Spatial Analysis of Mosquito-Borne Diseases in Europe: A Scoping Review

: Mosquito-borne infections are increasing in endemic areas and previously unaffected regions. In 2020, the notiﬁcation rate for Dengue was 0.5 cases per 100,000 population, and for Chikungunya <0.1/100,000. In 2019, the rate for Malaria was 1.3/100,000, and for West Nile Virus, 0.1/100,000. Spatial analysis is increasingly used in surveillance and epidemiological investigation, but reviews about their use in this research topic are scarce. We identify and describe the methodological approaches used to investigate the distribution and ecological determinants of mosquito-borne infections in Europe. Relevant literature was extracted from PubMed, Scopus, and Web of Science from inception until October 2021 and analysed according to PRISMA-ScR protocol. We identiﬁed 110 studies. Most used geographical correlation analysis ( n = 50), mainly applying generalised linear models, and the remaining used spatial cluster detection ( n = 30) and disease mapping ( n = 30), mainly conducted using frequentist approaches. The most studied infections were Dengue ( n = 32), Malaria ( n = 26), Chikungunya ( n = 26), and West Nile Virus ( n = 24), and the most studied ecological determinants were temperature ( n = 39), precipitation ( n = 24), water bodies ( n = 14), and vegetation ( n = 11). Results from this review may support public health programs for mosquito-borne disease prevention and may help guide future research, as we recommended various good practices for spatial epidemiological studies.


Introduction
Due to climate change, deforestation, environmental degradation, urbanisation, human mobility, globalisation, and changes in public health practices, the incidence of vectorborne infectious diseases has been increasing [1]. This upsurge is not only due to these factors but also to genetic alterations found in infectious agents and to greater resistance acquired by the vectors to insecticides [2]. In addition, some of these factors explain the emergence of vectors and vector-borne diseases in new regions, namely in areas of the northern hemisphere, and a growing incidence in endemic areas [1].
Vectors transmit parasites, viruses, and bacteria that cause human diseases which are vector-borne diseases. The pathogens of humans or animals to humans can be transmitted by mosquito vectors [2]. Mosquitos ingest disease-producing microorganisms from an infected host (human or animal) during a blood meal as they are blood-sucking insects

Materials and Methods
The scoping review followed the methodology proposed by Arksey and O'Malley [21], which is organized into five steps: (1) identifying the research question and (2) the relevant studies; (3) selecting the studies according to inclusion criteria; (4) charting and interpreting data; (5) summarising and reporting of results. Results will be reported according to PRISMA-ScR (PRISMA extension for Scoping Reviews) [22]. The study protocol can be found at: https://zenodo.org/record/6758947#.YrmaIXbMKUk, accessed on 30 September 2021. The PRISMA-ScR can be found in Supplementary Table S4-Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist.

Identifying the Research Question
This review is centred around the following main research question: which spatial analysis methods are used to investigate the spatiotemporal variation of mosquito-borne diseases in Europe and the biotic and abiotic factors that may relate to its presence? .
A reference management software (EndNote 20, Clarivate Analytics (Philadelphia, PA, USA)) was used to import and organise the references and remove duplicates [23].

Selecting the Studies According to Inclusion Criteria
We selected studies that focused on mosquito-borne diseases and used spatial analysis methods. Studies were excluded hierarchically based on the following exclusion criteria: (1) study type (reviews, reports, abstracts, editorials, comments); (2) not written in Portuguese, Spanish, Italian, French, German, or English; (3) not about mosquito-borne diseases/infections or their vectors; (4) no spatial analysis was conducted. No temporal restrictions were imposed.
Two examiners (SM and AIR) analysed titles and abstracts to detect studies that did not meet the inclusion criteria or that did not have full texts. Then, full texts were read and those that did not meet the inclusion criteria were removed. When the two reviewers disagree, the final decision was made by a third examiner (JR). Forward and backward citation tracking of articles included in the review was performed to identify additional papers.
The study selection process is represented in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flowchart [24] from Figure 1. Out of 1755 eligible studies, after reading the abstracts and keywords, 156 were selected in the second step and finally, 110 studies were selected in the last stage after a thorough reading and analysis of the full paper.
the individual database from the following conceptual structure and is provided in supplementary material (Table S1).
A reference management software (EndNote 20, Clarivate Analytics (Philadelp PA, USA)) was used to import and organise the references and remove duplicates [23

Selecting the Studies According to Inclusion Criteria
We selected studies that focused on mosquito-borne diseases and used spatial a ysis methods. Studies were excluded hierarchically based on the following exclusion teria: (1) study type (reviews, reports, abstracts, editorials, comments); (2) not writte Portuguese, Spanish, Italian, French, German, or English; (3) not about mosquito-b diseases/infections or their vectors; (4) no spatial analysis was conducted. No temp restrictions were imposed.
Two examiners (SM and AIR) analysed titles and abstracts to detect studies that not meet the inclusion criteria or that did not have full texts. Then, full texts were and those that did not meet the inclusion criteria were removed. When the two review disagree, the final decision was made by a third examiner (JR). Forward and backw citation tracking of articles included in the review was performed to identify additi papers.
The study selection process is represented in the PRISMA (Preferred Reporting It for Systematic Reviews and Meta-Analyses) flowchart [24] from Figure 1. Out of 1755 gible studies, after reading the abstracts and keywords, 156 were selected in the sec step and finally, 110 studies were selected in the last stage after a thorough reading analysis of the full paper.

Charting and Interpreting Data
The results were structured by general characteristics (e.g., country/region, year of publication); by themes according to the diseases and/or mosquitoes studied; by the scale of analysis (size of the spatial units) and geographic extent (global, continental, regional or local); type of outcome data used (notification or survey); the methodology used for spatial analysis; studied ecological determinants.
Regarding the type of outcome data used, notifications correspond to data on diseases/infections that, by statutory requirements, must be reported to the public health authority whenever a case is detected, while surveys are typically sample-based and designed for research purposes or to assess the prevalence of infection/disease within a particular territory [25]. The spatial analysis methodology was divided into three main groups proposed by Elliot and Wartenberg-disease mapping, geographic correlation studies, and disease clusters and clustering [26]. Disease mapping studies commonly measure morbidity or mortality for small geographic areas through smoothed or unsmoothed maps (e.g., graduated colour maps, graduated symbol maps, heatmaps, etc.). Geographic correlation studies investigate geographic variations across population groups in exposure to ecological factors relating them to health outcomes measured on a particular geographic scale. Finally, disease clusters and clustering studies consist of the investigation of excess events above a background rate either in time and/or in space.
Regarding the determinants, these were grouped into two categories: biotic and abiotic. Biotic factors are related to, or caused by living organisms, and abiotic factors are related to or caused by the non-living part of an ecosystem that shapes its environment. To name a few, as biotic factors, we have vector abundance, host abundance, and population density; and as abiotic factors, we have climatic and socioeconomic factors.

Collating, Summarising, and Reporting Results
We synthesised the information from the papers using the previously described research question and scope of the investigation. Tables and figures were created to systematise and summarise the information. Counts and proportions were used to summarise study findings and characteristics.
The main software used was EndNote for reading, organizing, and selecting studies. In addition, Excel 365 and ArcGIS software were also used to create tables and graphs and to map the geographic distribution of the studies.

General Characteristics
A total of 110 studies were included in the present review. More details on the characteristics of the included studies can be found in the Supplementary Material (Table S2). The years 2020, 2017, and 2014 concentrated the highest number of studies, with 14, 13, and 11 publications, respectively. The timeline from Figure 2 shows that the number of studies on the topic has grown over time.

Charting and Interpreting Data
The results were structured by general characteristics (e.g., country/region, year of publication); by themes according to the diseases and/or mosquitoes studied; by the scale of analysis (size of the spatial units) and geographic extent (global, continental, regional or local); type of outcome data used (notification or survey); the methodology used for spatial analysis; studied ecological determinants.
Regarding the type of outcome data used, notifications correspond to data on diseases/infections that, by statutory requirements, must be reported to the public health authority whenever a case is detected, while surveys are typically sample-based and designed for research purposes or to assess the prevalence of infection/disease within a particular territory [25]. The spatial analysis methodology was divided into three main groups proposed by Elliot and Wartenberg-disease mapping, geographic correlation studies, and disease clusters and clustering [26]. Disease mapping studies commonly measure morbidity or mortality for small geographic areas through smoothed or unsmoothed maps (e.g., graduated colour maps, graduated symbol maps, heatmaps, etc.). Geographic correlation studies investigate geographic variations across population groups in exposure to ecological factors relating them to health outcomes measured on a particular geographic scale. Finally, disease clusters and clustering studies consist of the investigation of excess events above a background rate either in time and/or in space.
Regarding the determinants, these were grouped into two categories: biotic and abiotic. Biotic factors are related to, or caused by living organisms, and abiotic factors are related to or caused by the non-living part of an ecosystem that shapes its environment.
To name a few, as biotic factors, we have vector abundance, host abundance, and population density; and as abiotic factors, we have climatic and socioeconomic factors.

Collating, Summarising, and Reporting Results
We synthesised the information from the papers using the previously described research question and scope of the investigation. Tables and figures were created to systematise and summarise the information. Counts and proportions were used to summarise study findings and characteristics.
The main software used was EndNote for reading, organizing, and selecting studies. In addition, Excel 365 and ArcGIS software were also used to create tables and graphs and to map the geographic distribution of the studies.

General Characteristics
A total of 110 studies were included in the present review. More details on the characteristics of the included studies can be found in the Supplementary Material (Table S2). The years 2020, 2017, and 2014 concentrated the highest number of studies, with 14, 13, and 11 publications, respectively. The timeline from Figure 2 shows that the number of studies on the topic has grown over time.  Examining the map of the geographic distribution of the studies, a total of 16 European countries have studied at supranational, national, regional, or local levels. Italy is the country with the most scientific studies (n = 18), followed by Germany (n = 10) and Spain (n = 9) (Figure 3).
Examining the map of the geographic distribution of the studies, a total of 16 European countries have studied at supranational, national, regional, or local levels. Italy is the country with the most scientific studies (n = 18), followed by Germany (n = 10) and Spain (n = 9) (Figure 3).

Studied Vectors and Infections/Diseases
The most investigated vectors are those of Dengue, Malaria, Chikungunya, and West Nile Virus, with 32 (24%), 26 (19%), 26 (19%), and 24 (18%) studies, respectively. With a smaller number of studies, 6% of studies focused on Zika, and the remaining studies analysed all mosquito communities, Rift Valley fever virus, Tularemia, Encephalitis, and others (Dirofilaria/dirofilariosis; Xylella fastidiosa and Hemoparasites and ectoparasites (acari and dipterans)) with 5%, 3%, 2%, 2%, and 2% of studies respectively (Figure 4). Italy is the European country where the most studies have been carried out, where eight studies focused on Chikungunya, another eight on West Nile Virus, and five on Dengue. Germany is the country where Malaria is most frequently studied with six studies. In the UK and in Spain the most commonly studied infections were Tularemia (n = 2) and Zika (n = 1), respectively (Figure 4).

Studied Vectors and Infections/Diseases
The most investigated vectors are those of Dengue, Malaria, Chikungunya, and West Nile Virus, with 32 (24%), 26 (19%), 26 (19%), and 24 (18%) studies, respectively. With a smaller number of studies, 6% of studies focused on Zika, and the remaining studies analysed all mosquito communities, Rift Valley fever virus, Tularemia, Encephalitis, and others (Dirofilaria/dirofilariosis; Xylella fastidiosa and Hemoparasites and ectoparasites (acari and dipterans)) with 5%, 3%, 2%, 2%, and 2% of studies respectively (Figure 4). Italy is the European country where the most studies have been carried out, where eight studies focused on Chikungunya, another eight on West Nile Virus, and five on Dengue. Germany is the country where Malaria is most frequently studied with six studies. In the UK and in Spain the most commonly studied infections were Tularemia (n = 2) and Zika (n = 1), respectively ( Figure 4).

Data Used, Type of Data, Geographical Extent, and Spatial Scale
Studies were conducted at different scales, with the study unit size ranging from 0.0005 km 2 to 400 km 2 (median 4 km 2 , IQR 8 km 2 ) and the mean population per area ranging from 1738 inhabitants to 60,000,000 inhabitants (median 28,048 inhabitants, IQR 662,501 inhabitants).
Studies have very different geographical extents: 42 (38%) were carried out at the national level, 24 (22%) at the global level (i.e., covering the entire world), 21 (19%) at the continental level, 19 (17%) at the regional level, and four (4%) at the local level. Regarding the type of data, 47 studies obtained data from notifications and 63 studies collected data using surveys.

Studied Biotic and Abiotic Factors
The abiotic variables most used to analyse the relationship with infections and to estimate probability and risk were temperature in first place, in 35% of the studies, followed by precipitation in 22% and water bodies in 13% ( Figure 5). Regarding the biotic factors, the most studied variables were the human population data or ratio (5%), population density (3%), and animals in farms (3%) (Figure 6).

Data Used, Type of Data, Geographical Extent, and Spatial Scale
Studies were conducted at different scales, with the study unit size ranging from 0.0005 km 2 to 400 km 2 (median 4 km 2 , IQR 8 km 2 ) and the mean population per area ranging from 1738 inhabitants to 60,000,000 inhabitants (median 28,048 inhabitants, IQR 662,501 inhabitants).
Studies have very different geographical extents: 42 (38%) were carried out at the national level, 24 (22%) at the global level (i.e., covering the entire world), 21 (19%) at the continental level, 19 (17%) at the regional level, and four (4%) at the local level. Regarding the type of data, 47 studies obtained data from notifications and 63 studies collected data using surveys.

Studied Biotic and Abiotic Factors
The abiotic variables most used to analyse the relationship with infections and to estimate probability and risk were temperature in first place, in 35% of the studies, followed by precipitation in 22% and water bodies in 13% ( Figure 5). Regarding the biotic factors, the most studied variables were the human population data or ratio (5%), population density (3%), and animals in farms (3%) (Figure 6).

Spatial Analysis Methods
Nearly half (46%) of the studies used geographic correlation analyses, and in the rest, in equal parts, clustering and surveillance analyses (27%), and disease mapping (27%) were used (Table S1). The three most widely used software for the analyses were: R Core Team software (37%), ArcGIS Desktop: Release 10. Environmental Systems Research Institute (ESRI), Redlands, CA (33%) and QuantumGis (QGIS) (9%) (the complete list can be found in Supplementary Table S3).

Disease Mapping
Disease mapping studies reported and mapped the geographic distribution of the occurrences of the diseases under investigation and analyse the geographic distribution

Spatial Analysis Methods
Nearly half (46%) of the studies used geographic correlation analyses, and in the rest, in equal parts, clustering and surveillance analyses (27%), and disease mapping (27%) were used (Table S1). The three most widely used software for the analyses were: R Core Team software (37%), ArcGIS Desktop: Release 10. Environmental Systems Research Institute (ESRI), Redlands, CA (33%) and QuantumGis (QGIS) (9%) (the complete list can be found in Supplementary Table S3).

Disease Mapping
Disease mapping studies reported and mapped the geographic distribution of the occurrences of the diseases under investigation and analyse the geographic distribution

Spatial Analysis Methods
Nearly half (46%) of the studies used geographic correlation analyses, and in the rest, in equal parts, clustering and surveillance analyses (27%), and disease mapping (27%) were used (Table S1). The three most widely used software for the analyses were: R Core Team software (37%), ArcGIS Desktop: Release 10. Environmental Systems Research Institute (ESRI), Redlands, CA (33%) and QuantumGis (QGIS) (9%) (the complete list can be found in Supplementary Table S3).

Disease Mapping
Disease mapping studies reported and mapped the geographic distribution of the occurrences of the diseases under investigation and analyse the geographic distribution of different mosquito species. They represented the incidence or abundance of mosquitoborne infections and mosquitoes using statistical or descriptive mapping techniques. Many created suitability maps or maps with the predicted distribution of individual species Sustainability 2022, 14, 8975 9 of 20 or species complexes. They also used predictive analysis methods based on ecological variables to identify and map risk areas suitable for transmission.
The most used methods in this spatial analysis group were the risk maps (n = 32), rate maps (n = 31), and case count maps (n = 14).

Clusters, Clustering, and Surveillance
The studies that applied clusters, clustering, and disease surveillance performed a geographic analysis of the location of cases to detect high-risk areas, such as outbreaks, and conducted a statistical analysis of spatiotemporal patterns through point density and modelling to study the dynamics of mosquitoes.
Most studies using cluster analysis focused on single infections and/or vectors, while a few explored co-clusters of more than one infection/vector [27]. To cite a few examples: in Italy, Dengue clusters tended to be located in coastal and urban areas [28][29][30]; in Greece, central Greece emerged as an important hotspot of Malaria, especially in districts with more water bodies [31]; in Sweden, hotspots of Malaria were concentrated around big inland lakes and in southernmost Sweden [31,32]; at a more global extent, France and the UK constitute critical Malaria hotspots with the highest number of cases, more than 4000 imported cases per year on average [33].

Geographic Correlation Studies
The geographic correlation studies analysed the correlation between ecological variables and the breeding and propagation of mosquito vectors. Investigations under this category produced maps that described the spatial patterns of ecological and sociodemographic determinants and the effects of environmental changes and investigated the spatial and temporal structure of disease transmission caused by those determinants, mainly by temperature. Additionally, they fitted simulation models that incorporated the principal mechanisms of the vector transmission cycle and combined them with fine spatial and temporal resolution data to study the time-series of factor suitability for transmission throughout time [34]. These studies found significant relationships between the habitat pattern and the pattern of mosquito clusters and biotic and abiotic factors [35]. A considerable amount also examined the potential global distributions of vectors in relation to global climate variation.

Biotic factors
Population Density [107,119] [46] Mobility [81,94] [94] Human Population Data/Ratio [30,71,104,106,126] Ditch shrimp of the genus Palaemonetes and Fish as predators [99] Birds [80] Horses [80] Animals in farms [56,61,63] To cite a few examples, in Europe as a whole, geographical accessibility, absolute humidity, and annual minimum temperatures were the strongest predictors for the presence of Aedes vectors [119]. In addition, the best environmental predictors of West Nile Fever outbreaks in Europe were climatic (maximum temperature of the warmest month and annual temperature range), human-related (rain-fed agriculture, density of poultry and horses), and topo-hydrographic variables (presence of rivers and altitude) [80]. In the Netherlands, higher elevation, precipitation, day and night temperature, and vegetation indices were important predictors of the occurrence of An. plumbeus [48]. In Hungary, wetlands were important target areas for mosquito control [132]. In Italy, Ae. albopictus was mostly found near areas with a human presence and urban landscape [38], while Culex pipiens had a more scattered distribution and could be found in wilder and less urbanised areas [100]. Finally, in Spain, unsuitable areas for Culex pipiens were located at higher altitudes and in colder regions [96].

Discussion
This scoping review has demonstrated that investigations apply various spatial analysis techniques to studying mosquito-borne infections in Europe. Most studies used geographical correlation analysis with a wide range of spatial modelling techniques implemented in specialised statistical software. The remaining studies used spatial cluster detection methods and disease mapping, mostly done using frequentist approaches in GIS software such as the ArcGis (ESRI), GRASS GIS, R software, QuantumGis, and GeoDa [27,29,36,45,49,66,76,88]. The most frequently studied infections were Dengue, Malaria, Chikungunya, and West Nile Virus, and the most widely studied ecological determinants were temperature and precipitation, as well as water bodies and vegetation. Studies were predominantly conducted at a global or a continental level and in particular countries such as Italy, Germany, and Spain.
Overall, spatial analysis studies applied to mosquito-borne infections and vectors in Europe have increased over the last two decades. While most approaches are based on classical frequentist statistical methods, such as generalised linear models, recent advances in computing, statistical methodology, and the availability of high-resolution, geographically referenced databases led to the use of new techniques such as Bayesian Models, geostatistical methods such as Kriging estimation and ecological niche models. These methods should be used to the detriment of the previous ones because they account for spatial dependency and other analytic challenges, such as spatial confounding and small number problems [26]. Innovative analytical and pioneering skills and tools in spatial statistics should be employed to analyse existing data allowing to inform policymakers and other stakeholders better. For that, and to overcome the complexity of spatial analysis and spatial data management, it is important to invest in the development of intuitive and ready-to-use software for spatial epidemiological analysis. In fact, in the last years, many examples have emerged. For instance, many sophisticated analytical tools suitable for Big Data (e.g., Mann-Kendall space-time trend analysis, convolutional neural network approaches [133,134], image classification) are now implemented in commercial software like ArcGIS Pro, while at the same time many researchers and developers have created and updated open-source apps and software to facilitate spatial analyses (SaTScan, GeoDA, Crimestat, among others).
According to the studies included in this review, the threat of viruses to Europe is low but uncertain, justifying the need to keep monitoring from areas of greatest predicted environmental suitability of mosquito-borne infections, especially in the Mediterranean and central Europe. The results of some studies have verified where there is a risk of introducing and spreading the infectious diseases under study and also showed that the temporal variation in the number of publications over the years is driven by the fluctuating topicality of mosquito-borne diseases in the medium and large-scale climate conditions [126]. Air temperature and, to a lesser degree, relative humidity, soil water content and wind speed seem to significantly affect the epidemiology of mosquito vectors in Europe [116]. Many studies identify clusters of infections and vectors covering specific localities and regions within certain European countries [36,66,83,97,98,114]. In these areas, the risk of disease transmission should be reduced by reducing mosquito-human contact by reducing mosquito populations and eliminating breeding sites.
Spatial analysis, through the use of innovative tools like the ones referred above, has the potential to help identify target areas, biotic and abiotic ecological determinants, and assess the risk of emergence or re-emergence of vectors and mosquito-borne infections, as demonstrated by various studies [27,64]. In fact, the implementation of the spatial analysis techniques substantially helped to improve the results of the mosquito abatement programs [131]. In addition, the results of these analyses provide relevant information for surveillance activities aiming to identify where the local transmission is higher and where is the potential for the vector-borne introduction [66].

Strengths and Limitations
This is the first review to provide the range and depth of published studies using spatial analysis techniques to analyse the geographical distribution of mosquito and mosquitoborne diseases in Europe and associated biotic and abiotic determinants. Our search strategy was exhaustive and transparent, in accordance with the scoping reviews' methodological guidelines, covered a period of circa 25 years, and provided an evidence base for future spatial epidemiology studies on the topic. This scoping study began at the end of July 2021 and the selected studies were retrieved in October 2021 for analysis, as defined in the study protocol found at: https://zenodo.org/record/6758947#.YrlkAnbMKUk (accessed on 9 August 2021).
However, some limitations of this scoping review must be discussed. Firstly, the studies that were not indexed in the searched databases or (if they were) not available in the included languages were omitted. Secondly, the scoping review methodology has some inconveniences despite being the most suitable for the purpose of our study. For example, no restrictions were placed on the included studies to guarantee homogeneity because it does not allow for the meta-analysis of the associations between ecological factors and outcomes. Even so, the meta-analysis would not be feasible due to the fact that research on this topic is recent, heterogeneous, and sparse. In addition, our literature review, like others, is subject to publication bias; it is recognised that the studies most likely to be published are those with significant associations.

Evidence Gaps and Recommendations
The present scoping review allowed us to identify evidence gaps that should be addressed in future studies on the topic. One of the weaknesses in this scoping review is the lack of investigations carried out at the finer scales using local geographical extents. It is fundamental in precision public health (PPH) and in the efficient allocation of resources, using finer scales to identify clusters of cases/vectors and inequalities to monitor disease variation. Additionally, few studies explored the issue of the Modifiable Areal Unit Problem (MAUP) in spatial analyses which affects the study conclusions due to the number and the size of the scale used to define the same areas [89,135]. As part of sensitivity analysis, we recommend that studies consider using multiple geography levels to assess how robust the results are to the chosen geographies. We also found that there are no studies in some countries located in northern Europe due to the lower influx of vectors. However, it is essential to highlight that with climate change, it is advisable to monitor changes in biodiversity and climate in these areas to anticipate a future introduction of vectors. Thus, more studies should be conducted in northern European regions. The results also highlight the difficulties in modelling climate and viruses. It is difficult to predict the incidence of infectious diseases, despite the predicted changes that result in the distribution of the vector. The local temperature adaptation, vector-pathogen interactions, and human-derived landscape changes are distinctive processes that may have important roles in creating future dynamics of pathogen transmission. Therefore, more complex study designs (e.g., system approach, agent-based models) should be used to capture such dynamics. Despite the growing recognition of One Health, few studies explicitly addressed the entire triad of animals-humans-environment in their analysis [136]. Finally, while the included studies addressed a wide range of biotic and abiotic factors, the role of socioeconomic factors has been insufficiently addressed. However, they are well-established determinants of communicable and non-communicable human and animal diseases [137][138][139]. Since, to prevent and control the dissemination of infection in both humans and animals, sanitation and enhancement of hygiene practices are very important, socioeconomic factors may constitute major determinants of mosquito-borne disease [137]. However, as demonstrated by a recent literature review [140], focused on endemic countries, the association between socioeconomic conditions and vector-borne diseases is unclear and may be highly situationally dependent. Thus, by adding to the current body of literature other continents, namely Europe-where socioeconomic conditions are better but socioeconomic inequalities in health and pockets of poverty persist-may help to better understand the directionality of the associations between mosquito-borne infections and socioeconomic factors and design tailored interventions according to the socio-economic profile of the communities. Hence, future studies should explore the direct and indirect associations between socioeconomic factors (e.g., area level deprivation, population literacy, education, housing conditions) and mosquito-borne infections.

Conclusions
In the contemporary context of globalisation and climate change, the spatial analysis of mosquito-borne infections and their vectors constitutes an essential component for understanding the present and future burden of these emerging diseases in Europe. Our review described the spatial analysis approaches and potential predictors used in mapping mosquito-borne infection risk. Results from this review may help guide researchers aiming to conduct spatial epidemiological studies of mosquito-borne infections and may support public health and territorial planning policies and programs towards vector control and mosquito-borne disease prevention in Europe.