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The spatial relationship between leishmaniases and sand flies in Europe and neighboring countries

Parasites & Vectors volume 17, Article number: 404 (2024) Cite this article

Abstract

Background

Leishmania infantum is endemic in Europe (and elsewhere) while L. donovani s.s., L. tropica and L. major are not but are present in neighboring countries in North Africa, the Middle East, (the Asian part of) Turkey and the Southern Caucasus. Lists of sand fly vector species in the scientific literature vary with the criteria for vector incrimination, and criteria vary because, for some, evidence is difficult to generate. With minimal criteria, about 20 sand fly species are proven or suspected vectors of L. infantum in Europe and neighboring countries, while for L. tropica and L. major, there are seven and four proven or suspected vector species, respectively, in this area. For L. donovani s.s., present in Cyprus, the Middle East and (the Asian part of) Turkey, no local vectors have been incriminated so far. The aim was to assess the degree of spatial agreement between Leishmania spp. and various vectors species and their relative contribution to the explained variation.

Methods

We used multivariate regression modeling to analyze the spatial relationship between autochthonous Leishmania spp. and clinical forms in humans and animals and 14 Phlebotomus spp. in Europe and neighboring countries.

Results

There was only fair agreement between parasite and vector distributions. The most parsimonious models describing the distribution of Leishmania spp. and clinical forms included three to six sand fly species and explained between 12% (L. infantum) and 37% (L. donovani) of the observed variation. Selected models included confirmed and suspected vector species as well as unexpected species.

Conclusions

The relatively low agreement between Leishmania and vector distributions highlights the need to improve leishmaniasis reporting and vector surveillance in areas where no information is available, both for a better understanding of the epidemiology of infection in endemic areas and to monitor possible spread of infection into non-endemic areas. While some of the unexpected sand fly-Leishmania spp. statistical associations might be spurious, for others, the existence of sporadic or recent reports of infections warrants further vector competence studies that consider strain variation.

Graphical Abstract

Background

In Europe and neighboring countries, Leishmania spp., transmitted by phlebotomine sand flies (Diptera, Psychodidae), are responsible for human and animal leishmaniases that are endemic in countries bordering the Mediterranean Sea and Black Sea [1]. Endemic species in this area include Leishmania donovani sensu lato (s.l.), which is a species complex including L. donovani sensu stricto (s.s.) and Leishmania infantum [2] and Leishmania tropica and Leishmania major. The most frequent clinical forms of human leishmaniasis include visceral leishmaniasis (VL), associated with L. infantum and L. donovani s.l., and cutaneous leishmaniasis (CL), caused by any of the four species. Leishmania infantum is the main Leishmania pathogen in animals, dogs being the most sensitive host species, and canine leishmaniasis (CanL) is a major disease of dogs. If not treated promptly, VL and CanL are life-threatening conditions. Cutaneous leishmaniasis is most commonly a localized infection characterized by persistent skin nodules and ulcers which eventually self-heal but are a significant cause of social stigma and work-related disability [3]. The incidence of CL is notably higher than that of VL, especially in Northern Africa and the Middle East, where it is primarily associated with L. tropica and L. major [4]. These two species and L. donovani s.s.—a highly prevalent species in Eastern Africa and the Indian continent—are not endemic in Europe, although sporadic cases of L. donovani s.s. have been reported in the Middle East [5,6,7].

Leishmania spp. endemicity depends on the presence of specific vectors and reservoir host species. Leishmania infantum and L. major have zoonotic transmission cycles, involving dogs and wild rodent species as primary reservoirs, respectively. The cycle of L. donovani s.s. is anthroponotic, with humans being the primary reservoir, while L. tropica exhibits both anthroponotic and zoonotic cycles involving rodents and hyraxes as reservoirs [8]. The number of sand fly species/subspecies presently recognized worldwide is 1060 [9], but only about 100 are proven or suspected vectors according to Maroli and colleagues [10], who used the following “minimal requirements for robust vectorial incrimination: (a) epidemiological evidence indicated by the overlapping of the geographical distributions of the vector and the human disease; (b) evidence that the vector feeds on humans, and (c) evidence that the vector supports natural gut infections with promastigotes of the same Leishmania species as occurs in humans.” The criteria of Maroli and colleagues for vector incrimination are a simplification and relaxation of criteria by WHO [11], combined with a supporting criterion of Killick-Kendrick [12] “that (the fly) is present in places where the Leishmania and the disease it causes are found.” The WHO criteria (2010) require that (i) the vector must be anthropophilic; (ii) the vector must bite the reservoir host(s); (iii) the vector must be infected in nature with the same Leishmania as occurs in humans; (iv) the vector must support flourishing growth of the parasite it transmits; (v) the vector must be able to transmit the parasite by bite. Maroli and colleagues [10] cited, in particular, difficulties with testing vectors against the fifth criterion that the vector is able to transmit the parasite by bite to a susceptible host while taking a blood meal. Of note, Ready [13] raised the bar for vector incrimination over those listed by WHO [11] not only by requiring evidence for strong ecological associations but also by requiring evidence (through mathematical modeling) that the vector is essential for maintaining transmission and that changes in ‘biting densities’ affect disease incidence.

Maroli and colleagues [10] classified vectors as proven when the evidence met their criteria, or as suspected vectors, if there was only epidemiological evidence of overlapping spatial distributions. According to these authors, there are no records of proven vectors of L. donovani s.s. in Europe and neighboring countriesFootnote 1. Nevertheless, Phlebotomus (Paraphlebotomus) alexandri is a proven vector of L. donovani s.l./L. infantum in China and Phlebotomus (Adlerious) longiductus (present in e.g. Ukraine) is a suspected vector of L. donovani s.s. in northern China. In contrast, proven vectors of L. infantum in Europe and neighboring countries include Phlebotomus (Larroussius) ariasi, P. (Adlerious) balcanicus, P. (Larroussius) kandelakii, P. (Larroussius) langeroni, P. (Larroussius) major s.l., P. (Larrousius) perfiliewi, P. (Larroussius) perniciosus and P. (Larroussius) tobbi. The P. (Ad) major s.l. complex encompasses Phlebotomus (Larroussius) neglectus (Europe, Asian part of Turkey), Phlebotomus (Larroussius) syriacus (Middle East), P. (La) major s.s. (Iran and India) and other less-characterized species [14], and P. (La) perfiliewi includes subspecies Phlebotomus (Larroussius) galileus and Phlebotomus (Larrousius) transcaucasicus [15]. The vectorial role of P. (La) galileus and P. (La) syriacus has not been confirmed. Other suspected L. infantum vectors in the study area mentioned by Maroli et al. [10] are P. (Pa) alexandri, Phlebotomus (Adlerious) halepensis, P. (Larroussius) longicuspis, P. (Adlerious) longiductus (this species is a proven vector in Kazakhstan), Phlebotomus (Transphlebotomus) mascittii (see also [16]) and Phlebotomus (Adlerious) turanicus. According to Lewis & Ward [17], other suspected L. infantum vectors are Phlebotomus (Adlerious) simici in the Eastern Mediterranean, Phlebotomus (Adlerious) brevis in Kazakhstan and Phlebotomus (Adlerious) kyreniae in Cyprus.

Phlebotomus (Paraphlebotomus) sergenti and Phlebotomus (Adlerious) arabicus are proven vectors of L. tropica [15, 18]. Phlebotomus (Paraphlebotous) similis, considered a sister species of P. (Pa) sergenti [15], was incriminated as a vector of L. tropica in Crete [19]. Other suspected vectors of L. tropica are Phlebotomus (Paraphlebotomus) chabaudi, the closely related Phlebotomus (Paraphlebotomus) riouxi [20], Phlebotomus (Paraphlebotomus) jacusieli and Phlebotomus (Paraphlebotomus) kazeruni [15]. Finally, Phlebotomus (Phlebotomus) papatasi is a specific vector of L. major, and other species incriminated in the transmission of L. major include P. (Pa) alexandri [21], P. (Ad) halepensis [22] and P. (La) langeroni [23, 24].

The ability of vectors to sustain development of one or more than one Leishmania spp. is used to classify them as “non-permissive” or “permissive” vectors. Specificity for Leishmania spp. is based on the presence of receptors in the sand fly midgut that allow binding of specific ligands in the nectomonad stage of the parasite [25, 26]. Phlebotomus papatasi and P. sergenti are considered non-permissive and specific vectors of L. major and L. tropica, respectively. In contrast, permissive species, which may include other Phlebotomus spp., do not display such specificity for Leishmania spp. [22, 27,28,29].

Because the geographical distribution of vectors is a key aspect influencing the epidemiology of leishmaniasis, the European Centre for Disease Prevention and Control (ECDC) has been compiling and mapping the presence and absence of Leishmania spp. vectors in Europe and neighboring countries at NUTS3/GAUL2 spatial resolution through comprehensive literature reviews and, in some cases, unpublished surveillance data, in the context of projects such as VBORNET (2010–2013) and VectorNet (2014–2023), the latter in collaboration with the European Food Safety Authority (EFSA). Furthermore, ECDC commissioned a review of the epidemiology of leishmaniasis in this region, involving peer-reviewed and gray literature from 2009 to 2020, which included the mapping of reported Leishmania spp. infections and clinical forms [30]. The compiled data on the presence and absence of Leishmania spp. and clinical leishmaniasis and their confirmed and suspected respective vector species were subsequently investigated [31]. The present study expands the latter investigation, including parasite and vector information published up to March 2023, and analyzed the relationship between Leishmania spp. and visceral leishmaniasis, and Phlebotomus spp., regardless of their known specificity for Leishmania spp. The investigation presented in this article had two aims. The first was to assess the statistical association between spatial distribution (the presences and absences in spatial units) of Leishmania spp. and clinical forms on one hand and the spatial distribution of confirmed and suspected vector species on the other in an attempt to test whether this association could provide some epidemiological insights into their potential vector status and relative importance in this area. The second aim was to identify areas without parasite and/or vector information where enhanced surveillance should be promoted.

Methods

Sand fly vector and Leishmania spp. data

Data on the distribution of 14 confirmed and suspected vector species for Leishmania spp. in Europe and neighboring countries were extracted from the VectorNet database (requested from ECDC as described in: https://www.ecdc.europa.eu/en/about-us/document-request), updated as of March 2023, across 1506 territorial units at NUTS3/GAUL2 spatial resolution, referred to as “mapping polygons.” These species are considered priority species for VectorNet mapping and include: Phlebotomus alexandri, P. ariasi, P. balcanicus, P. kandelakii, P. halepensis, P. langeroni, P. major s.l. (including P. major s.s. and P. neglectus), P. mascittii, P. papatasi, P. perfiliewi, P. perniciosus, P. sergenti, P. similis and P. tobbi. They represent 93% of species presence records in the VectorNet database. Other vector species and subspecies, cited in the introduction, were not included because they have a comparatively small distribution in the VectorNet area.

The 14 vector species were categorized by VectorNet based on their distribution status, with categories being ‘observed presence,’ ‘observed absence,’ ‘presumed absence,’ ‘unknown presence’ and ‘no data’. For this study, we merged ‘observed absence’ and ‘presumed absence’ into a single category labeled ‘absent’ while ‘unknown presence’ was grouped with ‘no data.’ Therefore, the vector distribution categories considered in our analysis were ‘present’, ‘absent’ and ‘no data’ only. Additionally, we assumed that a species was absent from any given polygon where at least one sand fly trapping study had been conducted, and the species was not found, irrespective of the sampling effort.

Presence and absence data on autochthonous Leishmania spp. infections and clinical forms in humans and animals (including vectors) were procured from the ECDC leishmaniasis review [30], updated with further scientific documents published in the SCOPUS database between August 2020 and March 2023. The review incorporated 1167 scientific articles and 120 additional documents, including 46 PhD and MSc theses. Furthermore, data from the National Epidemiological Surveillance networks of Bulgaria, France and Greece, as well as the Centralized Hospital Discharge records of Italy, Malta, Portugal and Spain, were included in the dataset. In cases where a mapping polygon had not reported any Leishmania spp. or leishmaniasis cases, it was considered absent. In some cases, only the clinical form of leishmaniasis in humans (CL and VL) was provided without specifying the species responsible, and vice versa. In our analysis, we combined and examined all species and clinical forms together for a comprehensive overview.

Study area: combination of presumed Leishmania spp. and leishmaniasis and vector distributions

We defined specific areas that encompassed both the presumed distribution of Leishmania spp. and/or leishmaniasis clinical forms and their associated vectors to examine their spatial relationship and agreement. These delineated areas for Leishmania spp., clinical forms and vectors were established by connecting the central points of the outermost consecutive polygons where they were reported as present. To achieve this, we utilized the geoprocessing tool ‘Aggregate Points’ within the generalization toolkit available in ArcGIS version 10.5 [32]. This tool is designed to outline areas surrounding clusters of nearby point features, requiring a minimum of three or more points within a specified aggregation distance. In our case, this aggregation distance was defined as 10 of geographical coordinate units, which is roughly equivalent to approximately 1110 kms. This distance corresponds to one-third of the entire longitudinal span of the study area, ensuring the creation of compact and well-defined groupings of points.

The resulting study area excluded polygons located outside the combined presumed distribution of Leishmania species and/or clinical forms and their vectors. Additionally, polygons lacking information regarding vector presence and those situated beyond the scope of the leishmaniases review area were also excluded from the final analysis.

Statistical analysis

We calculated the number of polygons in the study area where Leishmania species and/or clinical forms and vectors were reported from. To assess the statistical relationship and level of agreement between parasite and vector distributions, we employed bivariate logistic regression (without accounting for spatial autocorrelation) and Cohen's kappa coefficient (k) analysis, respectively. Furthermore, we conducted multivariate logistic modeling to explore the independent contributions of individual vector species to the distribution of Leishmania spp. and clinical forms [33]. We selected models with the highest McFadden pseudo R2 values for an increasing number of vector species and compared models using likelihood ratio tests (LRTs) with an alpha threshold of 0.05. Models where the coefficient for at least one of the vectors was negative were excluded.

All statistical analyses were carried out using the R statistical software package [34]. Maps illustrating the distribution of vector species and Leishmania species along with clinical forms were generated using ArcGIS version 10.5 [32].

Results

Spatial distributions of autochthonous Leishmania species and clinical forms and Phlebotomus spp. vectors

Maps of the reported presence of autochthonous Leishmania spp. and clinical forms and vectors are provided as supplementary material in Figs. S1 to S25. Table S1, also in the supplementary material, presents the number of polygons in the final study area (after superimposing leishmaniasis and vector delineated areas), those where individual Leishmania spp. and clinical forms and vector species were reported from and the bivariate statistical relationship and degree of agreement between Leishmania spp. and clinical forms and vector species.

There were 848 polygons in the study area; leishmaniasis was reported in up to 556 polygons when considering all Leishmania spp. and clinical forms (VL and CL) and in as few as 11 polygons for L. donovani s.s. (Table S1). Similarly, for vector species, the number of polygons ranged between 591 polygons for all species together and 15 polygons for P. kandelakii (Table S1). Most leishmaniasis and vector distributions were significantly associated with each other, but the degree of agreement was generally low (slight or less than expected by chance) except for a few associations with fair agreement (Table S1). Fair agreement included associations, for example between L. infantum and P. perniciosus, L. donovani s.s. and P. halepensis and L. tropica and L. major with P. alexandri and P. sergenti (Table S1). Table S2 in the supplementary material provides lists of polygons where: (i) leishmaniasis was reported and vector surveillance existed but no vectors were found, (ii) leishmaniasis was reported and no vector surveillance existed and (iii) leishmaniasis was not reported while surveillance existed, and vectors were found.

Multivariate relationships between autochthonous Leishmania spp. and visceral leishmaniasis and Phlebotomus spp. distributions

The selection of vectors with distributions associated with distributions of Leishmania spp. and VL and the corresponding model McFadden’s adjusted pseudo-R-squared statistic are presented in Table 1.

Table 1 Selected (multivariate) logistic regression models of the relationship between Leishmania spp. and/or clinical forms and their sand fly vector species
Full size table

The best single sand fly species model explaining the spatial variation of L. donovani s.s. was the one with P. halepensis (R2 = 0.203). With two species, the species combination that explained the most L. donovani s.s. variation was P. major s.l. together with P. alexandri (R2 = 0.333), slightly higher than the combination of P. halepensis with P. alexandri (R2 = 0.312). Finally, the three-species combination that explained most L. donovani s.s. variation involved all three: P. alexandri, P. halepensis and P. major s.l. (R2 = 0.369) (Table 2). Among these, P. major s.l. had the highest regression coefficient (Table 2).

Table 2 Coefficients of the most complex selected (multivariate) logistic regression models of the relationship between Leishmania spp. and/or clinical forms and their sand fly vector species
Full size table

Regarding L. major spatial variation, the single species model with P. alexandri scored slightly higher (R2 = 0.164) than the one with its natural vector P. papatasi (R2 = 0.160) (Table 1). However, in the most complex model, which included P. papatasi, P. alexandri, P. halepensis, P. langeroni and P. perniciosus (R2 = 0.259), P. papatasi had the highest coefficient, closely followed by P. alexandri (Table 2).

As for L. tropica, the best single species model was that with its natural vector, P. sergenti (R2 = 0.152). With two vector species, the combination that explained most variation was P. alexandri with P. papatasi (R2 = 0.195), slightly better than the combination of P. sergenti with P. alexandri (R2 = 0.193). Finally, the three-species combination that explained most variation involved all three, P. sergenti, P. alexandri and P. papatasi (R2 = 0.212) (Table 1), with similar regression coefficients (Table 2).

With a single species to explain the variation in L. infantum, P. perniciosus explained most variation (R2 = 0.053) (Table 1). With two species, the best combination was P. perniciosus with P. similis (R2 = 0.091), slightly better than the combination of P. perniciosus with P. tobbi (R2 = 0.085). Phlebotomus similis (which is not a natural vector of L. infantum) continued to be included in the models as the complexity increased. The most complex model included P. perniciosus, P. similis, P. tobbi, P. ariasi, P. perfiliewi and P. kandelakii (R2 = 0.124) (Table 1), and P. similis had the highest regression coefficient, followed by P. kandelakii (Table 2). With P. similis excluded, the most complex model also included P. perniciosus, P. tobbi, P. ariasi, P. perfiliewi and P. kandelakii (R2 = 0.105).

Finally, for L. infantum combined with VL, sand flies could explain more variation than for L. infantum alone. Here, Phlebotomus perniciosus was also the single species explaining the most variation (R2 = 0.091) (Table 1) and continued to be included as the complexity increased. The two species model that explained most variation also included P. similis (R2 = 0.130), slightly more than the combination of P. perniciosus with P. tobbi (R2 = 0.123). Also here, P. similis continued to be included in the models as the complexity increased. The most complex model included P. perniciosus, P. similis, P. papatasi, P. tobbi, P. ariasi and P. kandelakii (R2 = 0.164). Excluding P. similis and P. papatasi, the most complex model included the same five species as in the analysis with only L. infantum data: P. perniciosus, P. tobbi, P. ariasi, P. perfiliewi and P. kandelakii (R2 = 0.140). Interestingly, P. similis had the highest regression coefficient, followed by P. perniciosus (Table 2).

Discussion

In the VectorNet region, which encompasses most of the Western Palearctic region, the distributions of autochthonous leishmaniases and vectors are primarily restricted to nations bordering the Mediterranean Sea and Black Sea. The differences in the distributions of various Leishmania spp. are not only explained by different vectors with different distributions but also by distinct reservoir host distributions. The extensive prevalence of L. infantum and VL can be largely attributed to dogs and other widespread host species [35] serving as reservoir for this parasite across the entire study area and to the large number of vector species capable of transmitting this parasite. In contrast, the transmission of L. major and L. tropica within this area is confined to North Africa and the Middle East (including Azerbaijan), despite their confirmed vectors (P. papatasi for L. major and P. sergenti for L. tropica) being present also in Europe. Zoonotic cycles for these Leishmania spp. depend on rodent species and hyraxes, which are absent in Europe. Anthroponotic transmission cycles of L. tropica in North Africa and the Middle East, as well as L. donovani s.s. in Turkey, are typically associated with impoverished urban and rural environments characterized by a high level of human-vector interaction. Concerns are raised about the potential for spread of L. tropica and L. donovani s.s. in southern European countries where competent vectors are widely present, albeit at a lower density [36].

Several factors probably contribute to the limited concordance between the spatial distributions of Leishmania spp. (and clinical manifestations) and the distributions of their vectors, including: (i) the absence of Leishmania spp. infections in vector populations in some regions, particularly in regions in the northern limit of the vector distribution range, such as large parts of France, southern Germany and Austria for L. infantum, as well as throughout Europe for L. donovani s.s., L. tropica and L. major. For L. infantum, this could be due to climatic limits to its transmission. For L. tropica, and L. major, the aforementioned limits in the distributions of the natural host reservoir provide an additional explanation: (ii) limited and sporadic studies on vector distribution and surveillance in many countries, leading to incomplete data, and failure to recognize the presence of vectors in areas because of low sampling efforts; (iii) significant underreporting of leishmaniasis cases, especially CL and CanL [37, 38]; (iv) the challenge of diagnosing L. infantum infections, since many infected dogs and most infected individuals remain asymptomatic [39]; (v) the possibility that reference laboratories and referral hospitals carrying out leishmaniasis diagnoses are not necessarily in the same area as the patients’ probable infection sites; (vi) the utilization in the analysis of administrative geographical units (NUTS3/GAUL2), which may not accurately reflect the ecological distribution of vectors or hosts; (vii) disparities in the sizes of administrative units, which could introduce bias into the analysis, particularly without adjustments for spatial autocorrelation. Possibly, agreement between parasite and vector distributions could have been somewhat increased if minoritarian suspected Phlebotomus vector species and subspecies, distributed in North Africa and the Middle East, had been included in the analysis. Of the 7% of records in the VectorNet database that were not of the 14 species included, most related to P. longicuspis (5% records) and P. simici (1% records).

Expanding the scope of sand fly surveillance in regions where Leishmania spp. and/or clinical forms have been reported but lack sand fly distribution data or so far reported no sand fly findings could yield more meaningful insights. In Europe, many of these regions are on the fringe of the broader L. infantum endemic zone, for example areas on the northern Atlantic coast of Spain. Also, enhancing leishmaniasis monitoring in peripheral vector areas with no reported cases should increase our chances of detecting parasite introductions via the movement of infected individuals and animals. The risk of L. infantum spreading into these areas after the introduction of infected dogs is deemed to be substantial [40]. Such risk can be reduced by implementing measures like pre-importation Leishmania spp. analysis of foreign dogs and the application of insecticides on dogs visiting endemic areas [40].

The distributions of L. infantum (with and without VL) and that of L. donovani s.s. were best predicted by a limited combination of rather than by all its proven and suspected vector species. This is to be expected, particularly for L. donovani s.s., which has a very small geographical distribution compared to its potential vectors in the study area. The three selected vector species in the most parsimonious L. donovani s.s. model, P. alexandri, P. halepensis and P. major s.l., are “permissive” vectors, and there is evidence of L. donovani s.s. transmission by P. major s.l. and P. alexandri in China and Iran [10, 21, 41]. The latter vector species was also associated with L. tropica, L. major and L. infantum transmission in Iran [21]. The role of P. halepensis as a vector of L. donovani s.s. has been neither suspected nor proven. However, this species was a suspected vector of VL in former USSR states [17] and is highly permissive to L. tropica and L. major infection in the laboratory [22].

The sand fly species selected in the model for L. infantum and VL were P. ariasi, P. kandelakii, P. perniciosus, P. tobbi, P. similis and P. papatasi, which are proven vectors of L. infantum in Europe according to Maroli and colleagues [10], except for the last two (which are suspected and proven vectors of L. tropica and L. major, respectively). Models excluding these two species incorporated P. perfiliewi instead, a confirmed L. infantum vector, but had a lower proportion of explained variation (R2 = 0.140 vs 0.164). The biological basis for the selection of P. papatasi in the L. infantum and VL model is doubtful because it is a “non-permissive” vector. This was demonstrated in an experimental study in which P. papatasi failed to sustain late-stage L. infantum infections while supporting L. major and, interestingly, also L. major/L. infantum hybrids [27]. Pimenta and colleagues [42] previously described P. papatasi's inability to sustain long-term L. donovani s.s. infections. Nonetheless, there are reports of natural L. infantum infections in P. papatasi female specimens with an empty abdomen, suggesting that the parasite was retained after the blood meal was digested and remnants excreted [43,44,45]. Unlike P. papatasi, the vectorial competence of P. similis for L. infantum has not been investigated. Of note, the other proven L. infantum vectors P. balcanicus, P. langeroni and P. major s.l. and the suspected vectors P. alexandri, P. halepensis and “probable vector” P. mascittii were not selected in these models. Like for L. donovani s.s., L. infantum and VL have not been reported in some areas where confirmed and suspected vectors are found, such as areas in central Europe with P. mascittii and in Romania for P. balcanicus. The reasons why other vector species were not selected in the models is likely to be related to their distributions overlapping with those of selected species. For example, P. langeroni and P. ariasi distributions in the Iberian Peninsula overlap with that of P. perniciosus, similarly, P. tobbi, P. major s.l. and P. perfiliewi in Italy, Greece and Turkey; P. alexandri with P. perniciosus in Spain and North Africa and with P. tobbi in the Middle East; and P. balcanicus, P. kandelakii and P. halepensis in the southern Caucasus. The selection of P. tobbi over P. major s.l. and P. perfiliewi in the L. infantum and VL model may suggest a greater vectorial capacity of P. tobbi compared to the other two species. However, it could also be that, in contrast to P. tobbi, P. major s.l. and P. perfiliewi are also found in L. infantum-endemic areas where there are other selected vectors, such as P. perniciosus in Western Europe and Northern Africa [46] and P. kandelakii in the southern Caucasus [46], thus limiting their predictive value. The selection of P. ariasi in L. infantum models, despite its sympatry with P. perniciosus in many areas, is compatible with its presence in some colder, humid ecosystems in Andorra and France where P. perniciosus is not reported [47, 48]. Phlebotomus perniciosus was the vector which in the single species model best explained the distribution of L. infantum. Unlike other Phlebotomus spp., P. perniciosus and L. infantum and VL are found in most NUTS3 geographical areas in the western part of the VectorNet area. In the review of Massoels and colleagues [49], P. perniciosus was the only sand fly vector (apart from Lutzomyia and Pintomyia species, which do not occur in the VectorNet geographic area) to have been classified as having both evidence of infection in unfed females in the field and evidence of vector capacity in laboratory studies. Other Western Palearctic species for which there was evidence of infection in unfed females in the field included P. longicuspis, P. longiductus, P. mascittii, P. neglectus, P. tobbi and, as mentioned before, P. papatasi, but not P. ariasi, P. kandelakii and P. perfiliewi [49].

Selected species in the L. tropica model were its principal vector, P. sergenti, and further P. alexandri and P. papatasi, but not P. similis. Phlebotomus alexandri is considered a permissive vector species, and in the above-mentioned study by Naghian and colleagues [21], unfed P. alexandri females harbored L. tropica as well as L. infantum and L. major infections [21]. The competence of Phlebotomus papatasis for L. tropica infection has been investigated in the laboratory by several authors. Killick-Kendrick and colleagues [50] reported complete development of L. tropica 7 to 9 days post-infection in 2 out of 36 (6%) specimens infected with a high dose of amastigotes and concluded that P. papatasi was unlikely to play an important role in the transmission of L. tropica. Similarly, Darwish and colleagues [51] reported no development of L. tropica in P. papatasi beyond day 3 of infection. Kamhawi and colleagues [25] demonstrated lack of development of L. tropica and L. donovani s.s. in P. papatasi from the Middle East. The absence of P. similis in the L. tropica models may be justified by its distribution overlapping that of selected vectors.

Finally, the model for L. major included its only proven natural vector in the study area, the restrictive vector P. papatasi, as well as the permissive species P. alexandri, P. halepensis, P. langeroni and P. perniciosus. As mentioned before, P. halepensis displayed high susceptibility to L. major (and L. tropica) infections in laboratory experiments [22]. Phlebotomus langeroni similarly sustained L. major development in earlier experiments [23, 24], and there is evidence of L. major infection in non-engorged P. alexandri female field specimens [21]. Moreover, laboratory experiments demonstrated L. major's ability to infect P. perniciosus promastigotes [52], and preliminary work within the CLIMOS project (https://climos-project.eu/) indicates that this vector species is indeed permissive of L. major infection (Dr. Jovana Sádlová, CLIMOS meeting, Prague, Czech Republic, December 12, 2023).

In summary, there is a fair degree of agreement between the spatial distributions of Leishmania spp. and their main vectors in Europe and neighboring countries. The fact that the agreement is perhaps lower than expected is probably best explained by sand fly species being present in areas where no leishmaniasis was detected, because of limiting factors to transmission (such as climatic limits and distribution limits of reservoir species). However, due to insufficient surveillance of vectors, there were also areas with reported leishmaniasis but without known vector presence. Nevertheless, the study confirmed expected statistical relationships with most proven vectors (such as P. perniciosus, P. tobbi, P. ariasi, P. perfiliewi and P. kandelakii in the L. infantum model, P. papatasi in the L. major model and P. sergenti in L. tropica model) and large variability between vectors in their ability to predict Leishmania spp. distributions, suggesting possible differences in their efficiency to transmit these pathogens. There were also unexpected significant relationships between Leishmania spp. and vectors which had not been listed as suspected by Maroli and colleagues [10], including P. similis and P. papatasi for L. infantum, P. halepensis and P. major for L. donovani s.s., P. alexandri, P. halepensis, P. langeroni and P. perniciosus for L. major and P. alexandri and P. papatasi for L. tropica, which deserve further investigation.

Conclusions

Sand fly surveillance and reporting and diagnosis of leishmaniasis cases should be improved for a more precise understanding of Leishmania spp. and vector distributions. While some unexpected significant relationships between Leishmania spp. and vectors might be spurious, for others, the existence of sporadic or recent reports of infections in these vectors suggests that further vector competence studies (considering strain variation) and vector infection status studies are warranted for these vectors. As the unexpected relationships include vector species with restricted ability for parasite development, this study supports the notion expressed by Dostalova and Volf [26] that categorizing vector species as ‘permissive’ and ‘non-permissive’ is likely to be an oversimplification.

Availability of data and materials

The data supporting the conclusions of this investigation (vecleish_fin2_PV-xlsx) are provided as part of the article's supplementary materials. The data source and the European Centre for Disease Control (ECDC) should be acknowledged in the event of future use of these data. Data are provided as supplementary information files.

Notes

  1. We use the VectorNet geographical area, encompassing Europe (excluding Russia), northern Africa (excluding Sudan), (the Asian part of) Turkey, Israel, Syria, Jordan, Palestine (this designation shall not be construed as recognition of a State of Palestine and is without prejudice to the individual positions of the Member States on this issue), (the Asian part of) Georgia, Armenia, Azerbaijan, western parts of Kazakhstan, Uzbekistan and Turkmenistan.

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Acknowledgements

We thank William Wint for providing help with organizing the databases and Wim Van Bortel, Celine Gossner and Marieta Braks for critically reviewing this manuscript.

Funding

This article was commissioned by ECDC under framework contract ECDC/2019/020 “European Network for Medical and Veterinary Entomology (VectorNet).” The contents of this publication are the sole responsibility of ECDC and the authors. The CLIMOS Project (http://www.climos-project.eu), funded by European Commission grant 101057690 and UKRI grants 10038150 and 10039289, supported the collection and analysis of the data relative to the period between July 2020 and March 2023, and the manuscript is catalogued by the CLIMOS Scientific Committee as CLIMOS number 010. The contents of this publication do not necessarily reflect the views of the European Commission, the Health and Digital Executive Agency or UKRI. Neither the European Union nor the granting authority nor UKRI can be held responsible for them. Neither the European Commission nor the UKRI had roles in study design, data collection and analysis, decision to publish or preparation of the manuscript. For the purposes of Open Access, the authors have applied a CC BY (1) public copyright license to any Author Accepted Manuscript version arising from this submission. The six Horizon Europe projects, BlueAdapt, CATALYSE, CLIMOS, HIGH Horizons, IDAlert, and TRIGGER, form the Climate Change and Health Cluster.

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Authors and Affiliations

  1. Departamento de Sanidad Animal, Facultad de Veterinaria, Campus Regional de Excelencia Internacional “Campus Mare Nostrum”, Universidad de Murcia, Murcia, Spain

    Eduardo Berriatua

  2. Departamento de Geografía, Universidad de Murcia, Murcia, Spain

    Pedro Pérez-Cutillas

  3. Department of Information and Communications Engineering, Universidad de Murcia, Murcia, Spain

    Aurora González Vidal

  4. Disease Programmes Unit, European Centre for Disease Prevention and Control, Stockholm, Sweden

    Olivier J. T. Briët

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  1. Eduardo Berriatua
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Contributions

EB and OB contributed to the conception, data analysis and writing of the article, PPC contributed to acquisition and analysis of data, and elaborated the maps, and AGV wrote the code in R for some of the analysis of the data and interpreted the results. All authors revised and approved the submitted version, and agreed both to be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, are appropriately investigated, resolved, and the resolution documented in the literature.

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Supplementary Information

13071_2024_6484_MOESM1_ESM.zip

Supplementary Material 1. Fig. S1 Leishmania infantum distribution in Europe and neighboring countries. Fig. S2 Leishmania donovani sensu stricto distribution in Europe and neighboring countries. Fig. S3 Leishmania major distribution in Europe and neighboring countries. Fig. S4 Leishmania tropica distribution in Europe and neighboring countries. Fig. S5 Leishmania spp. distribution in Europe and neighboring countries. Fig. S6 Visceral leishmaniasis (VL) distribution in Europe and neighboring countries. Fig. S7 Cutaneous leishmaniasis (CL) distribution in Europe and neighboring countries. Fig. S8 Leishmania infantum and VL distribution in Europe and neighboring countries. Fig. S9 Leishmania spp., VL and CL distribution in Europe and neighboring countries. Fig. S10Phlebotomus alexandri distribution in Europe and neighboring countries. Fig. S11Phlebotomus ariasi distribution in Europe and neighboring countries. Fig. S12Phlebotomus balcanicus distribution in Europe and neighboring countries. Fig. S13Phlebotomus halepensis distribution in Europe and neighboring countries. Fig. S14Phlebotomus kandelakii distribution in Europe and neighboring countries. Fig. S15Phlebotomus langeroni distribution in Europe and neighboring countries. Fig. S16Phlebotomus mascittii distribution in Europe and neighboring countries. Fig. S17 Phlebotomus major sensu lato distribution in Europe and neighboring countries. Fig. S18Phlebotomus papatasi distribution in Europe and neighboring countries. Fig. S19Phlebotomus perfiliewi distribution in Europe and neighboring countries. Fig. S20Phlebotomus perniciosus distribution in Europe and neighboring countries. Fig. S21Phlebotomus sergenti distribution in Europe and neighboring countries. Fig. S22Phlebotomus similis distribution in Europe and neighboring countries. Fig. S23Phlebotomus tobbi distribution in Europe and neighboring countries. Fig. S24 Phlebotomus major sensu stricto distribution in Europe and neighboring countries. Fig. S25Phlebotomus neglectus distribution in Europe and neighboring countries.

Supplementary Material 2.

Supplementary Material 3.

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Berriatua, E., Pérez-Cutillas, P., Vidal, A.G. et al. The spatial relationship between leishmaniases and sand flies in Europe and neighboring countries. Parasites Vectors 17, 404 (2024). https://doi.org/10.1186/s13071-024-06484-2

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