAO Irrigable Soils Datasets and Water balance (100050/53)

New-ID: NBI18

The Africa Major Infrastructure and Human Settlements Dataset

Files: TOWNS2.E00 Code: 100022-002 ROADS2.E00 100021-002

Vector Members: The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename

The Africa major infrastructure and human settlements dataset form part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. This dataset was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the DMA Global Navigation and Planning charts for Africa (various dates: 1976-1982) and the Rand-McNally, New International Atlas (1982). All sources were re-registered to the basemap by comparing known features on the basemap those of the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm of the US Geological Survey and ESRI to create coverages for one-degree graticules. The Population Centers were selected based upon their inclusion in the list of major cities and populated areas in the Rand McNally New International Atlas Contact: UNEP/GRID-Nairobi, P.O. Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy ESRI, 380 New York Street, Redlands, CA. 92373, USA The ROADS2 file shows major roads of the African continent The TOWNS2 file shows human settlements and airports for the African continent

References:

ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP

FAO. UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris

Defence Mapping Agency. Global Navigation and Planning charts for Africa (various dates: 1976-1982). Scale 1:5000000. Washington DC.

Grosvenor. National Geographic Atlas of the World (1975). Scale 1:850000. National Geographic Society Washington DC.

DMA. Topographic Maps of Africa (various dates). Scale 1:2000000 Washington DC.

Rand-McNally. The new International Atlas (1982). Scale 1:6,000,000. Rand McNally & Co.Chicago

Source: FAO Soil Map of the World. Scale 1:5000000 Publication Date: Dec 1984 Projection: Miller Type: Points Format: Arc/Info export non-compressed Related Datasets: All UNEP/FAO/ESRI Datasets ADMINLL (100012-002) administrative boundries AFURBAN (100082) urban percentage coverage Comments: There is no outline of Africa

In 2023, the distribution of body-mass-index (BMI) across Italy varied greatly by region. According to the data, southern regions had a higher share of overweight and obese people compared to the national average. Overall, the overweight population in Italy is projected to reach 69.4 percent by 2029. The Italian regions with the highest share of people considered as having a normal weight in 2023 were Trentino-South Tyrol, Tuscany, and Marche. Conversely, the region of Aosta Valley hosted the most underweight people in the country, in relative terms, with 5.7 percent.

Diabetes The number of individuals suffering from diabetes in Italy amounted to 3,888 in 2022. Although the risk factors related to type one diabetes are not fully known, among the risk factors for diabetes type 2, being overweight or obese are among the most common. Indeed, in 2021, almost 17 percent of obese women were also diabetic. This rate lowers to 14.1 percent for men. Obesity among children and adolescents Childhood obesity is becoming an issue in the country, with the share of overweight and obese children growing every year. Indeed, Italy has become one of the European countries with the highest obesity rate among children. This tendency is more prevalent among young boys, with 29.8 percent of male minors overweight between 2020 and 2021, compared to 24 percent of females.

The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.

The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the European Commission H2020 Framework Programme within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services.

Reference literature:

Le Gal, M., Fernández-Montblanc, T., Duo, E., Montes Perez, J., Cabrita, P., Souto Ceccon, P., Gastal, V., Ciavola, P., and Armaroli, C.: A new European coastal flood database for low–medium intensity events, Nat. Hazards Earth Syst. Sci., 23, 3585–3602, https://doi.org/10.5194/nhess-23-3585-2023, 2023.

Description of the Dataset

The present database gathers flood and velocity maps for the European Union coast as well as their associated forcing parameters. The coast is divided into geographic regions embracing similar oceanographic conditions and subsequently into coastal sectors. The coastal sectors can be identified by its region index RXXX and its own index CSYYY. For each coastal sector, flood models were developed using the LISFLOOD-FP model with a grid resolution of 100 m. The flood model configuration follows the recommendation highlighted in ECFAS Deliverable D5.2 - Validated LISFLOOD-FP model for coastal areas. The flood and velocity maps are associated with synthetic storms that are characterised by a specific extreme water level and storm duration. These parameters were derived from Extreme Value Analyses performed on the ECFAS ANYEU-SSL hindcast (ECFAS D4.1 - Report on the calibration and validation of hindcasts and forecasts of TWL and D4.3 - Report on the identification of local thresholds of TWL for triggering coastal flooding). Five extreme water level values for each coastal point of the hindcast, and three durations (12, 24 and 36 h) were identified leading to 15 scenarios for each coastal sector. The flood and velocity maps are gathered into a NetCDF file for each coastal sector indicating the scenario parameters as attributes. In addition, the extreme water level values used for each coastal sector are contained in a complementary NetCDF file.

The shapefile of the polygons defining the coastal sectors as defined for the catalogue implementation is included in the database.

- The ECFAS Flood Catalogue was used to produce the associated ECFAS Pan-EU Impact Catalogue:

Impact Catalogue in Zenodo: Duo, E., Montes Pérez, J., Le Gal, M., Souto Ceccon, P.E., Cabrita, P., Fernández Montblanc, T., and Ciavola, P., 2022. ECFAS Pan-EU Impact Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211). www.ecfas.eu [Data set]. Zenodo. https://doi.org/10.5281/zenodo.677865

Impact Catalogue Reference literature: Duo, E., Montes, J., Le Gal, M., Fernández-Montblanc, T., Ciavola, P., and Armaroli, C.: Validated probabilistic approach to estimate flood direct impacts on the population and assets on European coastlines, Nat. Hazards Earth Syst. Sci., 25, 13–39, https://doi.org/10.5194/nhess-25-13-2025, 2025.

The Flood Catalogue is accompanied by a technical document describing methods, datasets, structure, format and content of the ECFAS Flood and Impact Catalogues:

Duo, E., Le Gal, M., Souto Ceccon, P.E., Montes Pérez, J., 2022. Technical document on the ECFAS Flood and Impact Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211). www.ecfas.eu

This ECFAS Flood Catalogue is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the Flood Catalogue are licensed under the Open Database License: http://opendatacommons.org/licenses/dbcl/1.0/.

This technical document describing methods, datasets, structure, format and content of the ECFAS Flood and Impact Catalogues is made available under the Creative Commons Attribution 4.0 International License.

*The size of the uncompressed dataset is 124 GB.

Disclaimer:

ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.

This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211

The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.

The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the European Commission H2020 Framework Programme within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services.

Reference literature:

Le Gal, M., Fernández-Montblanc, T., Duo, E., Montes Perez, J., Cabrita, P., Souto Ceccon, P., Gastal, V., Ciavola, P., and Armaroli, C.: A new European coastal flood database for low–medium intensity events, Nat. Hazards Earth Syst. Sci., 23, 3585–3602, https://doi.org/10.5194/nhess-23-3585-2023, 2023.

Description of the Dataset

The present database gathers flood and velocity maps for the European Union coast as well as their associated forcing parameters. The coast is divided into geographic regions embracing similar oceanographic conditions and subsequently into coastal sectors. The coastal sectors can be identified by its region index RXXX and its own index CSYYY. For each coastal sector, flood models were developed using the LISFLOOD-FP model with a grid resolution of 100 m. The flood model configuration follows the recommendation highlighted in ECFAS Deliverable D5.2 - Validated LISFLOOD-FP model for coastal areas. The flood and velocity maps are associated with synthetic storms that are characterised by a specific extreme water level and storm duration. These parameters were derived from Extreme Value Analyses performed on the ECFAS ANYEU-SSL hindcast (ECFAS D4.1 - Report on the calibration and validation of hindcasts and forecasts of TWL and D4.3 - Report on the identification of local thresholds of TWL for triggering coastal flooding). Five extreme water level values for each coastal point of the hindcast, and three durations (12, 24 and 36 h) were identified leading to 15 scenarios for each coastal sector. The flood and velocity maps are gathered into a NetCDF file for each coastal sector indicating the scenario parameters as attributes. In addition, the extreme water level values used for each coastal sector are contained in a complementary NetCDF file.

The shapefile of the polygons defining the coastal sectors as defined for the catalogue implementation is included in the database.

• The ECFAS Flood Catalogue was used to produce the associated ECFAS Pan-EU Impact Catalogue:

Impact Catalogue in Zenodo: Duo, E., Montes Pérez, J., Le Gal, M., Souto Ceccon, P.E., Cabrita, P., Fernández Montblanc, T., and Ciavola, P., 2022. ECFAS Pan-EU Impact Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211). www.ecfas.eu [Data set]. Zenodo. https://doi.org/10.5281/zenodo.677865

Impact Catalogue Reference literature: Duo, E., Montes, J., Le Gal, M., Fernández-Montblanc, T., Ciavola, P., and Armaroli, C.: Validated probabilistic approach to estimate flood direct impacts on the population and assets on European coastlines, Nat. Hazards Earth Syst. Sci., 25, 13–39, https://doi.org/10.5194/nhess-25-13-2025, 2025.

The Flood Catalogue is accompanied by a technical document describing methods, datasets, structure, format and content of the ECFAS Flood and Impact Catalogues:

Duo, E., Le Gal, M., Souto Ceccon, P.E., Montes Pérez, J., 2022. Technical document on the ECFAS Flood and Impact Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211). www.ecfas.eu

This ECFAS Flood Catalogue is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the Flood Catalogue are licensed under the Open Database License: http://opendatacommons.org/licenses/dbcl/1.0/.

This technical document describing methods, datasets, structure, format and content of the ECFAS Flood and Impact Catalogues is made available under the Creative Commons Attribution 4.0 International License.

*The size of the uncompressed dataset is 124 GB.

Disclaimer:

ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.

This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211

As of December 2024, Lombardy was the region in Italy hosting the largest share of immigrants, followed by Emilia-Romagna, Lazio, and Piedmont. Lombardy is the region with the highest number of inhabitants in the country. The north Italian region has ten million residents, around one sixth of the total national population, and was housing 18,200 immigrants. The Mediterranean route to Europe  In 2020, 955 migrants died or went missing in the Italian Central Mediterranean Sea in the attempt to reach Europe. In 2024, 66,317 people arrived at the Italian shores, 91,300 individuals less compared to 2023. Death and missing cases still represent a serious hazard for the people who want to reach Italy from North Africa. Racism on the rise in Italy Race-related violence is strictly correlated with immigration. According to 2020 data, the cases of racial physical violence increased, in particular between 2016 and 2018. Over these three years, the cases of body violence ranged from 24 to 127 attacks. Similarly, insults, threats, and harassment became more widespread. Between 2017 and 2019, the cases grew from 88 to 206, while only in the first three months of 2020 there were 53 episodes of racist insults, threats, and harassment.

In 1938, the year before the outbreak of the Second world War, the countries with the largest populations were China, the Soviet Union, and the United States, although the United Kingdom had the largest overall population when it's colonies, dominions, and metropole are combined. Alongside France, these were the five Allied "Great Powers" that emerged victorious from the Second World War. The Axis Powers in the war were led by Germany and Japan in their respective theaters, and their smaller populations were decisive factors in their defeat. Manpower as a resource In the context of the Second World War, a country or territory's population played a vital role in its ability to wage war on such a large scale. Not only were armies able to call upon their people to fight in the war and replenish their forces, but war economies were also dependent on their workforce being able to meet the agricultural, manufacturing, and logistical demands of the war. For the Axis powers, invasions and the annexation of territories were often motivated by the fact that it granted access to valuable resources that would further their own war effort - millions of people living in occupied territories were then forced to gather these resources, or forcibly transported to work in manufacturing in other Axis territories. Similarly, colonial powers were able to use resources taken from their territories to supply their armies, however this often had devastating consequences for the regions from which food was redirected, contributing to numerous food shortages and famines across Africa, Asia, and Europe. Men from annexed or colonized territories were also used in the armies of the war's Great Powers, and in the Axis armies especially. This meant that soldiers often fought alongside their former-enemies. Aftermath The Second World War was the costliest in human history, resulting in the deaths of between 70 and 85 million people. Due to the turmoil and destruction of the war, accurate records for death tolls generally do not exist, therefore pre-war populations (in combination with other statistics), are used to estimate death tolls. The Soviet Union is believed to have lost the largest amount of people during the war, suffering approximately 24 million fatalities by 1945, followed by China at around 20 million people. The Soviet death toll is equal to approximately 14 percent of its pre-war population - the countries with the highest relative death tolls in the war are found in Eastern Europe, due to the intensity of the conflict and the systematic genocide committed in the region during the war.

In Italy, the share of the population at risk of poverty stood at 14.5 percent in 2023. The southern regions and the islands were mostly at risk of poverty compared to the northern regions. Indeed. In Calabria and Apulia, around 30 percent of the population was at risk of poverty in 2023.

The world's Jewish population has had a complex and tumultuous history over the past millennia, regularly dealing with persecution, pogroms, and even genocide. The legacy of expulsion and persecution of Jews, including bans on land ownership, meant that Jewish communities disproportionately lived in urban areas, working as artisans or traders, and often lived in their own settlements separate to the rest of the urban population. This separation contributed to the impression that events such as pandemics, famines, or economic shocks did not affect Jews as much as other populations, and such factors came to form the basis of the mistrust and stereotypes of wealth (characterized as greed) that have made up anti-Semitic rhetoric for centuries. Development since the Middle Ages The concentration of Jewish populations across the world has shifted across different centuries. In the Middle Ages, the largest Jewish populations were found in Palestine and the wider Levant region, with other sizeable populations in present-day France, Italy, and Spain. Later, however, the Jewish disapora became increasingly concentrated in Eastern Europe after waves of pogroms in the west saw Jewish communities move eastward. Poland in particular was often considered a refuge for Jews from the late-Middle Ages until the 18th century, when it was then partitioned between Austria, Prussia, and Russia, and persecution increased. Push factors such as major pogroms in the Russian Empire in the 19th century and growing oppression in the west during the interwar period then saw many Jews migrate to the United States in search of opportunity.

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

In 2023, the Faroe Islands was the European country estimated to have the highest fertility rate. The small Atlantic island state had a fertility rate of 2.71 children per woman. Other small countries such as Monaco and Gibraltar also came towards the top of the list for 2023, while the large country with the highest fertility rate was France, with 1.79 children per woman. On the other hand, Andorra, San Marino, and Malta had the lowest fertility rates in Europe, with Ukraine, Spain, and Italy being the largest countries with low fertility rates in that year, averaging around 1.3 children per woman.

The smallest country in the world is Vatican City, with a landmass of just **** square kilometers (0.19 square miles). Vatican City is an independent state surrounded by Rome. Vatican City is not the only small country located inside Italy. San Marino is another microstate, with a land area of ** square kilometers, making it the fifth-smallest country in the world. Many of these small nations have equally small populations, typically less than ************** inhabitants. However, the population of Singapore is almost *** million, and it is the twentieth smallest country in the world with a land area of *** square kilometers. In comparison, Jamaica is almost eight times larger than Singapore, but has half the population.

This statistic shows the estimated number of Muslims living in different European countries as of 2016. Approximately **** million Muslims were estimated to live in France, the most of any country listed. Germany and the United Kingdom also have large muslim populations with **** million and **** million respectively.