As of 2043, Nairobi was the most populated city in Kenya, with more than 2.7 million people living in the capital. The city is also the only one in the country with a population exceeding one million. For instance, Mombasa, the second most populated, has nearly 800 thousand inhabitants. As of 2020, Kenya's population was estimated at over 53.7 million people.

This statistic shows the biggest cities in Kenya as of 2019. In 2019, approximately *** million people lived in Nairobi, making it the biggest city in Kenya.

Kenya KE: Population in Largest City: as % of Urban Population data was reported at 31.985 % in 2017. This records a decrease from the previous number of 32.132 % for 2016. Kenya KE: Population in Largest City: as % of Urban Population data is updated yearly, averaging 35.120 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 50.731 % in 1962 and a record low of 31.985 % in 2017. Kenya KE: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted average;

Population in largest city in Kenya was reported at 5766989 in 2025, according to the World Bank collection of development indicators, compiled from officially recognized sources. Kenya - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2026.

Population in the largest city (% of urban population) in Kenya was reported at 32.68 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Kenya - Population in the largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on February of 2026.

This data was used in maps throughout Nature's Benefits in Kenya: An Atlas of Ecosystems and Human Well-Being.

View yearly updates and historical trends for Kenya Population in the Largest City. Source: World Bank. Track economic data with YCharts analytics.

KE: Population in Largest City data was reported at 4,222,389.000 Person in 2017. This records an increase from the previous number of 4,065,018.000 Person for 2016. KE: Population in Largest City data is updated yearly, averaging 1,285,227.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 4,222,389.000 Person in 2017 and a record low of 292,622.000 Person in 1960. KE: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

Nairobi is the most populated county in Kenya. The area formed by the country's capital and its surroundings has a population of over 4.3 million inhabitants. Of the 47 counties in Kenya, 18 have a population of more than one million people.

The objective of the survey was to produce baselines for 15 large urban centers in Kenya. The urban centers covered Nairobi, Mombasa, Naivasha, Nakuru, Malindi, Eldoret, Garissa, Embu, Kitui, Kericho, Thika, Kakamega, Kisumu, Machakos, and Nyeri. The survey covered the following issues: (a) household characteristics; (b) household economic profile; (c) housing, tenure, and rents; and (d) infrastructure services. The survey was undertaken to deepen understanding of the cities’ growth dynamics, and to identify specific challenges to quality of life for residents. The survey pays special attention to living conditions for residents of formal versus informal settlements, poor versus non-poor, and male and female headed households.

Abstract

The objective of the survey was to produce baselines for 15 large urban centers in Kenya. The urban centers covered Nairobi, Mombasa, Naivasha, Nakuru, Malindi, Eldoret, Garissa, Embu, Kitui, Kericho, Thika, Kakamega, Kisumu, Machakos, and Nyeri. The survey covered the following issues: (a) household characteristics; (b) household economic profile; (c) housing, tenure, and rents; and (d) infrastructure services. The survey was undertaken to deepen understanding of the cities’ growth dynamics, and to identify specific challenges to quality of life for residents. The survey pays special attention to living conditions for residents of formal versus informal settlements, poor versus non-poor, and male and female headed households.

Analysis unit

Household Urban center

Kind of data

Sample survey data [ssd]

Sampling procedure

The Kenya State of the Cities Baseline Survey is aimed to produce reliable estimates of key indicators related to demographic profile, infrastructure access and economic profile for each of the 15 towns and cities based on representative samples, including representative samples of households (HHs) residing in slum and non-slum areas. For this baseline household survey, NORC used a two- or three-stage stratified cluster sampling design within each of the 15 urban centers. Our first-stage sampling frame was based on the 2009 census frame of enumeration areas. For each of the 15 towns and cities, NORC received the sampling frame of EAs from the Kenya National Bureau of Statistics (KNBS). In the first stage, NORC selected a sample of enumeration areas (PSUs). The second stage involved a random selection of households (SSUs) from each selected EA. In order to manage the field interviewing efficiently, we drew a fixed number of HHs from each selected EA, irrespective of EA size. The third stage arose in instances of very large EAs (EAs containing more than 200 households) in which EAs were divided into 2, 3 or 4 segments, from which one segment was selected randomly for household selection.

Stratification of Enumeration Areas: A few stratification factors were available for stratifying the EAs to help to achieve the survey objectives. As mentioned earlier, for this baseline survey we wanted to draw representative samples from slum and non-slum areas and also to include poor/non-poor households (HHs). For the 2009 census, depending on the location, KNBS divided the EAs into three categories: rural, urban, and peri-urban.

Although there is a clear distinction of EAs into slum and non-slum areas, it is hard to classify EAs into poor and non-poor categories. To guarantee enough representation of HHs living in slum and non-slum areas (also referred to as formal and informal areas) as well as HHs living below and above the poverty line, NORC stratified the first-stage sampling units (EAs) into strata, based on EA type (3 types) and settlement type (2 types). Given the resources available, we believe this stratification would serve our purpose as HHs living in slum and in rural areas tend to be poor. Table 1 in Appendix C of final Overview Report (provided under the Related Materials tab) presents the allocation of sampled EAs across the strata for each of the 15 cities in the baseline survey.

Sampling households is not as straightforward as the first-stage sampling of EAs, since the 2009 census frame of HHs does not exist. In the absence of a household sampling frame, NORC carried out a listing of HHs within each EA selected in the first stage. Trained listers, accompanied by local cluster guides (local residents with some form of authority in the EA), systematically listed all households in each selected EA, gathering the address, names of head of household and spouse, household description, latitude and longitude. To ensure completeness of listing data, avoid duplication and improve ease of locating households that were eventually selected for interview, listers enumerated households by chalking household identification number above the household doorway (an accepted practice for national surveys). The sampling frame of HHs produced from the listing activity was, therefore, up-to-date and included new formal and informal settlements that appeared after the 2009 census.

For adequate representativeness and to manage the interviewing task efficiently, NORC planned seven completed household interviews per EA. The final recommended sample size for the Kenya State of the Cities baseline survey is found in Table 2 in Appendix C of the final Overview Report.

Because the expected response rate was unknown prior to the start of the field period, the sampling team randomly selected ten households per enumeration area and distributed them to the interviewers working within the EA. Interviewing teams were instructed to complete at least seven interviews per EA from among the ten selected households. Interviewers were instructed to attempt at least three contacts with each selected household, approaching potential respondents on different days of the week and different times of day. Table 2 presents the final number of EAs listed per city and the final number of completed interviews per city. The table also presents the percent of planned EAs and interviews that were completed vs. planned. Please note that in several cities more interviews were completed than planned. As part of NORC's data quality plan, data collection teams were instructed to overshoot slightly the target of seven interviews per EA, if feasible, to mitigate any potential loss of cases due to poor quality or uncooperative respondents. Few cases were lost due to poor quality, therefore the target number of interviews remains over 100 percent in ten of the fifteen cities.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was developed by World Bank staff with input from stakeholders in the Kenya Municipal Program and NORC researchers and survey methodologists. The base questionnaire for the project was a 2004 World Bank survey of Nairobi slums. However, an extended iterative review process led to many changes in the questionnaire. The final version that was used for programming provided under the Related Materials tab, and in Volume II of the Overview.

The questionnaire’s topical coverage is indicated by the titles of its nine modules: 1. Demographics and household composition 2. Security of housing, land and tenure 3. Housing and settlement profile 4. Economic profile 5. Infrastructure services 6. Health 7. Household enterprises7 8. Civil participation and respondent tracking

Response rate

The completion rate is reported as the number of households that successfully completed an interview over the total number of households selected for the EA. These are shown by city in Table 5 in Appendix C of the final Overview Report, and have an average rate of 68.66 percent, with variation from 66 to 74 percent (aside from Nairobi at 61.47 percent and Machakos at 56 percent). As described earlier, ten households were selected per EA if the EA contained more than 10 households. For EAs where fewer than ten households were selected for interviews, all households were selected. In some EAs, more than ten households were selected due to a central office error.

Kenya is a country in Eastern Africa. At 580,367 square kilometres (224,081 sq mi), Kenya is the world's 48th largest country by area. With a population of more than 47.6 million in the 2019 census, Kenya is the 29th most populous country. Kenya's capital and largest city is Nairobi, while its oldest city and first capital is the coastal city of Mombasa. Kisumu City is the third-largest city and also an inland port on Lake Victoria. Other important urban centres include Nakuru and Eldoret. As of 2020, Kenya is the third-largest economy in sub-Saharan Africa after Nigeria and South Africa. Kenya is bordered by South Sudan to the northwest, Ethiopia to the north, Somalia to the east, Uganda to the west, Tanzania to the south, and the Indian Ocean to the southeast.

Source: Objaverse 1.0 / Sketchfab

Mean body and leg titers for Aedes aegypti from three major cities in Kenya exposed to chikungunya virus.

Abstract

African Population and Health Research Center (APHRC) had from 2005 to 2010, conducted a longitudinal survey in two formal settlements (Harambee and Jericho) and two informal (slum) settlements (Korogocho and Viwandani) in Nairobi to understand the uptake and patterns of school enrolment after the introduction of the Free Primary Education (FPE) in Kenya. The results of the study showed increased utilization of private informal schools among slum households as compared to the formal settlements.

That is, by 2010, almost two thirds of pupils in the slum settlements were enrolled in private informal schools while in Harambee and Jericho, more than three quarters of the pupils were enrolled in government primary schools with the remaining portion attending high-end private schools.

In 2012, ERP conducted a cross-sectional survey across six major urban centers to investigate, within the context of FPE, if the pattern of school enrolment observed in Korogocho and Viwandani slums could also be observed in other urban slums in Kenya. Below are some key facts from this study. Data is manly disaggregated by school type - government schools (FPE schools), and non-government schools, specifically the formal private schools and low-cost schools.

The study tried to answer four broad questions: What is the impact of free primary education (FPE) on schooling patterns among poor households in urban slums in Kenya? What are the qualitative and quantitative explanations of the observed patterns? Is there a difference in achievement measured by performance in a standardized test on literacy and numeracy administered to pupils in government schools under FPE and non-government schools?

Geographic coverage

Kenya - in six urban slums of Nairobi spread across 6 towns - Nairobi, Mombasa, Nyeri, Eldoret, Nakuru and Kisumu. In total 5854 households and 230 schools were covered.

Analysis unit

A cross-sectional survey focusing on households with individuals aged between 5 and 19, as well as schools and pupils in grades 3 and 6. Data therefore exits at household, individuals, schools and student levels.

Universe

This is a cross sectional study that was conducted in seven slum sites spread across six towns namely Nairobi, Mombasa, Kisumu, Eldoret, Nakuru and Nyeri and targetted hoseholds with individuals aged between 5 and 19 years and schools located within the study site or within a 1KM radius. For the schools to be included in the study they had to have both grade 3 and 6, which were target grades for this study.

Sampling procedure

This was a cross-sectional study involving schools and households. The study covered six purposively selected major towns (Eldoret, Kisumu, Mombasa, Nairobi, Nakuru and Nyeri) in different parts of Kenya (see Map 1) to provide case studies that could lead to a broader understanding of what is happening in urban informal settlements. The selection of a town was informed by presence of informal settlements and its administrative importance, that is, provincial headquarter or regional business hub. A three-stage cluster sampling procedure was used to select households in all towns with an exception of Nairobi. At the first stage, major informal settlement locations were identified in each of the six towns. The informal settlement sites were identified based on enumeration areas (EAs) designated as slums in the 2009 National Population and Housing Census conducted by the Kenya National Bureau of Statistics (KNBS). After identifying all slum EAs in each of the study towns, the location with the highest number of EAs designated as slum settlements was selected for the study. At the second stage of sampling, 20% of EAs within each major slum location were randomly selected. However, in Nakuru we randomly selected 67% (10) EAs while in Nyeri all available 9 EAs were included in the sample. This is because these two towns had fewer EAs and therefore the need to oversample to have a representative number of EAs. In total, 101 EAs were sampled from the major slum locations across the five towns. At the third stage, all households in the sampled EAs were listed using the 2009 census' EA maps prepared by KNBS. During the listing, 10,388 households were listed in all sampled EAs. Excluding Nairobi, 4,042 (57%) households which met the criteria of having at least one school-going child aged 5-20 years were selected for the survey. In Nairobi, 50% of all households which had at least one school-going child aged between 5 and 20 years were randomly sampled from all EAs existing in APHRC schooling data collected in 2010. A total of 3,060 households which met the criteria were selected. The need to select a large sample of households in Nairobi was to enable us link data from the current study with previous ones that covered over 6000 households in Korogocho and Viwandani. By so doing, we were able to get a representative sample of households in Nairobi to continue observing the schooling patterns longitudinally. In all, there were 7,102 eligible households in all six towns. A total of 14,084 individuals within the target age bracket living in 5,854 (82% of all eligible households) participated in the study. The remaining 18% of eligible households were not available for the interview as most of them had either left the study areas, declined the interview, or lacked credible respondents in the household at the time of the data collection visit or call back.

For the school-based survey, schools in each town were listed and classified into three groups based on their location: (i) within the selected slum location; (ii) within the catchment area of the selected slum area - about 1 km radius from the border of the study locations; and (iii) away from a selected slum. In Nairobi, schools were selected from existing APHRC data. During the listing exercise, lists of schools were also obtained from Municipality/City Education Departments in selected towns. The lists were used to counter-check the information obtained during listing. All schools located within the selected slum areas and those situated within the catchment area (1 km radius from the border of the slum) were included in the sample as long as they had a grade 6 class or intended to have one in 2012. The selection of schools within an informal settlement and those located within 1 km radius was because they were more likely to be accessed by children from the target informal settlement. Two hundred and forty-five (245) schools met the selection criteria and were included in the sample. Two hundred and thirty (230) primary schools (89 government schools, 94 formal private, and 47 low-cost schools) eventually participated in the survey. A total of 7,711 grade 3, 7,319 grade 6 pupils and 671 teachers of the same grades were reached and interviewed. All 230 head teachers (or their deputies) were interviewed on school characteristics.

Mode of data collection

Face-to-face [f2f]; Focus groups; Assessment; Filming (classroom observation).

Research instrument

Five survey questionnaires were administered at household level:

(i). An individual schooling history questionnaire was administered to individuals aged 5 - 20. The questionnaire was directly administered to individuals aged 12 - 20 and administered to a proxy for children younger than 12 years. Ideally, the proxy was the child's parent or guardian, or an adult familiar with the individual's schooling history and who usually resides in the same household. The questionnaire had two main sections: school participation for the current year (year of interview), and school participation for the five years preceding the year of interview (i.e. 2007 to 2011). The section on schooling participation on the current year collected information on the schooling status of the individual, the type, name and location of the school that the individual was attending, grade, and whether the individual had changed schools or dropped out of school in the current year. Respondents also provided information on the reasons for changing schools and dropping out of school, where applicable. The section on school participation for previous years also collected similar information. The questionnaire also collected information on the individual's year of birth and when they joined grade one.

(ii). A household schedule questionnaire was administered to the household head or the spouse. It sought information on the members of the household, their relationship to the household head, their gender, age, education and parental survivorship.

(iii). A parental/guardian perception questionnaire was administered to the household head or the parent/guardian of the child. It collected information on the parents/guardians' perceptions on Free Primary Education since its implementation, household support to school where child(ren) attends and household schooling decision.

(iv). A parental/guardian involvement questionnaire was strictly administered to a parent or guardian who usually lives in the household and who was equipped with adequate knowledge of the individual's schooling information (i.e. credible respondent). The questionnaire was completed for each individual of the targeted age bracket (5-20 years). The information on the child comprised questions on the gender of the child, parental/guardian aspirations for the child's educational attainment, and parental beliefs about the child's ability in school and their chances of achieving the aspired level.

(v). A household amenities and livelihood questionnaire was administered to the household head or the spouse or a member of the household who could give reliable information. The questionnaire collected information on duration of stay in the

Major urban centers like Nairobi, Mombasa, and Kisumu, plus regional trade hubs near the port of Mombasa, dominate the Kenya B2B marketplace landscape. These cities host dense clusters of informal retail (dukas), wholesalers, and distribution nodes. Nairobi’s superior logistics connectivity, concentration of manufacturers and supplier headquarters, and higher digital penetration make it a natural epicenter. The coastal corridor’s proximity to the Port of Mombasa enables efficient import flows for cross-border B2B supply, giving coastal towns an edge in supplier sourcing and inventory turnover. Secondary cities along major trade corridors (e.g. Nakuru, Eldoret) benefit from spill-over distribution demand. The Kenya B2B marketplace market size is estimated to be USD 1.2 billion in 2023, based on aggregated platform and marketplace revenue disclosures from leading firms coupled with public data on Kenya’s e-commerce growth. This figure is underpinned by a broader e-commerce environment in Kenya where the entire e-commerce GMV for 2023 was ~ USD 2.3 billion (for B2C + others). The B2B subset captures orders placed by informal retailers, small enterprises, clinics, and HORECA via digital platforms. Growth is driven by accelerated mobile penetration, spread of FinTech and digital credit, MSME digitization mandates, and logistics investments. In 2024, the broader e-commerce market is projected to reach USD 2.6 billion, which implies further tailwinds for the B2B segment as digital procurement gains deeper adoption. Kenya B2B Marketplaces Market Overview and Size

Abstract

The Kenya Innovation Survey is a national innovation survey undertaken from March to June 2012. The survey was designed to measure the innovation activity based on a set of core indicators to inform policies that will help the country configure the national system of innovation in order to respond to socio-economic challenges. The survey was based on the Oslo Manual by OECD. The survey covered Nairobi, Mombasa, Kisumu, Nakuru and Eldoret.

This innovation survey, the first in Kenya, was carried out in order to generate crucial learning lessons to inform the planning of the main national innovation survey to be undertaken at a later date. However, the overall objective of the innovation survey, being part of the national ST&I system of indicators that is under development, is to build Kenya’s capacity to develop and use innovation indicators in designing and implementing ST&I policies and strategies for national development.

The survey is therefore an attempt to probe the activity of innovation through the collection of data on various aspects of innovation in order to develop relevant innovation indicators and specific innovation policies for the country. These indicators will then enable key stakeholders to understand the state of the national innovation system and its capacity to deliver the intended results so as to address the components that need attention.

The innovation survey is designed to: • Develop and cause the adoption of internationally comparable innovation indicators; • Build human and institutional capacities to collect innovation indicators; • Inform the country on the state of innovation; and • Provide both qualitative and quantitative data on innovation at firm level.

Geographic coverage

Firms in Major Towns of Kenya (urban)

Mombasa City, Nakuru Town, Eldoret Town and Kisumu City

Analysis unit

• Firms
• Establishment

Universe

Selected towns

Kind of data

Aggregate data [agg]

Sampling procedure

The sample frame consisted of all registered firms, public/private universities and public research institutions, national polytechnics and NGOs. The firms were randomly selected by ISIC sector from the frame. A total of 194 firms were selected in Nairobi and its environs while 102 firms were selected upcountry as follows: Mombasa (25 firms), Kisumu (25 firms), Eldoret (24 firms) and Nakuru (25 firms).

Mode of data collection

Other [oth]

Research instrument

The questionnaire was divided into eleven parts as follows: • Part 1: General information of the firm • Part 2: Product (goods or services) innovation • Part 3: Process innovation • Part 4: Ongoing or abandoned Innovation activities • Part 5: Performed innovation activities and expenditures • Part 6: Sources of information and co-operation for innovation activities • Part 7: Effects / Objectives of innovation • Part 8: Factors hampering innovation activities • Part 9: Intellectual property rights • Part 10: Organizational and marketing innovation • Part 11: Specific innovations

NOTE: The full questionnaire is attached in the external resource

Cleaning operations

In this survey, data processing personnel were drawn from the Kenya National Bureau of Statistics assisted by some officers from the Ministry of Higher Education, Science and Technology. The questionnaires were received from the field, recorded and edited in preparation for data capture.

Data processing and analysis were done at the Kenya National Bureau of Statistics. The Census and Survey Software Programme (CSPro) was used for data capture,editing, validation and tabulation. In developing the data capture system, certain controls were in-built to check the characters entered afterwhich validation was done in preparation for the production of frequency tables and in readiness for data analysis.

Response rate

The Innovation Survey covered business firms in Nairobi, Mombasa, Kisumu, Nakuru and Eldoret. A total of 293 firms were targeted in this innovation survey. Out of these, 160 firms completed and returned the questionnaires, thus representing a 54.6 percent overall response rate. The different regions response rate are listed as follows: Nairobi - 43.3% Mombasa - 68.0% Kisumu - 60.0% Nakuru - 92.0% Eldoret - 87.5% Total - 54.6%

Abstract

Rapid urbanization amidst stagnating economies and poor governance have created a new face of abject poverty concentrated in overcrowded informal settlements, commonly called slums, in Africa's major cities. UN-HABITAT estimates that about 72% of urban residents in sub-Saharan Africa live in slums. Residents therein are often more unhealthy than their rural counterparts because they are deprived of basic public social services such as health care, water supply, sanitation and garbage disposal. Slum dwellers, exhibit relatively high mortality rates because they are less likely to access preventative and curative medical care despite their proximity to the best hospitals and clinics located in cities. The UN projects that more Africans will live in urban than rural areas by 2016 and that over 300 million urban Africans will live in slums by 2020. Evidently, poor health outcomes among slum residents will increasingly shape national indicators and frustrate overall progress in attaining the Millennium Development Goals. Slum dwellers have unique vulnerabilities. The absence of public health services in slums has resulted in a vibrant private health sector that offers cheap, but ineffective and sometimes dangerous treatments and procedures. The private sector is poorly organized and poorly regulated. Most private providers are under (or un)-qualified, operate in one-room structures, and lack basic equipment and supplies.

Moreover, most healthcare programs, which are mostly based on the rural public health sector, may not be readily transferable to urban slums. The delivery of primary health care (PHC) to slum residents has therefore failed because of government absence and lack of lessons on how best to utilize existing resources in the private sector. The African Population and Health Research Center (APHRC), Population Council (PopCouncil), AMREF-Kenya and JHPIEGO - an affiliate of Johns Hopkins University offered the Doris Duke Charitable Foundation's African Health Initiative this Letter of Interest. Under the name Partnership for a Healthy Nairobi (PHN), the team focused on overcoming obstacles that limit the capacity of both public and private health systems to deliver integrated primary health care (PHC) to residents in three slum settlements of Nairobi - Korogocho, Viwandani and Kibera. These settlements house at least 650,000 people in an area of only four square kilometers.

The objectives were:

i) To demonstrate the feasibility and cost-effectiveness of forging public-private partnerships to deliver integrated PHC in slum settings;

ii) To test the feasibility of implementing the Community Based Kenya Essential Package for Health (CB-KEPH) in a slum setting and its impact on health outcomes ;

iii) To evaluate the impact of integrated PHC on morbidity and mortality in slum settings.

Geographic coverage

Three informal settlements, Korogocho, Viwandani and Kibera, in Nairobi City (the capital city) of Kenya.

Analysis unit

The unit of analysis for various sections included:

Civil society organizations

Health Facilities and

Individual midwives

Universe

Midwives

Health Facilities

Civil Society Organizations

Mode of data collection

Face-to-face [f2f]

Research instrument

A total of six tools were administered. These include:

1. The Civil Society Organisation Assessment

2. Drug Store Assessment

3. Health Facilities Checklist

4. Health Facilities Assessment Tool

5. Community Midwifery Tool

6. Staff Training Tool

Cleaning operations

Data editing took place at a number of stages throughout the processing, including:

a) Office editing and coding

b) During data entry

c) Structure checking and completeness

d) Secondary editing

Detailed documentation of the editing of data can be found in the "Standard Procedures Manual" document provided as an external resource.

Some corrections are made automatically by the program (80%) and the rest by visual control of the questionnaire (20%).

Nigeria has the largest population in Africa. As of 2025, the country counted over 237.5 million individuals, whereas Ethiopia, which ranked second, has around 135.5 million inhabitants. Egypt registered the largest population in North Africa, reaching nearly 118.4 million people. In terms of inhabitants per square kilometer, Nigeria only ranked seventh, while Mauritius had the highest population density on the whole African continent in 2023. The fastest-growing world region Africa is the second most populous continent in the world, after Asia. Nevertheless, Africa records the highest growth rate worldwide, with figures rising by over two percent every year. In some countries, such as Chad, South Sudan, Somalia, and the Central African Republic, the population increase peaks at over 3.4 percent. With so many births, Africa is also the youngest continent in the world. However, this coincides with a low life expectancy. African cities on the rise The last decades have seen high urbanization rates in Asia, mainly in China and India. African cities are also growing at large rates. Indeed, the continent has three megacities and is expected to add four more by 2050. Furthermore, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria, by 2035.

Urbanization is one of the major drivers of dengue epidemics globally. In Kenya, an intriguing pattern of urban dengue virus epidemics has been documented in which recurrent epidemics are reported from the coastal city of Mombasa, whereas no outbreaks occur in the two major inland cities of Kisumu and Nairobi. In an attempt to understand the entomological risk factors underlying the observed urban dengue epidemic pattern in Kenya, we evaluated vector density, human feeding patterns, vector genetics, and prevailing environmental temperature to establish how these may interact with one another to shape the disease transmission pattern. We determined that (i) Nairobi and Kisumu had lower vector density and human blood indices, respectively, than Mombasa, (ii) vector competence for dengue-2 virus was comparable among Ae. aegypti populations from the three cities, with no discernible association between susceptibility and vector cytochrome c oxidase subunit 1 gene variation, and (iii) vector competence was temperature-dependent. Our study suggests that lower temperature and Ae. aegypti vector density in Nairobi may be responsible for the absence of dengue outbreaks in the capital city, whereas differences in feeding behavior, but not vector competence, temperature, or vector density, contribute in part to the observed recurrent dengue epidemics in coastal Mombasa compared to Kisumu.

KE：最大城市人口在12-01-2017达4,222,389.000人，相较于12-01-2016的4,065,018.000人有所增长。KE：最大城市人口数据按年更新，12-01-1960至12-01-2017期间平均值为1,285,227.500人，共58份观测结果。该数据的历史最高值出现于12-01-2017，达4,222,389.000人，而历史最低值则出现于12-01-1960，为292,622.000人。CEIC提供的KE：最大城市人口数据处于定期更新的状态，数据来源于World Bank，数据归类于Global Database的肯尼亚 – 表 KE.世界银行：人口和城市化进程统计。