Urban, Architectural, and Socioeconomic Factors Contributing to the Concentration of Potential Arbovirus Vectors and Arbovirosis in Urban Environments from a One Health Perspective: A Systematic Review

Abstract

Today, urbanisation and environmental changes are increasingly influencing the social and biological landscape of our planet. This systematic review aims to assess the relationship between urban–architectural and socioeconomic factors and vector concentrations in the urban environment. Following the Preferred Reporting Methodology for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the PubMed, Scopus, and Cochrane libraries were screened for studies conducted in urban contexts and those involving only arboviruses and potential exposure to arboviral vectors in urban environments, published from the beginning to 22 April 2025. Out of a total of 171 identified studies, 132 were selected for further analysis. Of these, 14 studies were eligible. The included studies reported different ways of measuring vector density and different considerations of the urban–architectural or socio-demographic factors related to it. The studies were set in different urban contexts: Asia, Central and South America, Africa and Oceania. Among the vector species, Aedes aegypti was the most analysed, often with Aedes albopictus. Socioeconomic status emerges as a determining factor. Low-income areas experience higher mosquito densities due to overcrowding, sub-optimal infrastructure, and environmental neglect. This review highlights the importance of implementing a standardised and effective global approach in urban health planning.

1. Introduction

Today, urbanisation and environmental changes are increasingly influencing the social and biological landscape of our planet. This systematic review aims to assess the relationship between urban–architectural and socioeconomic factors and the proliferation and spread of arboviruses. The factors considered include the type of construction, the presence of green spaces and their maintenance status, and population density.

Since the 19th century, global population growth has been accompanied by increasing urbanisation. Whereas most of the world’s population previously lived in rural areas, urban settlements have progressively expanded, with large cities becoming more common [1]. Today, however, while urbanisation is slowing down in the old, industrialised countries of Europe and North America, it is growing strongly in the south. Cities have grown significantly over the past 60 years, making urban areas the new dominant ecology on a global scale, where 70% of the population will live by the 2030s [1,2]. The two main factors changing the social and biological landscape of the planet today are urbanisation and environmental changes. On many levels, urban areas are hotspots for environmental change [2,3].

Rapid and unplanned urbanisation, along with a high population density, promotes the spread of Aedes mosquitoes. Densely populated areas—over 1000 inhabitants per km²—are consistently associated with higher rates of arboviral diseases [4]. High-rise buildings and urban density also influence the presence of mosquitoes, with higher numbers found in outdoor environments and lower floors of buildings [5].

The WHO predicts that urbanisation will lead to new vector-borne diseases and a further increase in others, particularly viral communicable diseases caused by Aedes mosquitoes [6]. Controlling Aedes species is a complex process, and it necessitates the risk of an epidemic to be reduced by implementing standard control measures and quality control activities, monitoring protocols, community-wide actions, and firefighting plans [7].

Implementing these interventions can be particularly challenging due to other growing social problems, such as population movements and rapid unplanned urbanisation [8]. Socioeconomic status significantly influences mosquito distribution, with lower-income neighbourhoods experiencing 63% higher mosquito densities and related diseases due to factors like abandoned properties, waste accumulation, and poor sanitation [9]. Moreover, the presence of garbage, trash, and plastic containers is significantly higher in low-income areas, contributing to mosquito breeding sites [10]. The presence of mosquitoes is influenced by environmental conditions such as vegetation, stagnant water, and decaying infrastructure.

Vegetation and water containers in urban areas provide breeding sites for mosquitoes, while decaying infrastructure in abandoned areas exacerbates the problem [11]. The presence of decorative vegetation and entry points for mosquitoes into households are associated with a higher Aedes density [12].

Temperature, humidity, and rainfall also influence mosquitooes’ distribution, with higher numbers found during the summer and autumn [5,13]. Microclimate variables, including temperature and humidity, strongly predict mosquito abundance.

Urban planning that considers these factors can help mitigate the presence of mosquitoes. For instance, models incorporating microclimate data and larval habitat density can predict adult mosquito abundance, aiding in targeted vector control [14].

Furthermore, the consumption of raw materials by humans, and their constant demand for more advanced goods, have caused an exponential and inevitable increase in industrial production, which has a significant impact on water systems, biodiversity, land use, and cover [15].

The extreme weather events that devastated every continent in 2021 and 2022 put additional strain on health services that were already struggling to cope with the impact of the COVID-19 pandemic’s after-effects. The spread of infectious diseases is being affected by climate change, increasing the risk of co-epidemics and emerging diseases for populations [16]. Zika, dengue, and chikungunya are among the arboviruses that are closely linked to climate change. In recent years, these diseases have led to an increase in local epidemics. Climate change affects the distribution and cycle of vector mosquitoes, such as Aedes aegypti and Aedes albopictus, which are responsible for the transmission of Zika, dengue, chikungunya, and yellow fever [17,18,19,20]. High temperatures and humidity promote the proliferation of these mosquitoes and increase arboviral infection rates [13,21]. Moreover, extreme weather events such as El Niño and La Niña have been associated with an increase in infection cases [21].

Italy, located in a temperate zone, has seen an increase in outbreaks of arboviruses. Estimates of the basic breeding number (R0) for these diseases vary by climate zone, with lower values in temperate zones than tropical and subtropical zones. However, with global warming, the R0 is also expected to increase in temperate regions, including Italy [20]. Italy has recorded cases of Zika infection, especially among travellers returning from endemic areas [22]. There have also been cases of dengue, with local epidemics, especially in the summer months, when the weather conditions favour the proliferation of vector mosquitoes [20,22].

In addition, Italy has recorded cases of chikungunya, with a significant outbreak in 2017 that affected several regions, where favourable climatic conditions, as well as the presence of Aedes Albopictus, have favoured the spread of the virus [20,22]. Understanding infectious disease dynamics in urban areas requires considering spatial heterogeneity in socioeconomic, demographic, and physical city characteristics. Studies conducted in the past ten years have demonstrated that variations in socioeconomic characteristics (such as income, poverty, and access to healthcare), demographic characteristics (such as population density), and the physical characteristics of cities (such as growth patterns and slum density) can have both independent and combined effects on the spread of disease [23,24,25].

The population dynamics of infectious diseases in urban environments require an integrated approach that takes into account rapid advances in surveillance and the design of cities to control factors favouring the proliferation of infectious vectors, without focusing on ecological differences among vectors, which are secondary to the relationship between socioeconomic, urban, and behavioural factors. In order to undertake preventive medicine and urban hygiene procedures to reduce the spread of arbovirosis, we questioned how the complex urban environment affects the transmission of disease and what factors can be addressed. This study therefore focuses on identifying factors associated with the prevalence of vectors in urban environments, with the aim of providing potential tools for urban planning and the prioritisation of interventions to control arboviral diseases.

2. Materials and Methods

2.1. Selection Protocol and Search Strategy

The Preferred Reporting Methodology for Systematic Reviews and Meta-Analyses (PRISMA) was followed in the conduct of the current systematic review. The associated protocol, CRD42024613799, has been registered in PROSPERO [26].

Three distinct databases (PubMed, Scopus, and Web of Science) were used in this research. The search string (“vector density” OR “mosquito* density” OR “mosquito* development”) AND (“urban determinant*” OR “social determinant*” OR urbanization OR “urban development”) was then used to find all articles published up until the 22 April 2025.

2.2. Inclusion and Exclusion Criteria for the Study

Furthermore, all of the found articles were then filtered, first by abstract and title and then by full text. Following that, each author (L.C., M.S., L.P., M.A.D., C.D.P., S.D.G.) selected the articles independently. All the publications were reviewed by the same authors (L.C., M.S., L.P., M.A.D., C.D.P., S.D.G.) separately. Following a thorough debate, all the authors came to unanimous agreement if any concerns, doubts, or contradictions were discovered.

All studies that focused on controlling the concentration of arboviral vectors in the urban environment and that presented specific data with a focus on socioeconomic, urban planning, and behavioural factors and their influence were considered eligible. All research that merely measured vector density without linking it to environmental or urban factors was excluded. This study did not take into account reviews, case reports, meta-analyses, symposia, editorials, or any other type of literature. Only articles written in Italian or English were included.

2.3. Data Extraction and Quality Assessment

All of the included papers were then analysed in order to extract information. This includes information regarding author, year, country, socioeconomic and demographic characteristics, urban and architectural factors, natural factors, vector density, and study methodology.

The data were then arranged according to several evaluation and intervention methodologies. The Newcastle–Ottawa Quality evaluation Scale (NOS) was used to conduct a quality evaluation. A collection of questions is used by the NOS for observational studies to assess their research quality, and each study can receive up to nine points in three different categories. The study group selection, sample size, responder profile, and clarity of the various risk factors were all taken into account in the first area, “SELECT” (4 points). The comparability of various result groups and the presence or absence of confounding variables were included in the second category, “COMPARATIVITY” (2 points). The last area, “RESULTS” (3 points), looked at whether exposure and outcome verification was evaluated clearly or if the statistical testing was suitable when it was applied. Following the summation of the scores, the quality was classified as “good” if the final score was higher than 7, “fair” if it was between 5 and 7, or “poor” if it was lower than 5 [27].

3. Results

Out of a total of 171 identified studies on databases, after having removed duplicates, 132 articles were evaluated for their title and abstract, and then 43 were selected for further analysis, and 14 were finally analysed after applying all the inclusion and exclusion criteria, as depicted in Figure 1.

The main explanatory variables used to associate different urban contexts with epidemiological/entomological results were as follows: socioeconomic and demographic factors, including social health determinants (education level, income, employment status, population density, overcrowding); urban factors (building fabric, compact/compact city, dispersed/suburban, formal or informal urbanisation); naturalistic factors (presence and care of green and blue areas); vector density; and the type of measurement of vectors (direct, estimated, indirect).

Fourteen studies were included, reporting one or more of the following elements when comparing different areas: socio-demographic, urban–architectural, and natural characteristics. The selected studies focused on urban contexts in Asia (n = 7), with a clear prevalence in Indian (n = 3) and Chinese (n = 2) contexts, Central and South America (n = 4) (Colombia, n = 2, Brazil, n = 2), Africa (n = 2), and Oceania (n = 1), as reported in Table 1. This prevalence highlights a stronger interest among endemic countries in vector-borne diseases such as dengue, Zika, and malaria. Most urban contexts analysed featured significant socioeconomic disparities, reflected in pronounced differences in urban quality. These cities often include well-serviced neighbourhoods with high-quality infrastructure and informal settlements or slums, characterised by extensive self-construction and lack of regulation.

Due to the high heterogeneity in the content of the included studies, the evaluation was entirely qualitative. This choice is justified by the highly diverse objectives and methods across the studies. Not all the studies provided detailed methodologies for assessing vector density, with some limiting their analysis to counting vectors captured in containers or using specific traps, including direct aspiration when vectors were identified in specific locations. Twelve studies [28,29,30,31,32,33,34,35,36,37,38,39] measured vector density or abundance using direct methods involving vector capture. One study [40] estimated vector density based on contextual conditions, and another [41] inferred density from dengue incidence cases (see Table 1).

Studies employing direct measurements utilised a variety of methods, including the Container Index (CI) [28,33,38], Breteau Index (BI) [29,33,38,39], Human Landing Index (HLI) [28], Standard Space Index (SSI) [29], Adult Density Index (ADI) [29], pupae per person index (PPI) [33,39], Egg Density Index (EDI) [30], and House Index (HI) [33,38]. These methods reflect a significant variability in the calculation of vector density, aiming to produce specific spatial indices for area comparisons. The choice of methodology often depended on the study’s objectives, such as density within drinking water containers (CI), inside homes (HI), generalised density based on sampling and trapping (BI, HLI), or vector density in specific life stages (PPI, EDI). This variability highlights the complexity of studying vector density.

Regarding vector species, Aedes aegypti was the most analysed, appearing in 9 out of 14 studies [31,33,35,36,37,38,39,40,41], often alongside Aedes albopictus [31,36]. The importance of these vectors lies in their high transmission capacity for arboviruses and their notable affinity for urbanised contexts, as underlined in all the studies.

As for the analysed factors, considerable heterogeneity emerged. Most studies were ecological, associating these factors with specific areas or inferring them from contextual descriptions related to vector density analyses. This evidence necessitates further evaluation and specific studies.

Table 1 summarises the number of studies providing the information targeted by this review.

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Socio-demographic factors: 11 studies;

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Urban–architectural factors: 13 studies;

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Natural factors: 12 studies (including temperature and humidity).

Table 1. Included studies and data extraction.

Authors Year Country	Socioeconomic and Demographic Factors	Urban and Architectural Factors	Natural Factors	Carrier Types/Species	Type of Measurement (Direct, Estimated, Indirect)	Quality
Liu et al. 2013 China [28]	Low socioeconomic level and high population density associated with higher carrier density	Leakage from pipes and wetlands associated with higher presence; tree-lined courtyards, moderate presence; absence of greenery but with drainage systems very low presence; affluent residential area but with private courtyards and proximity to large park, high vector density	Vegetation and lake with fish associated with lower occurrence; trees and irrigated areas associated with higher occurrence	Culex pipiens complex	Direct measurement (bed net trap; light trap; labour hour method)	Good
Wu et al. 2020 China [29]	Low socioeconomic status and overcrowding, high carrier density	Heavily urbanised areas, lower density; sparsely urbanised areas with haphazard urbanisation, overcrowding, and density with low socioeconomic level, very high density	Proximity to waterways but with low socioeconomic level, very high vector density; vegetated areas, intermediate vector density; unused areas (unfinished construction sites, wasteland, or vacant lots), urban—assimilated to urban voids—and suburban lowest density of all	Aedes albopictus	Direct measurement: Breteau Index (BI); Standard Space Index (SSI); Adult Density Index (ADI)	Good
Romeo-Aznar et al. 2018 India [41]	Low socioeconomic level and high population density with overcrowding, high carrier density	High building density in deprived areas (and thus poor quality), sheet metal, use of plastic roofing, lack of planning and drainage systems, functional mix (although it is correlated with poor control, open water containers), heat islands, and shaded areas (narrow alleys or lots of vegetation), high vector density	Not described	Aedes aegypti	Indirect estimation (from dengue incidence)	Good
Santos J.P.C. et al. 2020 Brazil [30]	Net demographic density	Percentage of occupied area; percentage of residential area; percentage of area of substandard clusters; percentage of area with strategic points; mean verticalisation; number of neighbouring districts; border perimeter with neighbouring districts; percentage of households with unpaved streets; percentage of households with no tree-lined streets; percentage of households with streets without manholes; percentage of households with streets with exposed trash; percentage of households with streets with open sewage	Percentage of vegetation; mean daytime surface temperature; mean night-time surface temperature; monthly cumulative rainfall	Incidence of dengue cases and Aedes Egg Density Index (EDI)	Mean number of incident cases in the study period to the mean population over the same period multiplied by 100,000 inhabitants. The Aedes Egg Density Index (EDI) is calculated for each neighbourhood from 2013 to 2014 through entomological monitoring by ovitraps.	Good
French M.A. et al. 2021 Australia [31]	Head of household; number of members, number living in household; name, date of birth, sex, marital status, relationship to head of household, education level, literacy; symptoms, healthcare utilisation, mental health; height and weight for children; haemoglobin; soil-transmitted helminths; individual primary activities and time use, household assets, self-assessed socioeconomic status and life satisfaction	Floor, wall, and roof materials; number of rooms; land ownership/tenure, occupation tenure; water sources, access, treatment methods, and cost; sanitation access, type, ownership, and disposal; garbage disposal; local flooding events; child environmental exposure	E. coli, nitrogen, pH, temperature, turbidity, conductivity, dissolved oxygen in water; E. Coli in soil; local agricultural (foodstock), domestic, or feral animals in animal faeces; temperature and humidity in thermal environment; mosquito species and relative abundance; small mammalian pest species and relative abundance	Culex quinquefasciatus (94⋅7%) and Aedes aegypti (4.9%). Small numbers of other species were caught, including Anopheles species, Aedes albopictus, Culex sitiens, a Uranotaenia species, and a Mansonia species	Counting and identification of mosquitoes caught in traps	Good
Kigozi S.P. et al. 2015 Uganda [32]	Number of participants; mean age in years during follow-up; total number of routine blood slides; parasite prevalence; person-years of observation; total episodes of malaria; incidence of malaria per person-year	Household density, and night-time light brightness	Land cover, vegetation amount	Anopheles mosquitoes	Household density of mosquitoes (number of female Anopheles mosquitoes captured)	Good
Wai et al. 2012 India, Myanmar, Thailand, Indonesia, Philippines, Sri Lanka [33]	Subjects aged >25 years. Middle class neighbourhoods and good to reasonable access to public services	Rainwater reservoirs, unplanned construction, low buildings in deprived areas, accumulation of materials in streets, unmaintained public or private spaces, all associated with higher vector density	Annual rainfall data	Aedes aegypti	Direct measurement: Breteau Index (BI); House Index (HI), Container Index (CI), pupae per person index (PPI)	Good
Kang et al. 2018 Uganda [34]	Not described	Concrete walls, brick floors, screened gutters and vents, tiled roofs, protective factors; incomplete houses, exposed brick, architectural elements that promote stagnation and vector density	Temperature, humidity, rainfall	Anopheles spp.	Direct measurement (trap)	Good
Ferdousi et al. 2015 Bangladesh [40]	Not described	Brick or concrete houses, brick houses and bamboo roofs, shacks, 111 different types of indoor, outdoor, or rooftop water containers	Not described	Aedes aegypti	Estimated	Good
Xavier et al., 2023 Brazil [35]	Not described	Mostly single-family houses with basic sanitation and mutual geographic isolation: about 2.5 km apart and separated by stretches of unbuilt environment	Possible temporal variations due to monthly weather conditions (precipitation or temperature) or insecticide spraying are considered	Aedes aegypti e Culex quinquefasciatus	The density of adult mosquitoes measured directly by active aspiration of adult mosquitoes and indices based on the presence of eggs of Aedes females in traps and the abundance of eggs in traps	Fair
Micanaldo E.F. et al. 2021 Philippines [36]	Low to middle, high population density, inadequate sanitary conditions	Entomological, epidemiological, and landscape data from the rainy season; lack of drainage infrastructure, which contributes to flooding deteriorating infrastructure, agriculture, water bodies, commercial, residential	Landscape and climate variables, strong monsoon rain and tropical storms. Heavy rain	Aedes aegypti and Aedes albopictus	Direct and indirect	Good
Gómez-Vargas W. et al. 2024 Colombia [37]	High population density, low to middle	Unregulated urbanisation, regulated urbanisation deteriorating infrastructure, agriculture, water bodies, commercial, residential, drainage system, 3 different municipalities	Landscape, monthly records of precipitation (mm), relative humidity (%), and temperature (˚C)	Aedes aegypti	Direct and indirect. Mean density of Ae. aegypti was 1.47 females/dwelling and 0.51 females/inhabitant, Prokopack aspirators	Good
Telle O. et al. 2021 India [38]	Low socioeconomic level and high population density associated with higher carrier density	Taller buildings (in affluent neighbourhoods) are associated with lower larval presence, building quality is associated with lower larval presence (less accumulation on sheet metal and uncovered tanks), higher maintenance, access to potable water	Absence of manicured vegetation in deprived areas creates favourable microclimate for mosquitoes	Aedes aegypti	House Index, Container Index, Breteau Index (BI)	Good
Fuentes-Vallejo M et al. 2015 Colombia [39]	High housing density in deprived areas increased vector density and lower land care and poor building quality	Favourable elements are unplanned urbanisation (coincides with proximity to waterways), low and close buildings (favourable microclimate), irregularly shaped yards (water stagnation), unmaintained common areas, lack of drainage	Proximity to waterways is associated with poor urban quality and poor drainage but also with alluvial features that promote water accumulation	Aedes aegypti	Pupae per person index (PPI), Breteau Index (BI)	Good

Table 1. Included studies and data extraction.

Authors Year Country	Socioeconomic and Demographic Factors	Urban and Architectural Factors	Natural Factors	Carrier Types/Species	Type of Measurement (Direct, Estimated, Indirect)	Quality
Liu et al. 2013 China [28]	Low socioeconomic level and high population density associated with higher carrier density	Leakage from pipes and wetlands associated with higher presence; tree-lined courtyards, moderate presence; absence of greenery but with drainage systems very low presence; affluent residential area but with private courtyards and proximity to large park, high vector density	Vegetation and lake with fish associated with lower occurrence; trees and irrigated areas associated with higher occurrence	Culex pipiens complex	Direct measurement (bed net trap; light trap; labour hour method)	Good
Wu et al. 2020 China [29]	Low socioeconomic status and overcrowding, high carrier density	Heavily urbanised areas, lower density; sparsely urbanised areas with haphazard urbanisation, overcrowding, and density with low socioeconomic level, very high density	Proximity to waterways but with low socioeconomic level, very high vector density; vegetated areas, intermediate vector density; unused areas (unfinished construction sites, wasteland, or vacant lots), urban—assimilated to urban voids—and suburban lowest density of all	Aedes albopictus	Direct measurement: Breteau Index (BI); Standard Space Index (SSI); Adult Density Index (ADI)	Good
Romeo-Aznar et al. 2018 India [41]	Low socioeconomic level and high population density with overcrowding, high carrier density	High building density in deprived areas (and thus poor quality), sheet metal, use of plastic roofing, lack of planning and drainage systems, functional mix (although it is correlated with poor control, open water containers), heat islands, and shaded areas (narrow alleys or lots of vegetation), high vector density	Not described	Aedes aegypti	Indirect estimation (from dengue incidence)	Good
Santos J.P.C. et al. 2020 Brazil [30]	Net demographic density	Percentage of occupied area; percentage of residential area; percentage of area of substandard clusters; percentage of area with strategic points; mean verticalisation; number of neighbouring districts; border perimeter with neighbouring districts; percentage of households with unpaved streets; percentage of households with no tree-lined streets; percentage of households with streets without manholes; percentage of households with streets with exposed trash; percentage of households with streets with open sewage	Percentage of vegetation; mean daytime surface temperature; mean night-time surface temperature; monthly cumulative rainfall	Incidence of dengue cases and Aedes Egg Density Index (EDI)	Mean number of incident cases in the study period to the mean population over the same period multiplied by 100,000 inhabitants. The Aedes Egg Density Index (EDI) is calculated for each neighbourhood from 2013 to 2014 through entomological monitoring by ovitraps.	Good
French M.A. et al. 2021 Australia [31]	Head of household; number of members, number living in household; name, date of birth, sex, marital status, relationship to head of household, education level, literacy; symptoms, healthcare utilisation, mental health; height and weight for children; haemoglobin; soil-transmitted helminths; individual primary activities and time use, household assets, self-assessed socioeconomic status and life satisfaction	Floor, wall, and roof materials; number of rooms; land ownership/tenure, occupation tenure; water sources, access, treatment methods, and cost; sanitation access, type, ownership, and disposal; garbage disposal; local flooding events; child environmental exposure	E. coli, nitrogen, pH, temperature, turbidity, conductivity, dissolved oxygen in water; E. Coli in soil; local agricultural (foodstock), domestic, or feral animals in animal faeces; temperature and humidity in thermal environment; mosquito species and relative abundance; small mammalian pest species and relative abundance	Culex quinquefasciatus (94⋅7%) and Aedes aegypti (4.9%). Small numbers of other species were caught, including Anopheles species, Aedes albopictus, Culex sitiens, a Uranotaenia species, and a Mansonia species	Counting and identification of mosquitoes caught in traps	Good
Kigozi S.P. et al. 2015 Uganda [32]	Number of participants; mean age in years during follow-up; total number of routine blood slides; parasite prevalence; person-years of observation; total episodes of malaria; incidence of malaria per person-year	Household density, and night-time light brightness	Land cover, vegetation amount	Anopheles mosquitoes	Household density of mosquitoes (number of female Anopheles mosquitoes captured)	Good
Wai et al. 2012 India, Myanmar, Thailand, Indonesia, Philippines, Sri Lanka [33]	Subjects aged >25 years. Middle class neighbourhoods and good to reasonable access to public services	Rainwater reservoirs, unplanned construction, low buildings in deprived areas, accumulation of materials in streets, unmaintained public or private spaces, all associated with higher vector density	Annual rainfall data	Aedes aegypti	Direct measurement: Breteau Index (BI); House Index (HI), Container Index (CI), pupae per person index (PPI)	Good
Kang et al. 2018 Uganda [34]	Not described	Concrete walls, brick floors, screened gutters and vents, tiled roofs, protective factors; incomplete houses, exposed brick, architectural elements that promote stagnation and vector density	Temperature, humidity, rainfall	Anopheles spp.	Direct measurement (trap)	Good
Ferdousi et al. 2015 Bangladesh [40]	Not described	Brick or concrete houses, brick houses and bamboo roofs, shacks, 111 different types of indoor, outdoor, or rooftop water containers	Not described	Aedes aegypti	Estimated	Good
Xavier et al., 2023 Brazil [35]	Not described	Mostly single-family houses with basic sanitation and mutual geographic isolation: about 2.5 km apart and separated by stretches of unbuilt environment	Possible temporal variations due to monthly weather conditions (precipitation or temperature) or insecticide spraying are considered	Aedes aegypti e Culex quinquefasciatus	The density of adult mosquitoes measured directly by active aspiration of adult mosquitoes and indices based on the presence of eggs of Aedes females in traps and the abundance of eggs in traps	Fair
Micanaldo E.F. et al. 2021 Philippines [36]	Low to middle, high population density, inadequate sanitary conditions	Entomological, epidemiological, and landscape data from the rainy season; lack of drainage infrastructure, which contributes to flooding deteriorating infrastructure, agriculture, water bodies, commercial, residential	Landscape and climate variables, strong monsoon rain and tropical storms. Heavy rain	Aedes aegypti and Aedes albopictus	Direct and indirect	Good
Gómez-Vargas W. et al. 2024 Colombia [37]	High population density, low to middle	Unregulated urbanisation, regulated urbanisation deteriorating infrastructure, agriculture, water bodies, commercial, residential, drainage system, 3 different municipalities	Landscape, monthly records of precipitation (mm), relative humidity (%), and temperature (˚C)	Aedes aegypti	Direct and indirect. Mean density of Ae. aegypti was 1.47 females/dwelling and 0.51 females/inhabitant, Prokopack aspirators	Good
Telle O. et al. 2021 India [38]	Low socioeconomic level and high population density associated with higher carrier density	Taller buildings (in affluent neighbourhoods) are associated with lower larval presence, building quality is associated with lower larval presence (less accumulation on sheet metal and uncovered tanks), higher maintenance, access to potable water	Absence of manicured vegetation in deprived areas creates favourable microclimate for mosquitoes	Aedes aegypti	House Index, Container Index, Breteau Index (BI)	Good
Fuentes-Vallejo M et al. 2015 Colombia [39]	High housing density in deprived areas increased vector density and lower land care and poor building quality	Favourable elements are unplanned urbanisation (coincides with proximity to waterways), low and close buildings (favourable microclimate), irregularly shaped yards (water stagnation), unmaintained common areas, lack of drainage	Proximity to waterways is associated with poor urban quality and poor drainage but also with alluvial features that promote water accumulation	Aedes aegypti	Pupae per person index (PPI), Breteau Index (BI)	Good

The socio-demographic factors associated with a higher vector density primarily included overcrowded housing and a low socioeconomic status, recognised in nearly all analysed studies as key elements impacting vector density through various causal pathways. A notable exception was observed in one study [28], which a linked higher vector presence to high socioeconomic status in a specific urban context, particularly due to private green areas.

The urban–architectural factors associated with a higher vector density primarily concerned informal and unplanned urbanisation [37,39], a reduced drainage capacity for surface and rainwater [34,36,39], the use of substandard materials in degraded urban contexts [41], reduced building height, urban heat islands, and waste accumulation on streets or specific locations, such as tyres, which due to their shape facilitate widespread water stagnation in poorly managed contexts. Specific architectural factors included the use of materials like corrugated metal roofs, favouring rainwater accumulation in contexts lacking effective drainage systems, and exposed porous materials, such as uncovered hollow bricks, which can trap stagnant water, creating breeding grounds for vectors. In Indian contexts, uncovered water tanks on rooftops, especially in highly informal urban areas with limited piped water access, played a prominent role.

Beyond high temperatures and humidity, the natural factors associated with higher vector density included the poor management of riverside areas and neglected green spaces. Specific factors included water pH and bacterial presence, although these are likely correlational rather than causal [31]. Bacterial proliferation often characterises stagnant water accumulations, even in uncovered or poorly managed drinking water reservoirs, sometimes associated with other invasive animal species.

All studies were rated as “good” [28,29,30,31,32,33,34,36,37,38,39,40,41], with the exception of one which was “fair” [35].

4. Discussion

The literature review highlights several points of interest regarding the available evidence, potential research directions, and general assessments of the existing literature. All the results, following the adopted evaluation model, are presented in Table 1.

The identification of socio-demographic, architectural, and environmental factors influencing vector density offers valuable guidance for targeted and context-specific vector control strategies. Overcrowding and a low socioeconomic status consistently correlate with an increased vector presence, underscoring the need to integrate social equity into public health interventions. Architectural features—such as poor drainage, informal construction, and materials that promote water stagnation—highlight the critical intersection between urban design and vector ecology. Additionally, context-dependent findings, such as a higher vector density in affluent areas with private green spaces, emphasise the complexity of urban ecosystems. These insights support the development of multidisciplinary approaches combining urban planning, environmental management, and public health to effectively mitigate arbovirus transmission in diverse urban settings [28,29,30,31,32,33,34,35,36,37,38,39,40,41].

The first significant observation is the absence of studies specific to high-income countries. This represents a limitation in the generalisability of the results but also highlights a lack of interest in urban contexts in regions such as Europe and North America. Nevertheless, as presented in the introduction, early outbreaks of vector-borne diseases have already been observed in these regions, albeit limited in time and space, primarily due to habitat modifications driven by climate change and increased transcontinental connectivity. A prominent biomedical report addressing climate change impacts [16] emphasises that such diseases require greater global attention. Unfortunately, it is not possible to compare measurements obtained using different methodologies, which represents a significant limitation. All the methods used and analysed are validated and reflect a necessary differentiation in measurement capacity, as this is strongly influenced by contextual characteristics, as well as by the type of vector and the location where it is being investigated [42].

Moreover, there is little focus in the literature on systematically studying contextual characteristics as specific exposure variables, combining robust entomological assessments of anthropogenic habitats with contextual characteristics.

Such studies could provide concrete evidence to guide public policies in urban and architectural planning. The reviewed studies suggest potential research directions, while also highlighting a lack of awareness about the necessity to integrate biomedical knowledge with expertise from other fields, particularly urban planning, and combine qualitative and quantitative methodologies. This interdisciplinary approach is further explored at the conclusion of the discussion.

Socioeconomic factors emerge as critical determinants of vector density, although causality is clearly mediated by urban and architectural factors. While this conclusion is influenced by the selection of study contexts—where low-income urban areas often feature extensive formal neighbourhoods alongside informal settlements or slums [43]—it is still possible to identify recurring general aspects. Socioeconomic factors are literally “the causes of the causes” [44], as lower socioeconomic populations are more likely to live in degraded urban contexts, often without drainage systems for rainwater or proper sewage management [45]. In this sense, we highlight that patterns of inequality in the distribution of vectors within the same city—depending on the socioeconomic status of neighbourhoods—are also observed in diverse urban contexts.

In these neighbourhoods, buildings are often constructed in a disorderly fashion, lacking planned spaces for green infrastructure, thus favouring water collection in urban interstices, exacerbating heat islands, and creating ideal conditions for vector proliferation [46]. These contexts are further characterised by substandard infrastructure, necessitating the use of uncovered water tanks for potable water storage. Building materials, often recycled debris such as hollow bricks and corrugated sheets improperly installed, facilitate water accumulation in crevices, creating breeding grounds for vectors. Waste accumulation on streets and the lack of proper waste disposal also contribute to additional vector habitats. In this sense, cisterns and used car tires are both well-known and notorious due to the volume of water they can hold, as well as the protection they offer from evaporation and UV exposure, promoting vector proliferation [47].

Architectural, urban, and economic variables have a major impact on the transmission of arboviral infections [48]. Economic disparities between countries and regions influence socioeconomic conditions, which impact healthcare access, infrastructure, and mosquito control strategies [49]. Additionally, because highly crowded places, inadequate waste management, and a lack of natural space can all act as vector breeding grounds, urban design is crucial. Indoor ventilation, water storage, and mosquito exposure are all impacted by architectural design, which is frequently tailored to the temperature and terrain [50]. Additionally, by emphasising regional vulnerabilities and intervention goals, the inclusion of a geographical classification and an economic country–city framework in disease transmission analysis improves comprehension [51].

It is evident that urban–architectural and socio-demographic factors (including overcrowding, often confused with population density but representing its opposite in terms of social stratification and the spread of infectious diseases [52]) are intrinsically linked. None of the reviewed studies delves deeply into this causal relationship, suggesting potential further research avenues.

In this regard, it is essential to leverage additional knowledge and practices already employed in other fields of epidemiology and urban studies, through the targeted use of GISs (Geographical Information Systems) integrated with reliable data sources, both social and health-related. GIS-driven studies are, therefore, highly desirable and relatively feasible, particularly in high-income settings. These could especially benefit from population-specific record-linkage methodologies, enabling a thorough investigation of the phenomenon while accounting for population mobility within urban contexts.

The integration of geospatial modelling and geoinformation systems (GISs) offers a transformative potential in the surveillance and control of vector-borne diseases in urban settings. By enabling the visualisation and analysis of spatial patterns of vector density in relation to socioeconomic, architectural, and environmental factors, GIS-driven approaches can support evidence-based decision-making. Advanced techniques such as spatial interpolation, hotspot analysis, and predictive modelling can identify high-risk areas, optimising resource allocation for targeted interventions. Furthermore, coupling GISs with machine learning algorithms and real-time environmental monitoring (e.g., weather and humidity sensors) could enhance the predictive accuracy of vector proliferation models. This geospatially informed approach aligns with the global shift towards data-driven urban health strategies, providing a critical toolset for addressing the complexities of vector-borne diseases in rapidly urbanising regions [53,54].

From the perspective of natural factors, it becomes clear that vector density is not merely linked to the presence of green or blue spaces but rather to their management. Parks, private green areas, and riverbanks favour vector density when neglected. Moreover, the loss of biodiversity appears to disrupt natural balances that regulate vector proliferation, especially in wetlands where predator species may control vector populations [55,56].

5. Limitations and Conclusions

This study suggests that more multidisciplinary research is required to assess environmental management and sustainable urban design strategies for the prevention of arbovirosis. In order to achieve the global Sustainable Development Goals (SDGs), especially Goal 3 (Good Health and Well-Being), Goal 11 (Sustainable Cities and Communities), and Goal 13 (Climate Action), it is highly recommended that integrated and sustainable urban policies be developed that concurrently address the urban, socioeconomic, and behavioural determinants of vector proliferation [57]. Using important international strategic documents like the WHO’s Global Vector Control Response (2017–2030), the Lancet Countdown on Health and Climate Change, and reports from the EU’s Climate Change and Health (CCM) program, longitudinal and interventional studies would be helpful in evaluating the effect of sustainable urban interventions on the spread of vector-borne diseases [58,59,60].

In light of climate change, the European Climate Risk Assessment (EUCRA) also emphasises the growing threats to public health posed by vector-borne illnesses. The expansion of vectors like Aedes mosquitoes is being facilitated by rising temperatures and changing precipitation patterns, which could raise the danger of arbovirus transmission in previously non-endemic locations [61].

This study has certain limitations. The primary challenge lies in the significant heterogeneity of the studies analysed, which complicated the data extraction and discussion. This heterogeneity is reflected in the diverse methodologies, both qualitative and quantitative, employed by the reviewed studies. As a result, it was not feasible to perform a robust quantitative synthesis, necessitating a predominantly qualitative evaluation, which may introduce subjective interpretation biases. Additionally, the lack of standardised methodologies for vector density assessment represents another limitation. Although the indices used are valid within their specific contexts, the absence of a uniform approach restricts direct comparisons across results. Owing to the heterogeneity of the findings across the included studies, conducting a quantitative meta-analysis or performing a statistical synthesis was not feasible. Certainly, there may also be issues of article selection related to publication bias, as grey literature was not taken into account.

Most of the studies adopted a cross-sectional design, providing a static snapshot of associations between the analysed factors and vector density. Longitudinal studies are needed to better understand the temporal and causal dynamics, especially in rapidly changing contexts. Another limitation is the lack of generalisability of the findings to high-income urban contexts, as most studies focus on urban settings with a high informality, which are characteristic of specific global areas.

This review clearly shows that vector density results from complex interactions between socioeconomic, urban, and environmental factors. However, the findings strongly emphasise that deficiencies in urban planning, particularly in informal settlements, amplify public health risks. These results underscore the importance of urban regeneration policies that integrate solutions to improve water drainage, promote high-quality building materials, and reduce vector habitats through sustainable waste and water infrastructure management. Urban planning is not merely a technical tool but a necessity for public health [62].

Another significant limitation was that it was not possible to evaluate the standardisation of Global Urban Health in the countries reported in the literature, despite the differences in the health budgets of the various countries, due to the absence of information on this.

The evidence collected calls for a future research agenda that is interdisciplinary, global, and action-oriented. Prospective studies are needed to assess the impact of integrated and context-specific interventions. Additionally, governments must adopt a proactive approach by investing in resilient infrastructure and developing urban planning strategies that address the health risks associated with climate change. Only by doing so will it be possible to systematically and sustainably tackle the challenges posed by vector-borne diseases in urban settings.

The implementation of comprehensive, evidence-based policies by policymakers is crucial in reducing the development of arboviral infections in urban environments. Addressing the socioeconomic disparities that increase exposure to vector habitats should be a top priority, especially in underprivileged areas with limited housing and poor infrastructure. To avoid water build-up and eradicate breeding grounds, urban development regulations must guarantee the installation of efficient drainage systems, controlled building techniques, and the preservation of green areas. By making investments in geospatial technology, such as spatial analysis, GIS mapping, and predictive modelling, surveillance can be greatly improved and focused, resource-efficient interventions made possible. Furthermore, sustaining urban natural spaces and biodiversity are crucial elements of long-term vector control. In high-income nations, where global mobility and climate change are promoting the formation of new danger zones, policymakers must also broaden their research and monitoring frameworks. Building resilient urban health systems that can address the changing problems of vector-borne diseases requires a multisectoral, forward-looking approach.