The Black Death was the largest and deadliest pandemic of Yersinia pestis recorded in human history, and likely the most infamous individual pandemic ever documented. The plague originated in the Eurasian Steppes, before moving with Mongol hordes to the Black Sea, where it was then brought by Italian merchants to the Mediterranean. From here, the Black Death then spread to almost all corners of Europe, the Middle East, and North Africa. While it was never endemic to these regions, it was constantly re-introduced via trade routes from Asia (such as the Silk Road), and plague was present in Western Europe until the seventeenth century, and the other regions until the nineteenth century. Impact on Europe In Europe, the major port cities and metropolitan areas were hit the hardest. The plague spread through south-western Europe, following the arrival of Italian galleys in Sicily, Genoa, Venice, and Marseilles, at the beginning of 1347. It is claimed that Venice, Florence, and Siena lost up to two thirds of their total population during epidemic's peak, while London, which was hit in 1348, is said to have lost at least half of its population. The plague then made its way around the west of Europe, and arrived in Germany and Scandinavia in 1348, before travelling along the Baltic coast to Russia by 1351 (although data relating to the death tolls east of Germany is scarce). Some areas of Europe remained untouched by the plague for decades; for example, plague did not arrive in Iceland until 1402, however it swept across the island with devastating effect, causing the population to drop from 120,000 to 40,000 within two years. Reliability While the Black Death affected three continents, there is little recorded evidence of its impact outside of Southern or Western Europe. In Europe, however, many sources conflict and contrast with one another, often giving death tolls exceeding the estimated population at the time (such as London, where the death toll is said to be three times larger than the total population). Therefore, the precise death tolls remain uncertain, and any figures given should be treated tentatively.

Analysis of ‘Population by age group in black buck on 31.12. ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-region-statistik-nord-de-detail_timeline-13-1102-5-1-350-907- on 16 January 2022.

--- Dataset description provided by original source is as follows ---

Population — Population (Official update) — Population by age group in Schwartbuck on 31.12.

on the HTML offer of the time series

regional data for Schleswig-Holstein

Statistical Office for Hamburg and Schleswig-Holstein

--- Original source retains full ownership of the source dataset ---

Data used for analyses in "Exogenous Loss of Labor: The Black Death in Fourteenth Century Europe" (Chapter 4).

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

As of 2022, South Africa's population increased and counted approximately 60.6 million inhabitants in total, of which the majority (roughly 49.1 million) were Black Africans. Individuals with an Indian or Asian background formed the smallest population group, counting approximately 1.56 million people overall. Looking at the population from a regional perspective, Gauteng (includes Johannesburg) is the smallest province of South Africa, though highly urbanized with a population of nearly 16 million people.

Increase in number of households

The total number of households increased annually between 2002 and 2022. Between this period, the number of households in South Africa grew by approximately 65 percent. Furthermore, households comprising two to three members were more common in urban areas (39.2 percent) than they were in rural areas (30.6 percent). Households with six or more people, on the other hand, amounted to 19.3 percent in rural areas, being roughly twice as common as those in urban areas.

Main sources of income

The majority of the households in South Africa had salaries or grants as a main source of income in 2019. Roughly 10.7 million drew their income from regular wages, whereas 7.9 million households received social grants paid by the government for citizens in need of state support.

Table showing ethnic group statistics by aggregated groupings.

Categories covered:

• White - includes White British, Irish, Gypsy or Irish Traveller, and Other
• Black - includes Black African, Caribbean, and Other
• Asian - includes Indian, Pakistani, Bangladeshi, Chinese and Other
• Mixed/Other - includes White and Black Caribbean, White and Asian, White and Black African, Other Mixed, Arab and any Other ethnicity not covered above.

Figures may not add exactly due to rounding. Numbers rounded to the nearest thousand.

Data is from the Annual Population Survey.

HLA Class II Haplotype Frequency Distributions (for 99% haplotypes per population) and HLA Class II Simulated Populations (Genotype level information for sample sizes of 1000, 5000, 10000 simulated individuals) for 4 broad and 21 detailed US population groups.

Broad population groups: African Americans (AFA), Asian and Pacific Islanders (API), Caucasians (CAU), Hispanics (HIS).

Detailed population groups: African American (AAFA), African (AFB), South Asian Indian (AINDI), American Indian - South or Central American (AISC), Alaska native of Aleut (ALANAM), North American Indian (AMIND), Caribbean Black (CARB), Caribbean Hispanic (CARHIS), Caribbean Indian (CARIBI), European Caucasian (EURCAU), Filipino (FILII), Hawaiian or other Pacific Islander (HAWI), Japanese (JAPI), Korean (KORI), Middle Eastern or North Coast of Africa (MENAFC), Mexican or Chicano (MSWHIS), Chinese (NCHI), Hispanic - South or Central American (SCAHIS), Black - South or Central American (SCAMB), Southeast Asian (SCSEAI), Vietnamese (VIET).

One of the major concerns in conservation today is the loss of genetic diversity which is a frequent consequence of population isolation and small population sizes. Fragmentation of populations and persecution of carnivores has posed a substantial threat to the persistence of free ranging carnivores in North America since the arrival of European settlers. Black bears have seen significant reductions in range size from their historic extent, which is most pronounced in the southeastern United States and even more starkly in Alabama where until recently bears were reduced to a single geographically isolated population in the Mobile River Basin. Recently a second population has naturally re-established itself in northeastern Alabama. We sought to determine size, genetic diversity and genetic connectivity for these two populations in relation to other regional populations. Both populations of black bears in Alabama had small population sizes and had moderate to low genetic diversity, but showed different levels of connectivity to surrounding populations of bears. The Mobile River Basin population had a small population size at only 86 individuals (76–124, 95% C.I.), the lowest genetic diversity of compared populations (richness = 2.33, Ho and He = 0.33), and showed near complete genetic isolation from surrounding populations across multiple tests. The newly recolonizing population in northeastern Alabama had a small but growing population doubling in 3 years (34 individuals 26–43, 95% C.I.), relatively moderate genetic diversity compared to surrounding populations (richness = 3.32, Ho = 0.53, He = 0.65), and showed a high level of genetic connectivity with surrounding populations.

This dataset was produced in the 1990s by Myron Gutmann and others at the University of Texas to assess demographic change in European- and Mexican-origin populations in Texas from the mid-nineteenth to early-twentieth centuries. Most of the data come from manuscript records for six rural Texas counties - Angelina, DeWitt, Gillespie, Jack, Red River, and Webb - for the U.S. Censuses of 1850-1880 and 1900-1910, and tax records where available. Together, the populations of these counties reflect the cultural, ethnic, economic, and ecological diversity of rural Texas. Red River and Angelina Counties, in Eastern Texas, had largely native-born white and black populations and cotton economies. DeWitt County in Southeast Texas had the most diverse population, including European and Mexican immigrants as well as native-born white and black Americans, and its economy was divided between cotton and cattle. The population of Webb County, on the Mexican border, was almost entirely of Mexican origin, and economic activities included transportation services as well as cattle ranching. Gillespie County in Central Texas had a mostly European immigrant population and an economy devoted to cropping and livestock. Jack County in North-Central Texas was sparsely populated, mainly by native-born white cattle ranchers. These counties were selected to over-represent the European and Mexican immigrant populations. Slave schedules were not included, so there are no African Americans in the samples for 1850 or 1860. In some years and counties, the Census records were sub-sampled, using a letter-based sample with the family as the primary sampling unit (families were chosen if the surname of the head began with one of the sample letters for the county). In other counties and years, complete populations were transcribed from the Census microfilms. For details and sample sizes by county, see the County table in the Original P.I. Documentation section of the ICPSR Codebook, or see Gutmann, Myron P. and Kenneth H. Fliess, How to Study Southern Demography in the Nineteenth Century: Early Lessons of the Texas Demography Project (Austin: Texas Population Research Center Papers, no. 11.11, 1989).

The survey, commissioned by the newsmagazine Suomen Kuvalehti, charted attitudes in Finland towards immigrants from different countries as well as beliefs about race. First, the respondents were asked to state their position on a scale from 0 to 10, where 10 indicated they hoped that Finland would be populated as much as possible by people of Finnish origin sharing the national values, and 0 that they hoped Finland would be populated as much as possible by people from a diversity of countries and ethnic backgrounds. Next, opinions were studied regarding how desirable or undesirable the respondents thought it was that immigrants of certain nationalities would come to Finland. The nationalities mentioned were Swedes, Germans, Russians, Estonians, US Americans, Somalis, Kosovars, Iraqis, Afghans, Syrians, Chinese, Thai and Ukrainians. The respondents were also asked to what extent they agreed with the following four statements: 'The mental abilities of black Africans are lower than those of white people living in Western countries', ' All people have equal value regardless of the colour of their skin or ethnic background', 'The white European race should be prevented from being mixed with darker races because otherwise the original population of Europe will become extinct before long ', and 'There is no such thing as 'race' since all human beings are genetically very much alike'. One question studied whether the respondents thought the Finnish media reported more negatively or positively on the Perussuomalaiset party (the Finns Party) than on the other political parties. Background variables included the respondent's gender, age, region of residence (NUTS3), major region of residence (NUTS2), city or type of municipality, education, occupational status and economic activity, household composition, number and ages of children living at home, total gross annual income of the household, and type of housing.

Sex stratification.

Introduction: Human populations are often highly structured due to differences in genetic ancestry among groups, posing difficulties in associating genes with diseases. Ancestry-informative markers (AIMs) aid in the detection of population stratification and provide an alternative approach to map population-specific alleles to disease. Here, we identify and characterize a novel set of African AIMs that separate populations of African ancestry from other global populations including those of European ancestry.Methods: Using data from the 1000 Genomes Project, highly informative SNP markers from five African subpopulations were selected based on estimates of informativeness (In) and compared against the European population to generate a final set of 46,737 African ancestry-informative markers (AIMs). The AIMs identified were validated using an independent set and functionally annotated using tools like SIFT, PolyPhen. They were also investigated for representation of commonly used SNP arrays.Results: This set of African AIMs effectively separates populations of African ancestry from other global populations and further identifies substructure between populations of African ancestry. When a subset of these AIMs was studied in an independent dataset, they differentiated people who self-identify as African American or Black from those who identify their ancestry as primarily European. Most of the AIMs were found to be in their intergenic and intronic regions with only 0.6% in the coding regions of the genome. Most of the commonly used SNP array investigated contained less than 10% of the AIMs.Discussion: While several functional annotations of both coding and non-coding African AIMs are supported by the literature and linked these high-frequency African alleles to diseases in African populations, more effort is needed to map genes to diseases in these genetically diverse subpopulations. The relative dearth of these African AIMs on current genotyping platforms (the array with the highest fraction, llumina’s Omni 5, harbors less than a quarter of AIMs), further demonstrates a greater need to better represent historically understudied populations.

It is estimated that the largest cities in Western Europe in 1330 were Paris and Granada. At this time, Paris was the seat of power in northern France, while Granada had become the largest multicultural city in southern Spain, controlled by the Muslim, Nasrid Kingdom during Spain's Reconquista period. The next three largest cities were Venice, Genoa and Milan, all in northern Italy, renowned as important trading cities during the middle ages. In October 1347, the first wave of the Black Death had arrived in Sicily and then began spreading throughout Europe, decimating the population.

ar2≥0.50.bCalculated using African American LD patterns.cCalculated using European LD patterns.dCalculated as (European region size - African American region size (Kb)).Kb, kilobase. LD, linkage disequilibrium.

We present a comprehensive assessment of genomic diversity in the African-American population by studying three genotyped cohorts comprising 3,726 African-Americans from across the United States that provide a representative description of the population across all US states and socioeconomic status. An estimated 82.1% of ancestors to African-Americans lived in Africa prior to the advent of transatlantic travel, 16.7% in Europe, and 1.2% in the Americas, with increased African ancestry in the southern United States compared to the North and West. Combining demographic models of ancestry and those of relatedness suggests that admixture occurred predominantly in the South prior to the Civil War and that ancestry-biased migration is responsible for regional differences in ancestry. We find that recent migrations also caused a strong increase in genetic relatedness among geographically distant African-Americans. Long-range relatedness among African-Americans and between African-Americans and European-Americans thus track north- and west-bound migration routes followed during the Great Migration of the twentieth century. By contrast, short-range relatedness patterns suggest comparable mobility of ∼15–16km per generation for African-Americans and European-Americans, as estimated using a novel analytical model of isolation-by-distance.

Throughout the Common Era, Western Europe's population development fluctuated greatly. The population was very similar at the beginning and end of the first millennium, at around 25 million people. The largest decline in this period occurred in the sixth century, due to the Plague of Justinian, which the source claims to have killed around one third of the continent's population (although recent studies dispute this). Similarly, the population fell by almost 17 million throughout the 14th century, due to the Black Death.

Improvements in agriculture and infrastructure then saw population growth increase once more from the 15th century onwards, before the onset of the demographic transition saw a population boom throughout the 19th and 20th centuries.

The footnotes in the table are represented in brackets. The first footnote does not appear in the table.Footnotes: 1 For the 2011 National Household Survey (NHS) estimates, the global non-response rate (GNR) is used as an indicator of data quality. This indicator combines complete non-response (household) and partial non-response (question) into a single rate. The value of the GNR is presented to users. A smaller GNR indicates a lower risk of non-response bias and as a result, lower risk of inaccuracy. The threshold used for estimates' suppression is a GNR of 50% or more. For more information, please refer to the National Household Survey User Guide, 2011.2 The category 'Total - Single and multiple ethnic origin responses' indicates the number of respondents who reported a specified ethnic origin, either as their only ethnic origin or in addition to one or more other ethnic origins. The sum of all total responses for all ethnic origins is greater than the total population estimate due to the reporting of multiple origins.3 A single ethnic origin response occurs when a respondent provides one ethnic origin only.4 A multiple ethnic origin response occurs when a respondent provides two or more ethnic origins.5 This is a total population estimate. The sum of the ethnic groups in this table is greater than the total population estimate because a person may report more than one ethnic origin in the NHS.6 Includes general responses indicating North American origins (e.g., 'North American') as well as more specific responses indicating North American origins that have not been included elsewhere (e.g., 'Maritimer,' 'Manitoban').7 Includes general responses indicating British Isles origins (e.g., 'British,' 'United Kingdom') as well as more specific responses indicating British Isles origins that have not been included elsewhere (e.g., 'Celtic').8 Includes general responses indicating Western European origins (e.g., 'Western European') as well as more specific responses indicating Western European origins that have not been included elsewhere (e.g., 'Liechtensteiner').9 Includes general responses indicating Northern European origins (e.g., 'Northern European') as well as more specific responses indicating Northern European origins that have not been included elsewhere (e.g., 'Faroese,' 'Scandinavian').10 Includes general responses indicating Eastern European origins (e.g., 'Eastern European') as well as more specific responses indicating Eastern European origins that have not been included elsewhere (e.g., 'Baltic').11 Includes general responses indicating Southern European origins (e.g., 'Southern European') as well as more specific responses indicating Southern European origins that have not been included elsewhere (e.g., 'Gibraltarian').12 Includes general responses indicating Other European origins (e.g., 'European') as well as more specific responses indicating European origins that have not been included elsewhere (e.g., 'Central European').13 Includes general responses indicating Caribbean origins (e.g., 'Caribbean') as well as more specific responses indicating Caribbean origins that have not been included elsewhere (e.g., 'Guadelupian,' 'Aruban').14 Includes general responses indicating Latin, Central or South American origins (e.g., 'South American') as well as more specific responses indicating Latin, Central or South American origins that have not been included elsewhere (e.g., 'Surinamese').15 Includes general responses indicating Central or West African origins (e.g., 'West African') as well as more specific responses indicating Central or West African origins that have not been included elsewhere (e.g., 'Ewe,' 'Wolof').16 Includes general responses indicating North African origins (e.g., 'North African') as well as more specific responses indicating North African origins that have not been included elsewhere (e.g., 'Maghreb').17 Includes general responses indicating Southern or East African origins (e.g., 'East African') as well as more specific responses indicating Southern or East African origins that have not been included elsewhere (e.g., 'Hutu,' 'Shona').18 Some respondents may choose to provide very specific ethnic origins in the National Household Survey (NHS), while other respondents may choose to give more general responses. This means that two respondents with the same ethnic ancestry could have different response patterns and thus could be counted as having different ethnic origins. For example, one respondent may report 'East Indian' ethnic origin while another respondent, with a similar ancestral background, may report 'Punjabi' or 'South Asian' origins; one respondent may report 'Black' while another, similar respondent, may report 'Ghanaian' or 'African.' As a result, ethnic origin data are very fluid, and counts for certain origins, such as 'East Indian' and 'Black,' may seem lower than initially expected. Users who wish to obtain broader response counts may wish to combine data for one or more ethnic origins together or use counts for ethnic categories such as 'South Asian origins' or 'African origins.' (Please note, however, that 'African origins' should not be considered equivalent to the 'Black' population group or visible minority status, as there are persons reporting African origins who report a population group or visible minority status other than 'Black.' Conversely, many people report a population group or visible minority status of 'Black' and do not report having 'African' origins. For information on population group and visible minority population in the 2011 NHS, refer to the appropriate definitions in this publication).19 Includes general responses indicating Other African origins (e.g., 'African') as well as more specific responses indicating Other African origins that have not been included elsewhere (e.g., 'Saharan').20 Includes general responses indicating West Asian, Central Asian and Middle Eastern origins (e.g., 'West Asian,' 'Middle Eastern') as well as more specific responses indicating West Asian, Central Asian and Middle Eastern origins that have not been included elsewhere (e.g., 'Baloch,' 'Circassian').21 Some respondents may choose to provide very specific ethnic origins in the National Household Survey (NHS), while other respondents may choose to give more general responses. This means that two respondents with the same ethnic ancestry could have different response patterns and thus could be counted as having different ethnic origins. For example, one respondent may report 'East Indian' ethnic origin while another respondent, with a similar ancestral background, may report 'Punjabi' or 'South Asian' origins; one respondent may report 'Black' while another, similar respondent, may report 'Ghanaian' or 'African.' As a result, ethnic origin data are very fluid, and counts for certain origins, such as 'East Indian' and 'Black,' may seem lower than initially expected. Users who wish to obtain broader response counts may wish to combine data for one or more ethnic origins together or use counts for ethnic categories such as 'South Asian origins' or 'African origins.' (Please note, however, that 'African origins' should not be considered equivalent to the 'Black' population group or visible minority status, as there are persons reporting African origins who report a population group or visible minority status other than 'Black.' Conversely, many people report a population group or visible minority status of 'Black' and do not report having 'African' origins. For information on population group and visible minority population in the 2011 NHS, refer to the appropriate definitions in this publication).22 Includes general responses indicating South Asian origins (e.g., 'South Asian') as well as more specific responses indicating South Asian origins that have not been included elsewhere (e.g., 'Bhutanese').23 Includes general responses indicating East and Southeast Asian origins (e.g., 'Southeast Asian') as well as more specific responses indicating East and Southeast Asian origins that have not been included elsewhere (e.g., 'Bruneian,' 'Karen').24 Includes general responses indicating Other Asian origins (e.g., 'Asian') as well as more specific responses indicating Other Asian origins that have not been included elsewhere (e.g., 'Eurasian').25 Includes general responses indicating Pacific Islands origins (e.g., 'Pacific Islander') as well as more specific responses indicating Pacific Islands origins that have not been included elsewhere (e.g., 'Tahitian').

Genotype imputation, used in genome-wide association studies to expand coverage of single nucleotide polymorphisms (SNPs), has performed poorly in African Americans compared to less admixed populations. Overall, imputation has typically relied on HapMap reference haplotype panels from Africans (YRI), European Americans (CEU), and Asians (CHB/JPT). The 1000 Genomes project offers a wider range of reference populations, such as African Americans (ASW), but their imputation performance has had limited evaluation. Using 595 African Americans genotyped on Illumina’s HumanHap550v3 BeadChip, we compared imputation results from four software programs (IMPUTE2, BEAGLE, MaCH, and MaCH-Admix) and three reference panels consisting of different combinations of 1000 Genomes populations (February 2012 release): (1) 3 specifically selected populations (YRI, CEU, and ASW); (2) 8 populations of diverse African (AFR) or European (AFR) descent; and (3) all 14 available populations (ALL). Based on chromosome 22, we calculated three performance metrics: (1) concordance (percentage of masked genotyped SNPs with imputed and true genotype agreement); (2) imputation quality score (IQS; concordance adjusted for chance agreement, which is particularly informative for low minor allele frequency [MAF] SNPs); and (3) average r2hat (estimated correlation between the imputed and true genotypes, for all imputed SNPs). Across the reference panels, IMPUTE2 and MaCH had the highest concordance (91%–93%), but IMPUTE2 had the highest IQS (81%–83%) and average r2hat (0.68 using YRI+ASW+CEU, 0.62 using AFR+EUR, and 0.55 using ALL). Imputation quality for most programs was reduced by the addition of more distantly related reference populations, due entirely to the introduction of low frequency SNPs (MAF≤2%) that are monomorphic in the more closely related panels. While imputation was optimized by using IMPUTE2 with reference to the ALL panel (average r2hat = 0.86 for SNPs with MAF>2%), use of the ALL panel for African American studies requires careful interpretation of the population specificity and imputation quality of low frequency SNPs.

Independent classical HLA alleles and SNPs in African Americans.