The world's population first reached one billion people in 1805, and reached eight billion in 2022, and will peak at almost 10.2 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two-thirds of the world's population lives in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a few years later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

Population dynamics, its types. Population migration (external, internal), factors determining it, main trends. Impact of migration on population health.

Under the guidance of Moldoev M.I. Sir By Riya Patil and Rutuja Sonar

Abstract

Population dynamics influence development and vice versa, at various scale levels: global, continental/world-regional, national, regional, and local. Debates on how population growth affects development and how development affects population growth have already been subject of intensive debate and controversy since the late 18th century, and this debate is still ongoing. While these two debates initially focused mainly on natural population growth, the impact of migration on both population dynamics and development is also increasingly recognized. While world population will continue growing throughout the 21st century, there are substantial and growing contrasts between and within world-regions in the pace and nature of that growth, including some countries where population is stagnating or even shrinking. Because of these growing contrasts, population dynamics and their interrelationships with development have quite different governance implications in different parts of the world.

1. Population Dynamics

Population dynamics refers to the changes in population size, structure, and distribution over time. These changes are influenced by four main processes:

Birth rate (natality)

Death rate (mortality)

Immigration (inflow of people)

Emigration (outflow of people)

Types of Population Dynamics

Natural population change: Based on birth and death rates.

Migration-based change: Caused by people moving in or out of a region.

Demographic transition: A model that explains changes in population growth as societies industrialize.

Population distribution: Changes in where people live (urban vs rural).

2. Population Migration

Migration refers to the movement of people from one location to another, often across political or geographical boundaries.

Types of Migration

External migration (international):

Movement between countries.

Examples: Refugee relocation, labor migration, education.

Internal migration:

Movement within the same country or region.

Examples: Rural-to-urban migration, inter-state migration.

3. Factors Determining Migration

Migration is influenced by push and pull factors:

Push factors (reasons to leave a place):

Unemployment

Conflict or war

Natural disasters

Poverty

Lack of services or opportunities

Pull factors (reasons to move to a place):

Better job prospects

Safety and security

Higher standard of living

Education and healthcare access

Family reunification

4. Main Trends in Migration

Urbanization: Mass movement to cities for work and better services.

Global labor migration: Movement from developing to developed countries.

Refugee and asylum seeker flows: Due to conflict or persecution.

Circular migration: Repeated movement between two or more locations.

Brain drain/gain: Movement of skilled labor away from (or toward) a country.

5. Impact of Migration on Population Health

Positive Impacts:

Access to better healthcare (for migrants moving to better systems).

Skills and knowledge exchange among health professionals.

Remittances improving healthcare affordability in home countries.

Negative Impacts:

Migrants’ health risks: Increased exposure to stress, poor living conditions, and occupational hazards.

Spread of infectious diseases: Especially when health screening is lacking.

Strain on health services: In receiving areas, especially with sudden or large influxes.

Mental health challenges: Due to cultural dislocation, discrimination, or trauma.

Population dynamics is one of the fundamental areas of ecology, forming both the basis for the study of more complex communities and of many applied questions. Understanding population dynamics is the key to understanding the relative importance of competition for resources and predation in structuring ecological communities, which is a central question in ecology.

Population dynamics plays a central role in many approaches to preserving biodiversity, which until now have been primarily focused on a single species approach. The calculation of the intrinsic growth rate of a species from a life table is often the central piece of conservation plans. Similarly, management of natural resources, such as fisheries, depends on population dynamics as a way to determine appropriate management actions.

Population dynamics can be characterized by a nonlinear system of difference or differential equations between the birth sizes of consecutive periods. In such a nonlinear system, when the feedback elasticity of previous events on current birth size is larger, the more likely the dynamics will be volatile. Depending on the classification criteria of the population, the revealed cyclical behavior has various interpretations. Under different contextual scenarios, Malthusian cycles, Easterlin cycles, predator–prey cycles, dynastic cycles, and capitalist–laborer cycles have been introduced and analyzed

Generally, population dynamics is a nonlinear stochastic process. Nonlinearities tend to be complicated to deal with, both when we want to do analytic stochastic modelling and when analysing data. The way around the problem is to approximate the nonlinear model with a linear one, for which the mathematical and statistical theories are more developed and tractable. Let us assume that the population process is described as:

(1)Nt=f(Nt−1,εt)

where Nt is population density at time t and εt is a series of random variables with identical distributions (mean and variance). Function f specifies how the population density one time step back, plus the stochastic environment εt, is mapped into the current time step. Let us assume that the (deterministic) stationary (equilibrium) value of the population is N* and that ε has mean ε*. The linear approximation of Eq. (1) close to N* is then:

(2)xt=axt−1+bϕt

where xt=Nt−N*, a=f

f(N*,ε*)/f

N, b=ff(N*,ε*)/fε, and ϕt=εt−ε*

The term population refers to the members of a single species that can interact with each other. Thus, the fish in a lake, or the moose on an island, are clear examples of a population. In other cases, such as trees in a forest, it may not be nearly so clear what a population is, but the concept of population is still very useful.

Population dynamics is essentially the study of the changes in the numbers through time of a single species. This is clearly a case where a quantitative description is essential, since the numbers of individuals in the population will be counted. One could begin by looking at a series of measurements of the numbers of particular species through time. However, it would still be necessary to decide which changes in numbers through time are significant, and how to determine what causes the changes in numbers. Thus, it is more sensible to begin with models that relate changes in population numbers through time to underlying assumptions. The models will provide indications of what features of changes in numbers are important and what measurements are critical to make, and they will help determine what the cause of changes in population levels might be.

To understand the dynamics of biological populations, the study starts with the simplest possibility and determines what the dynamics of the population would be in that case. Then, deviations in observed populations from the predictions of that simplest case would provide information about the kinds of forces shaping the dynamics of populations. Therefore, in describing the dynamics in this simplest case it is essential to be explicit and clear about the assumptions made. It would not be argued that the idealized population described here would ever be found, but that focusing on the idealized population would provide insight into real populations, just as the study of Newtonian mechanics provides understanding of more realistic situations in physics.

Population migration

The vast majority of people continue to live in the countries where they were born —only one in 30 are migrants.

In most discussions on migration, the starting point is usually numbers. Understanding changes in scale, emerging trends, and shifting demographics related to global social and economic transformations, such as migration, help us make sense of the changing world we live in and plan for the future. The current global estimate is that there were around 281 million international migrants in the world in 2020, which equates to 3.6 percent of the global population.

Overall, the estimated number of international migrants has increased over the past five decades. The total estimated 281 million people living in a country other than their countries of birth in 2020 was 128 million more than in 1990 and over three times the estimated number in 1970.

There is currently a larger number of male than female international migrants worldwide and the growing gender gap has increased over the past 20 years. In 2000, the male to female split was 50.6 to 49.4 per cent (or 88 million male migrants and 86 million female migrants). In 2020 the split was 51.9 to 48.1 per cent, with 146 million male migrants and 135 million female migrants. The share of

The global population has grown rapidly since 1950, from *** billion to over eight billion in 2023. The age distribution shows that the number of people within all age groups increased over the period, with the two youngest age groups being the largest in 2023. Population growth driven by development in Asia The increasing global population is explained by economic development and a coinciding improvement in living conditions in several parts of the world, particularly in Asia. Improvements in sanitary conditions, the rollout of vaccination programs, and better medical treatment brought down death rates around the world. China saw fast economic development from the early 1980s to the late 2010s, going hand in hand with a rapidly increasing population. Furthermore, the population of India has grown rapidly since it gained independence from the British Empire in the late 1940s, now being the largest in the world. Most of the future population growth will happen in Africa The global population is forecast to continue to increase over the coming decades, set to reach over 10 billion people by 2060. Most of this increase is projected to occur on the African continent, as many African countries are expected to experience an improvement in living standards. In 2022, over ** percent of the population in Sub-Saharan Africa was below 15 years old.

Abstract

The Life in Transition Survey, after the crisis (LiTS II), is the second round of LiTS surveys, previously conducted in 2006 (LiTS I). In late 2006, the EBRD and World Bank carried out the first comprehensive survey of individuals and households across virtually the whole transition region. The purpose was to gain a better understanding of how people's lives had been shaped and affected by the upheavals of the previous 15 years.

Four years later, the EBRD and World Bank commissioned a second round of the survey. The circumstances facing most people were significantly different between the first and second rounds. The Life in Transition Survey I (LiTS I) was carried out at a time when the region's economies were, with few exceptions, growing strongly. In contrast, LiTS II took place in late 2010, at a time when most countries were still facing the aftershocks of a severe global economic crisis.

LiTS II advances and improves on LiTS I in two important ways. First, the questionnaire was substantially revised. The new questionnaire includes sections on the impact of the crisis and on climate change issues, as well as improved and expanded questions in areas such as corporate governance, public service delivery, and economic and social attitudes. Second, the coverage has been expanded to include five western European "comparator" countries - France, Germany, Italy, Sweden and the UK. This allows us to benchmark the transition region against some advanced market economies, thereby giving a clearer perspective on the remaining challenges facing transition countries.

Geographic coverage

The second Life in Transition Survey (LiTS II) was implemented in 30 transition countries (Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Former Yugoslav Republic of Macedonia (FYROM), Georgia, Hungary, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Moldova, Mongolia, Montenegro, Poland, Romania, Russia, Serbia, Slovak Republic, Slovenia, Tajikistan, Turkey, Ukraine, Uzbekistan and Kosovo) as well as five comparator countries in western Europe (France, Germany, Italy, Sweden and the United Kingdom).

Analysis unit

• individuals
• households

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling methodology was designed to make the sample nationally representative. In order to achieve this, a two-stage clustered stratified sampling procedure was used to select the households to be included in the sample. In 25 transition countries, France, Germany, Italy and Sweden, the survey was conducted face-to-face in 1,000 randomly chosen households. In Russia, Ukraine, Uzbekistan, Serbia, Poland and the United Kingdom there were 1,500 household interviews in order to allow for a reasonably large sample for a follow-up telephone survey, which will be based on a shortened version of the current questionnaire and which will be conducted one year after the face-to-face survey, i.e., in autumn 2011.

In the first stage of the sampling, sample frame of Primary Sampling Units were established. In all countries, the most recent available sample frame of Primary Sampling Units (PSUs) was selected as the starting point. Local electoral territorial units were used as PSUs wherever it was possible, as they tend to carry the most up-to-date information about household addresses. The following sampling frames were used:

Electoral districts: Bulgaria, Hungary, Poland, Romania, Serbia. Polling station territories: Albania, Armenia, Belarus, Bosnia and Herzegovina, Moldova, Montenegro. Census Enumeration Districts: Slovak Republic, Sweden, Tajikistan, Turkey. Geo-administrative divisions: the remaining countries.

The second stage in sampling consisted of selecting households within each PSU. The aim was to make sure that each household was selected with an equal probability within any given PSU and hence all households in the country had the same probability of being selected. Two sampling procedures were used. In the majority of countries, a random walk fieldwork procedure was used: the fieldwork coordinator selected the first address to be sampled, and the interviewer was given clear instructions on how to select remaining addresses within the PSUs. For a small number of countries - Hungary, Lithuania, Slovenia and Sweden and the United Kingdom - the sample was pre-selected to ensure that the probability of any household's inclusion was always equivalent to the probability generated by random selection.

The sampling procedures are more fully described in "Life in Transition Survey 2010 - Final Report" pp.114-115.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire of LiST II includes sections on the impact of the crisis and on climate change issues, as well as improved and expanded questions in areas such as corporate governance, public service delivery, and economic and social attitudes.

There are 8 Sections in the questionnaire: Household Roster, Housing and Expenses, Attitudes and Values, Climate Change, Labour, Education and Entrepreneurial Activity, Governance, Miscellaneous Questions, and Impact of the Crisis.

The respondents of the questionnaire are the head of the households or other knowledgeable household members for section 1 and 8. For sections 3-7, the respondents are the people selected randomly by using selection grids.

Response rate

The standard interview method called for each selected household to be visited at least three times before being replaced. In the majority of cases (79 percent), however, the interviews were completed on the first visit. In 61 percent of cases, the head of the household and the principal respondent were the same person; in the remaining 39 percent, two different interviews were required to be carried out in the same household.

In 2024, the average total fertility rate in Taiwan ranged at around **** children per woman over lifetime. This extremely low figure is not expected to increase over the coming years. Taiwan’s demographic development Taiwan was once known for its strong population growth. After the retreat of the Republican government to the island in 1949, the population grew quickly. However, during Taiwan’s rapid economic development thereafter, the fertility rate dropped substantially. This drastic change occurred in most East Asian countries as well, of which many have some of the lowest fertility rates in the world today. As a result, populations in many East Asian regions are already shrinking or are expected to do so soon.In Taiwan, population decreased in 2020 for the first time, and the declining trend is expected to accelerate in the years ahead. At the same time, life expectancy has increased considerably, and Taiwan’s population is now aging at fast pace, posing a huge challenge to the island’s social security net. Addressing challenges of an aging society Most east Asian countries could, until recently, afford generous public pensions and health care systems, but now need to adjust to their changing reality. Besides providing incentives to raise children, the Taiwanese government also tries to attract more immigrants by lowering requirements for permanent residency. As both strategies have been met with limited success, the focus remains on reforming the pension system. This is being done mainly by raising the retirement age, promoting late-age employment, increasing pension contributions, and lowering pension payments.

The Life in Transition Survey, after the crisis (LiTS II), is the second round of LiTS surveys, previously conducted in 2006 (LiTS I). In late 2006, the EBRD and World Bank carried out the first comprehensive survey of individuals and households across virtually the whole transition region. The purpose was to gain a better understanding of how people's lives had been shaped and affected by the upheavals of the previous 15 years. Four years later, the EBRD and World Bank commissioned a second round of the survey. The circumstances facing most people were significantly different between the first and second rounds. The Life in Transition Survey I (LiTS I) was carried out at a time when the region's economies were, with few exceptions, growing strongly. In contrast, LiTS II took place in late 2010, at a time when most countries were still facing the aftershocks of a severe global economic crisis. LiTS II advances and improves on LiTS I in two important ways. First, the questionnaire was substantially revised. The new questionnaire includes sections on the impact of the crisis and on climate change issues, as well as improved and expanded questions in areas such as corporate governance, public service delivery, and economic and social attitudes. Second, the coverage has been expanded to include five western European "comparator" countries France, Germany, Italy, Sweden and the UK. This allows us to benchmark the transition region against some advanced market economies, thereby giving a clearer perspective on the remaining challenges facing transition countries.

Snapshot img

The infertility drugs market share is expected to increase by USD 1.03 billion from 2019 to 2024, and the market’s growth momentum will accelerate at a CAGR of 4.82%.

This infertility drugs market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers infertility drugs market segmentations by product (hormone-based therapy and others) and geography (North America, Europe, APAC, and South America). The infertility drugs market report also offers information on several market vendors, including AbbVie Inc., Allergan Plc, Bayer AG, Ferring Pharmaceuticals AS, GlaxoSmithKline Plc, Merck & Co. Inc., Merck KGaA, Novartis AG, Pfizer Inc., and Sanofi among others.

What will the Infertility Drugs Market Size be During the Forecast Period?

Download the Free Report Sample to Unlock the Infertility Drugs Market Size for the Forecast Period and Other Important Statistics

Infertility Drugs Market: Key Drivers, Trends, and Challenges

Based on our research output, there has been a neutral impact on the market growth during and post COVID-19 era. The growing lifestyle diseases is notably driving the infertility drugs market growth, although factors such as rising inclination toward other modes of treatment for infertility may impede market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the infertility drugs industry. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key Infertility Drugs Market Driver

The rising trend of late parenthood is a key driving factor impacting the global infertility drugs market growth. Increased age of parenthood is one of the major factors leading to the infertility problem in the present generation. In fact, it has severely affected European countries and some Asian countries such as Japan. This growing trend of late motherhood is against the natural range of fertile years for women, which significantly brightens the prospects for the global infertility drugs market. Factors such as the growing use of various contraceptive technologies, increasing focus on higher education and career, and urbanization have led to the rising median age of first pregnancy, leading to falling fertility rates. Globally, more women are working than ever before, which has also increased the average age of conceiving a child. Such factors are creating more complications and often lead to infertility.

Key Infertility Drugs Market Trend

Raising awareness regarding infertility in couples is a key trend impacting the global infertility drugs market growth. There is growing awareness about sexual health, such as fertility issues across the globe. This is expected to act as a positive trend in the infertility treatment industry. Different government and non-government organizations across the globe are supporting the couples to come forward and discuss their problems and seek professional help to tackle their issues. For instance, the International Fertility Alliance (IFA) is on a mission to spread awareness about fertility issues and provide support to people with fertility-related issues. Additionally, the National Fertility Association founded the National Infertility Awareness Week, which focuses on providing the right information and answering queries about infertility.

Key Infertility Drugs Market Challenge

The rising inclination toward other modes of treatment for infertility is a key challenge negatively impacting the global infertility drugs market growth. Rising preference for other modes of treatment such as assisted reproductive technology (ART) is posing tough competition for the infertility drugs market. In developed countries, couples having problems conceiving children are inclining toward ART. IVF is the most adopted form of ART treatment. Some of the other forms of treatment for infertility are listed below. Artificial insemination/Intrauterine insemination (IUI) involves direct injection of sperm into the uterus via a thin, flexible catheter. Couples that face low sperm count in the male partner opt for such treatments.

This infertility drugs market analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2020-2024.

Parent Market Analysis

Technavio categorizes the global Infertility drugs market is a part of the global pharmaceuticals market. Our research report has extensively covered external factors influencing the parent market growth potential in the coming years, which will determine the levels of growth of the infertility drugs

COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.Reasons for undertaking this work:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-30 days + 5% from past 31-56 days - total deaths.We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source used as basis:Stephen A. Lauer, MS, PhD *; Kyra H. Grantz, BA *; Qifang Bi, MHS; Forrest K. Jones, MPH; Qulu Zheng, MHS; Hannah R. Meredith, PhD; Andrew S. Azman, PhD; Nicholas G. Reich, PhD; Justin Lessler, PhD. 2020. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of Internal Medicine DOI: 10.7326/M20-0504.New Cases per Day (NCD) = Measures the daily spread of COVID-19. This is the basis for all rates. 100 News Cases in a day as a spike threshold: Empirically, this is based on COVID-19’s rate of spread, or r0 of ~2.5, which indicates each case will infect between two and three other people. There is a point at which each administrative area’s capacity will not have the resources to trace and account for all contacts of each patient. Thus, this is an indicator of uncontrolled or epidemic trend. Spiking activity in combination with the rate of new cases is the basis for determining whether an area has a spreading or epidemic trend (see below). Source used as basis:World Health Organization (WHO). 16-24 Feb 2020. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Obtained online.Mean of Recent Tail of NCD = Empirical, and a COVID-19-specific basis for establishing a recent trend. The recent mean of NCD is taken from the most recent one third of case days. A minimum of 21 days of cases is required for analysis but cannot be considered reliable. Thus, a preference of 63 days of cases ensures much higher reliability. This analysis is not explanatory and thus, merely represents a likely trend. The tail is analyzed for the following:Most recent 2 days: In terms of likelihood, this does not mean much, but can indicate a reason for hope and a basis to share positive change that is not yet a trend. There are two worthwhile indicators:Last 2 days count of new cases is less than any in either the past five or 6-21 days. Past 2 days has only one or fewer new cases – this is an extremely positive outcome if the rate of testing has continued at the same rate as the previous 5 days or 6 to 21 days. Most recent 5 days: In terms of likelihood, this is more meaningful, as it does represent at short-term trend. There are five worthwhile indicators:Past five days is greater than past 2 days and past 6-21 days indicates the potential of the past 2 days being an aberration. Past five days is greater than past 6-21 days and less than past 2 days indicates slight positive trend, but likely still within peak trend timeframe.Past five days is less than the past 6-21 days. This means a downward trend. This would be an important trend for any administrative area in an epidemic trend that the rate of spread is slowing.If less than the past 2 days, but not the last 6-21 days, this is still positive, but is not indicating a passage out of the peak timeframe of the daily new cases curve.Past 5 days has only one or two new cases – this is an extremely positive outcome if the rate of testing has continued at the same rate as the previous 6 to 21 days. Most recent 6-21 days: Represents the full tail of the curve and provides context for the past 2- and 5-day trends.If this is greater than both the 2- and 5-day trends, then a short-term downward trend has begun. Mean of Recent Tail NCD in the context of the Mean of All NCD, and raw counts of cases:Mean of Recent NCD is less than 0.5 cases per 100,000 = high level of controlMean of Recent NCD is less than 1.0 and fewer than 30 cases indicate continued emergent trend.3. Mean of Recent NCD is less than 1.0 and greater than 30 cases indicate a change from emergent to spreading trend.Mean of All NCD less than 2.0 per 100,000, and areas that have been in epidemic trends have Mean of Recent NCD of less than 5.0 per 100,000 is a significant indicator of changing trends from epidemic to spreading, now going in the direction of controlled trend.Similarly, in the context of Mean of All NCD greater than 2.0

COVID-19 has been a massive trade shock that has disrupted global trade, making the last few years a special phase. Even during normal times, epidemic diseases have acted as trade shocks in specific countries, albeit not to the same extent as COVID-19. For some trade shocks, the situation normalizes after the disease transmission is over; for some, it does not. Thus, specific countries can sometimes lose their original trade ratio due to trade diversion; that is, an epidemic disease could lead to unexpected industry restructuring. To examine this, based on data on 110 WHO members from 1996 to 2018, we use a fixed-effect panel model supported by the Hausman Test to empirically identify whether epidemic diseases can cause trade shocks and trade diversion. We find: First, epidemic disease can lead to negative shocks to a country’s trade growth and its ratio of worldwide trade. Second, with a longer epidemic, the probability of the trade diversion effect increases. Our results hold even after considering country heterogeneity. This presents a considerable concern about the shock of COVID-19 lasting further. Many countries are not just facing the problem of temporary trade shocks, but also the challenge of trade diversions. In particular, the probability of trade diversions is increasing rapidly, especially for late-developed countries due to their lack of epidemic containment and vaccine-producing capabilities. Even middle and high income countries cannot ignore global industry chain restructuring. Forward-looking policies should be implemented in advance; it may be too late when long-term trade damage is shown.

COVID-19 has been a massive trade shock that has disrupted global trade, making the last few years a special phase. Even during normal times, epidemic diseases have acted as trade shocks in specific countries, albeit not to the same extent as COVID-19. For some trade shocks, the situation normalizes after the disease transmission is over; for some, it does not. Thus, specific countries can sometimes lose their original trade ratio due to trade diversion; that is, an epidemic disease could lead to unexpected industry restructuring. To examine this, based on data on 110 WHO members from 1996 to 2018, we use a fixed-effect panel model supported by the Hausman Test to empirically identify whether epidemic diseases can cause trade shocks and trade diversion. We find: First, epidemic disease can lead to negative shocks to a country’s trade growth and its ratio of worldwide trade. Second, with a longer epidemic, the probability of the trade diversion effect increases. Our results hold even after considering country heterogeneity. This presents a considerable concern about the shock of COVID-19 lasting further. Many countries are not just facing the problem of temporary trade shocks, but also the challenge of trade diversions. In particular, the probability of trade diversions is increasing rapidly, especially for late-developed countries due to their lack of epidemic containment and vaccine-producing capabilities. Even middle and high income countries cannot ignore global industry chain restructuring. Forward-looking policies should be implemented in advance; it may be too late when long-term trade damage is shown.

COVID-19 has been a massive trade shock that has disrupted global trade, making the last few years a special phase. Even during normal times, epidemic diseases have acted as trade shocks in specific countries, albeit not to the same extent as COVID-19. For some trade shocks, the situation normalizes after the disease transmission is over; for some, it does not. Thus, specific countries can sometimes lose their original trade ratio due to trade diversion; that is, an epidemic disease could lead to unexpected industry restructuring. To examine this, based on data on 110 WHO members from 1996 to 2018, we use a fixed-effect panel model supported by the Hausman Test to empirically identify whether epidemic diseases can cause trade shocks and trade diversion. We find: First, epidemic disease can lead to negative shocks to a country’s trade growth and its ratio of worldwide trade. Second, with a longer epidemic, the probability of the trade diversion effect increases. Our results hold even after considering country heterogeneity. This presents a considerable concern about the shock of COVID-19 lasting further. Many countries are not just facing the problem of temporary trade shocks, but also the challenge of trade diversions. In particular, the probability of trade diversions is increasing rapidly, especially for late-developed countries due to their lack of epidemic containment and vaccine-producing capabilities. Even middle and high income countries cannot ignore global industry chain restructuring. Forward-looking policies should be implemented in advance; it may be too late when long-term trade damage is shown.

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: World Health Organization (WHO)For more information, visit the Johns Hopkins Coronavirus Resource Center.COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.DOI: https://doi.org/10.6084/m9.figshare.125529863/7/2022 - Adjusted the rate of active cases calculation in the U.S. to reflect the rates of serious and severe cases due nearly completely dominant Omicron variant.6/24/2020 - Expanded Case Rates discussion to include fix on 6/23 for calculating active cases.6/22/2020 - Added Executive Summary and Subsequent Outbreaks sectionsRevisions on 6/10/2020 based on updated CDC reporting. This affects the estimate of active cases by revising the average duration of cases with hospital stays downward from 30 days to 25 days. The result shifted 76 U.S. counties out of Epidemic to Spreading trend and no change for national level trends.Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Correction on 6/1/2020Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Revisions added on 4/30/2020 are highlighted.Revisions added on 4/23/2020 are highlighted.Executive SummaryCOVID-19 Trends is a methodology for characterizing the current trend for places during the COVID-19 global pandemic. Each day we assign one of five trends: Emergent, Spreading, Epidemic, Controlled, or End Stage to geographic areas to geographic areas based on the number of new cases, the number of active cases, the total population, and an algorithm (described below) that contextualize the most recent fourteen days with the overall COVID-19 case history. Currently we analyze the countries of the world and the U.S. Counties. The purpose is to give policymakers, citizens, and analysts a fact-based data driven sense for the direction each place is currently going. When a place has the initial cases, they are assigned Emergent, and if that place controls the rate of new cases, they can move directly to Controlled, and even to End Stage in a short time. However, if the reporting or measures to curtail spread are not adequate and significant numbers of new cases continue, they are assigned to Spreading, and in cases where the spread is clearly uncontrolled, Epidemic trend.We analyze the data reported by Johns Hopkins University to produce the trends, and we report the rates of cases, spikes of new cases, the number of days since the last reported case, and number of deaths. We also make adjustments to the assignments based on population so rural areas are not assigned trends based solely on case rates, which can be quite high relative to local populations.Two key factors are not consistently known or available and should be taken into consideration with the assigned trend. First is the amount of resources, e.g., hospital beds, physicians, etc.that are currently available in each area. Second is the number of recoveries, which are often not tested or reported. On the latter, we provide a probable number of active cases based on CDC guidance for the typical duration of mild to severe cases.Reasons for undertaking this work in March of 2020:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-25 days + 5% from past 26-49 days - total deaths. On 3/17/2022, the U.S. calculation was adjusted to: Active Cases = 100% of new cases in past 14 days + 6% from past 15-25 days + 3% from past 26-49 days - total deaths. Sources: https://www.cdc.gov/mmwr/volumes/71/wr/mm7104e4.htm https://covid.cdc.gov/covid-data-tracker/#variant-proportions If a new variant arrives and appears to cause higher rates of serious cases, we will roll back this adjustment. We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source

Bottle Warmer Market Outlook

According to our latest research, the global bottle warmer market size reached USD 185.6 million in 2024, driven by increasing parental focus on infant care and convenience solutions. The market is expected to grow at a CAGR of 7.2% from 2025 to 2033, reaching an estimated USD 346.1 million by 2033. This robust growth is primarily attributed to rising birth rates in emerging economies, technological advancements in baby care appliances, and growing awareness regarding safe feeding practices. As per our comprehensive analysis, the bottle warmer market is witnessing a transformation with innovative product launches, expanding distribution networks, and a shift in consumer preferences toward safer, more efficient feeding solutions.

One of the primary growth drivers for the bottle warmer market is the rising awareness among parents regarding the importance of feeding infants at optimal temperatures. The increasing number of dual-income households, especially in urban areas, has led to a surge in demand for time-saving and convenient baby care products. Bottle warmers, which help maintain the nutritional value of milk and formula by preventing overheating, have become an essential appliance for modern parents. Additionally, the proliferation of social media and parenting blogs has amplified the dissemination of information about the benefits of using bottle warmers, further fueling market growth. The integration of advanced features such as auto shut-off, digital displays, and multi-functionality in bottle warmers is also attracting a broader consumer base, enhancing the market's overall value proposition.

Technological innovation is another critical factor propelling the bottle warmer market. Manufacturers are increasingly focusing on research and development to introduce products that cater to diverse consumer needs. For instance, the emergence of portable and travel-friendly bottle warmers has addressed the requirements of parents who are frequently on the move. These compact devices, often powered by batteries or USB, offer unmatched convenience and are witnessing high adoption rates. Furthermore, the growing trend of eco-friendly and energy-efficient appliances is encouraging brands to develop bottle warmers with lower power consumption and sustainable materials. This technological evolution not only improves user experience but also aligns with the broader sustainability goals of the baby care industry.

Demographic trends, particularly in Asia Pacific and Latin America, are significantly contributing to the bottle warmer market's expansion. Countries like China, India, and Brazil are experiencing a baby boom and rising disposable incomes, resulting in increased spending on baby care products. Additionally, government initiatives promoting child health and nutrition, coupled with the rapid urbanization of these regions, are creating a conducive environment for market growth. In contrast, North America and Europe, while exhibiting moderate birth rates, are characterized by high product penetration and consumer willingness to invest in premium baby care appliances. The Middle East & Africa region is gradually emerging as a potential market, driven by improving healthcare infrastructure and growing awareness of modern parenting practices.

In addition to technological advancements, the introduction of Bottle Insulators for Night Feedings has emerged as a practical solution for parents seeking to maintain the temperature of milk or formula during late-night feedings. These insulators are designed to keep bottles warm for extended periods, reducing the need for frequent reheating and ensuring that infants receive their feed at an optimal temperature. This innovation is particularly beneficial for parents who prefer to prepare bottles in advance, allowing them to quickly soothe a hungry baby without the hassle of waiting for a bottle warmer. By integrating bottle insulators into their nighttime routine, parents can experience greater convenience and peace of mind, knowing that their child's nutritional needs are met efficiently.

A Jupyter notebook (Python) is provided as part of the supporting information with the code required to reproduce the analysis presented and to generate the graphs and results included in this study. Necessary requirements and dependencies to execute this code are included in the Jupyter notebook file. (IPYNB)

In 2020, the child and old-age dependency ratios in China ranged at around **** and **** percent respectively, summing up to a total dependency ratio of **** percent. While the child dependency ratio is expected to drop slightly and then remain stable, the old-age dependency ratio will rise steadily in coming decades. Age demographics in China With a populace of 1.4 billion people by the end of 2023, China stands the country with the second largest population in the world. Since its foundation in 1949, the PRC has experienced high population growth. With the beginning of the reform period in the end of the 1970s, population growth decreased steadily. Finally, China's population size peaked in 2021 and entered a declining path. Falling birth rates in combination with higher life expectancy led to a continuously increasing median age of the population in China over the past five decades. The median age of the Chinese population is expected to rise further and to reach 50 years by the middle of the century. Development of the dependency ratio China has enjoyed a continuously growing work force since the late 1970s. Simultaneously, the total dependency ratio in China decreased from ** percent in 1970 to about ** percent in 2010. However, an important turning point was reached in 2011, as the total dependency ratio was set to increase again after 30 years of population bonus. As can be seen from the above graph, until 2100, child-dependency is estimated to remain steady around ** to ** percent. Old-age dependency on the other hand is expected to grow from about ** percent in 2010 to ** percent in 2060, implying a growing number of senior citizens that need support from the working population. The shift of age demographics in the near future in China is bound to have ineligible economical and social impacts. To learn more about age demographics in China, take a look at our dossier aging population in China.

In 1800, the population of Japan was just over 30 million, a figure which would grow by just two million in the first half of the 19th century. However, with the fall of the Tokugawa shogunate and the restoration of the emperor in the Meiji Restoration of 1868, Japan would begin transforming from an isolated feudal island, to a modernized empire built on Western models. The Meiji period would see a rapid rise in the population of Japan, as industrialization and advancements in healthcare lead to a significant reduction in child mortality rates, while the creation overseas colonies would lead to a strong economic boom. However, this growth would slow beginning in 1937, as Japan entered a prolonged war with the Republic of China, which later grew into a major theater of the Second World War. The war was eventually brought to Japan's home front, with the escalation of Allied air raids on Japanese urban centers from 1944 onwards (Tokyo was the most-bombed city of the Second World War). By the war's end in 1945 and the subsequent occupation of the island by the Allied military, Japan had suffered over two and a half million military fatalities, and over one million civilian deaths.

The population figures of Japan were quick to recover, as the post-war “economic miracle” would see an unprecedented expansion of the Japanese economy, and would lead to the country becoming one of the first fully industrialized nations in East Asia. As living standards rose, the population of Japan would increase from 77 million in 1945, to over 127 million by the end of the century. However, growth would begin to slow in the late 1980s, as birth rates and migration rates fell, and Japan eventually grew to have one of the oldest populations in the world. The population would peak in 2008 at just over 128 million, but has consistently fallen each year since then, as the fertility rate of the country remains below replacement level (despite government initiatives to counter this) and the country's immigrant population remains relatively stable. The population of Japan is expected to continue its decline in the coming years, and in 2020, it is estimated that approximately 126 million people inhabit the island country.

Between the Wall Street Crash of 1929 and the end of the Great Depression in the late 1930s, the Soviet Union saw the largest growth in its gross domestic product, growing by more than 70 percent between 1929 and 1937/8. The Great Depression began in 1929 in the United States, following the stock market crash in late October. The inter-connectedness of the global economy, particularly between North America and Europe, then came to the fore as the collapse of the U.S. economy exposed the instabilities of other industrialized countries. In contrast, the economic isolation of the Soviet Union and its detachment from the capitalist system meant that it was relatively shielded from these events. 1929-1932 The Soviet Union was one of just three countries listed that experienced GDP growth during the first three years of the Great Depression, with Bulgaria and Denmark being the other two. Bulgaria experienced the largest GDP growth over these three years, increasing by 27 percent, although it was also the only country to experience a decline in growth over the second period. The majority of other European countries saw their GDP growth fall in the depression's early years. However, none experienced the same level of decline as the United States, which dropped by 28 percent. 1932-1938 In the remaining years before the Second World War, all of the listed countries saw their GDP grow significantly, particularly Germany, the Soviet Union, and the United States. Coincidentally, these were the three most powerful nations during the Second World War. This recovery was primarily driven by industrialization, and, again, the U.S., USSR, and Germany all experienced the highest level of industrial growth between 1932 and 1938.

Abstract

The closure of the so-called "Balkan route" and the EU-Turkey Statement in March 2016 changed Greece from a transit country to a country hosting a growing population of refugees and asylum seekers. To address the needs of this growing population staying on the Greek mainland, the Greek Government established Open Reception Facilities (ORFs) in Northern and Central Greece. In the beginning of 2016, UNHCR through its partners established urban accommodation schemes to host asylum seekers eligible for relocation as part of the European solidarity measures. The program evolved to focus on the most vulnerable asylum seekers for whom accommodation in the ORFs was unsuitable. The Norwegian Refugee Council (NRC) set up a similar accommodation program in late 2016 also focusing on the most vulnerable. Arrivals at the Greek-Turkish land border increased in late 2017 and as a result a higher number of people started arriving directly to Thessaloniki, without having presented themselves to the authorities at the border. Hence, they were not registered by the Greek authorities and as a consequence lacked access to a dignified shelter, or other forms of basic assistance available to asylum seekers and refugees. The Municipality of Thessaloniki and the humanitarian community jointly decided to conduct a profiling exercise of the refugees and asylum seekers hosted in Thessaloniki as well as Third Country Nationals not registered with the Asylum Service in Thessaloniki. The objective was to explore the extent to which refugees and asylum seekers were moving towards local integration. This was done by looking at their outlook for the future as well as the obstacles and possibilities towards greater economic and socio-cultural integration in Greece. The analysis of persons with no asylum service documentation focused on the key challenges faced by those groups, such as lack of a regularized status and homelessness. The collected data would form a baseline for future integration monitoring and would additionally be a useful tool for the implementation of integration activities as foreseen in national and local strategies for integration. The survey included a total of 861 households. The survey found out that the great majority of refugees and asylum seekers in the accommodation scheme and in the ORF had been in Thessaloniki less than one year. The majority of the households in the accommodation scheme (60%) reported that they intended to stay in Thessaloniki in the long term, and one of the main conditions for being able to integrate locally is finding employment. Amongst the households in the ORF, less than half intended to stay in Thessaloniki (45%) and more than a third (38%) intended to move to another EU country. For those intending to stay, being able to integrate locally was very much linked to finding a different accommodation solution. The households having found their own accommodation were on average living longer in Thessaloniki, as almost half of them had lived in the city for more than one year compared to other groups who have been living in their majority in their accommodation for less than one year. This group of refugees and asylum seekers also included the biggest group reporting that they intended to stay in Thessaloniki longer term (76%). For them the main condition for local integration was access to employment and getting the status of international protection. Accessing employment as a key condition for local integration was also highlighted and confirmed during community consultations with asylum seekers and refugees.

Geographic coverage

Thessaloniki.

Analysis unit

Household and individual

Sampling procedure

In total, the survey of refugees and asylum seekers covered 1,808 individuals comprising 641 households. The sample was stratified by accommodation type into three strata: - Those in the urban accommodation scheme who have been provided with apartments - Those self-accommodated in Thessaloniki, i.e. are either renting an apartment by themselves, or being hosted by friends, relatives or volunteers - Those who were fully registered residents of the Open Reception Facilities (ORF)

The sampling frame for refugees and asylum seekers was UNHCR's ProGres database, while for the ORF, a site population list provided by the camp manager was used as a basis to generate a sample. A simple random sample of households was initially drawn for the accommodation scheme strata and the self-accommodated strata shortly before the data collection was due to begin. During data collection, reaching a majority of the sampled households was challenging due to the listed phone numbers being outdated, as persons of concern often change their pre-paid SIM cards. Unannounced home visits were not an option given time and resource constraints. It was therefore decided to aim for full coverage of both these strata, expecting that a high proportion of the persons in the ProGres database for these strata would not be reachable by phone. To assess potential bias introduced by this approach, the demographic profile of the surveyed persons was compared to that of the whole population of refugees and asylum seekers in the UNHCR ProGres database. The age and sex figures of the population were compared to the survey figures. The sample distributions resemble the population distributions quite closely on the basis of these demographic characteristics. As such, the overall impression is that there is little skew in the survey data for these two strata. It is therefore assumed that the survey results are representative and can be applied to the population as a whole. For the strata of the Open Reception Facility (ORF), the most update site registration list was obtained from the Reception and Identification Service (RIS) that manages the site. The enumerators managed to get in touch with at least one representative of each of the registered households living in the site at the time of the data collection. No one declined the request for an interview. It was not relevant to compare the surveyed population to the UNHCR database list to assess representativity, given that the population in the site had changed significantly since the list for that strata had been assembled by the camp manager in the site. Since a full count of the site population was achieved, the results are considered to be representative for the population.

A different sampling took place for third country nationals not registered with the Asylum Service. The unified registry for persons with police notes (EURODAC II) could not be accessed for the purpose of the profiling study. Although organizations that provide assistance to police note holders hold information about this population group, including UNHCR which provides cash assistance, there is no exhaustive list. Similarly there is no unified registry for undocumented persons. However, through comparing aggregated information from multiple service providers, a population figure of 200 households was estimated as a rough baseline. In the absence of a registry, it was not possible to construct a list from which a random sample could be drawn. Thus, a non-probability sampling strategy was applied, which included convenience sampling approaches. With non-probability approaches it is not possible to establish how well the sample represents the population unless all members of a given target group have been interviewed. Convenience sampling is a type of non-probability sampling method, where the sample is taken from a group of people easy to contact or to reach, e.g. by snowballing techniques where respondents identify other respondents known to them. The enumeration team interviewed 451 persons making up 227 households under the category of third country nationals not registered with the Asylum Service. This number of households interviewed was slightly higher than the number originally foreseen, a possible explanation for this being the aforementioned influx of arrivals to Thessaloniki the same month. The survey results support this theory, as more than half of the survey respondents from this target group had been in Thessaloniki for less than a month at the time of the interview. The high number of recent arrivals made the estimate of the total population more uncertain. In addition, many of the persons who were approached, declined to be interviewed. As a result, it is difficult to assess how representative the interviewees were of the target group.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire used to collect the data consists of the following sections: migration history family unity & mobility, housing, basic demographics, education, employment & work stats, household economy, access to health admin social and humanitarian services, social links and interaction, future intentions, social and cultural integration.

Cleaning operations

Data was anonymized through decoding and local suppression.

In the year 1500, the share of Western Europe's population living in urban areas was just six percent, but this rose to 31 percent by the end of the 19th century. Despite this drastic change, development was quite slow between 1500 and 1800, and it was not until the industrial revolution when there was a spike in urbanization. As Britain was the first region to undergo the industrial revolution, from around the 1760s until the 1840s, these areas were the most urbanized in Europe by 1890. The Low Countries Prior to the 19th century, Belgium and the Netherlands had been the most urbanized regions due to the legacy of their proto-industrial areas in the medieval period, and then the growth of their port cities during the Netherlands' empirical expansion (Belgium was a part of the Netherlands until the 1830s). Belgium was also quick to industrialize in the 1800s, and saw faster development than its larger, more economically powerful neighbors, France and Germany. Least-urban areas Ireland was the only Western European region with virtually no urbanization in the 16th and 17th century, but the industrial growth of Belfast and Dublin (then major port cities of the British Empire) saw this change by the late-1800s. The region of Scandinavia was the least-urbanized area in Western Europe by 1890, but it saw rapid economic growth in Europe during the first half of the following century.

Globally, about 25 percent of the population is under 15 years of age and 10 percent is over 65 years of age. Africa has the youngest population worldwide. In Sub-Saharan Africa, more than 40 percent of the population is below 15 years, and only three percent are above 65, indicating the low life expectancy in several of the countries. In Europe, on the other hand, a higher share of the population is above 65 years than the population under 15 years. Fertility rates The high share of children and youth in Africa is connected to the high fertility rates on the continent. For instance, South Sudan and Niger have the highest population growth rates globally. However, about 50 percent of the world’s population live in countries with low fertility, where women have less than 2.1 children. Some countries in Europe, like Latvia and Lithuania, have experienced a population decline of one percent, and in the Cook Islands, it is even above two percent. In Europe, the majority of the population was previously working-aged adults with few dependents, but this trend is expected to reverse soon, and it is predicted that by 2050, the older population will outnumber the young in many developed countries. Growing global population As of 2025, there are 8.1 billion people living on the planet, and this is expected to reach more than nine billion before 2040. Moreover, the global population is expected to reach 10 billions around 2060, before slowing and then even falling slightly by 2100. As the population growth rates indicate, a significant share of the population increase will happen in Africa.