https://orcid.org/0000-0001-7481-4355
Abstract
Background
Research on vector-borne diseases has traditionally centred on a limited number of vertebrate hosts and their associated pathogens, often neglecting the broader array of vectors within communities. Mosquitoes, with their vast species diversity, hold a central role in disease transmission, yet their capacity to transmit specific pathogens varies considerably among species. Quantitative modelling of mosquito-borne diseases is essential for understanding transmission dynamics and requires the necessity of incorporating the identity of vector species into these models. Consequently, understanding the role of different species of mosquitoes in modelling vector-borne diseases is crucial for comprehending pathogen amplification and spill-over into humans. This comprehensive overview highlights the importance of considering mosquito identity and emphasises the essential need for targeted research efforts to gain a complete understanding of vector-pathogen specificity.
Methods
Leveraging the recently published book, ‘Mosquitoes of the World’, I identified 19 target mosquito species in Europe, highlighting the diverse transmission patterns exhibited by different vector species and the presence of 135 medically important pathogens.
Results
The review delves into the complexities of vector-pathogen interactions, with a focus on specialist and generalist strategies. Furthermore, I discuss the importance of using appropriate diversity indices and the challenges associated with the identification of correct indices.
Conclusions
Given that the diversity and relative abundance of key species within a community significantly impact disease risk, comprehending the implications of mosquito diversity in pathogen transmission at a fine scale is crucial for advancing the management and surveillance of mosquito-borne diseases.
The emergence of infectious diseases is on the rise, with over 700,000 people dying annually due to pathogens transmitted by insect vectors [Citation1]. Among the most significant vector-borne diseases affecting human populations, malaria stands out as a common parasitic infection. This disease is caused by Plasmodium, a protozoa transmitted by anopheline mosquitoes, posing a significant risk in 2021 to nearly half of the world’s population [Citation2]. Additionally, other viral diseases, notably dengue, chikungunya, Zika, yellow fever and West Nile fever, transmitted by mosquitoes [Citation3,Citation4], are currently expanding their incidence and distribution in Europe [Citation5]. Despite this, traditional research on vector-borne diseases has predominantly focused on the implications in the vertebrate hosts, with the role of vectors themselves receiving comparatively less attention.
Mosquitoes (Culicidae), comprising 3,698 species, 187 subgenera, and 41 genera [Citation6,Citation7], are the primary vectors of numerous diseases with significant implications for human and wildlife health. However, it is noteworthy that less than 10% of mosquito species play a role in the transmission of pathogens that affect humans [Citation8]. This emphasises the critical importance of directing research efforts towards these specific target vector species (, [Citation6]). Furthermore, the characterisation of mosquito community composition presents inherent complexities owing to the sheer number of species involved, compounded by the challenges associated with species identification (sometimes requiring molecular methods) [Citation9]. Additionally, mosquitoes exhibit a wide array of diverse life history traits [Citation10], and the competence to transmit a particular parasite can vary not only between different species but even among individuals within the same population [Citation11]. Consequently, this underscores the imperative need for focused research to identify vector species, an essential step in comprehending the dynamics of disease transmission and establishing effective control and surveillance programs for mosquito-borne pathogens.
Table 1. List of the 19 key species of mosquito vectors of medically important pathogens in Europe following Wilkerson et al. [Citation6], including the total number of pathogens transmitted per species.
Through a comprehensive review of the book ‘Mosquitoes of the World’ [Citation6], which expands the previous catalogue of disease-transmitting mosquitoes based only on the Aedini tribe [Citation12], 19 target vector species have been identified in Europe, spanning 9 subgenera within the Aedes, Anopheles, and Culex genera. These mosquito species are globally acknowledged as significant vectors responsible for transmitting 135 different pathogens that affect human health [Citation6], encompassing bacteria, fungi, nematodes, protozoa, and viruses (). Aedes and Culex mosquitoes are responsible for transmitting 91 and 72 different pathogens, respectively, bridging all pathogen groups. Conversely, the Anopheles genus has been associated with the transmission of 13 different protozoa and viruses (). The considerable heterogeneity in transmission patterns revealed between mosquito species and pathogens () is noteworthy. Here, I observed no species-specific relationships, indicating that a single vector species did not exclusively transmit a specific pathogen. Nevertheless, among the Aedes genus, Ae. caspius (Pallas, 1771) was the only species able to transmit a specific type of fungus, i.e. Cristulospora aedis of the Amblyosporidae family (Table S1). It is important to note that focusing solely on single species transmitting a single pathogen might be misleading and not necessarily indicative of true specificity. It could be a consequence of insufficient data available for that particular species or the level of research conducted on it, especially in cases like fungal infection in mosquitoes. Therefore, drawing definitive conclusions about specificity would require more comprehensive studies and data analysis.
Figure 1. Alluvial plot showing mosquito and pathogen networks for mosquito species of medical importance in Europe following Wilkerson et al. [Citation6]. The flow lines represent the different relationships between mosquito species and the pathogens they transmit. In this plot, the left column represents vector species, while the right column represents pathogen identity, which are embedded in the flow of the plot. Different colours represent the mosquito genus where Aedes are shown in red, Anopheles in green, and Culex in blue. The width of the bands connecting the categories reflects the relative number of observations moving in the network.
Note: The pathogen species can be repeated in the right column, reflecting the fact that different vector species can transmit the same pathogen. For better visualisation, only labels of mosquitoes and pathogens with N > 3 items are shown.
![Figure 1. Alluvial plot showing mosquito and pathogen networks for mosquito species of medical importance in Europe following Wilkerson et al. [Citation6]. The flow lines represent the different relationships between mosquito species and the pathogens they transmit. In this plot, the left column represents vector species, while the right column represents pathogen identity, which are embedded in the flow of the plot. Different colours represent the mosquito genus where Aedes are shown in red, Anopheles in green, and Culex in blue. The width of the bands connecting the categories reflects the relative number of observations moving in the network.Note: The pathogen species can be repeated in the right column, reflecting the fact that different vector species can transmit the same pathogen. For better visualisation, only labels of mosquitoes and pathogens with N > 3 items are shown.](/cms/asset/aaf7e6f6-8bab-48da-abcc-2100f982c2cc/infd_a_2357624_f0001_c.jpg)
Table 2. List of the 135 pathogens transmitted by mosquitoes in Europe following Wilkerson et al. [Citation6].
Generally, Anopheles mosquitoes demonstrated specialisation in transmitting protozoan and viral pathogens, where An. multicolour (Cambouliu, 1902) and An. superpictus (Grassi, 1899) stood out as the most specialised species in their respective transmission patterns. They were identified as responsible for transmitting Plasmodium falciparum and Rift Valley fever virus (RVFV) and Plasmodium spp. and RVFV, respectively (Table S1). Conversely, the most generalist mosquito species was the invasive Ae. aegypti (Linnaeus, 1762), commonly known as the yellow fever mosquito, which transmitted up to 56 different pathogens [Citation6]. It was followed by Cx. quinquefasciatus (Say, 1823), vector of 41 pathogens, and Ae. albopictus (Skuse, 1895), capable of transmitting 25 pathogens. Cx. pipiens (Linnaeus, 1758) and Ae. vexans (Meigen, 1830), both of which transmitted 21 pathogens, were also among the most generalist vector species ().
From a pathogen perspective, the Flavivirus genus, which includes positive-strand RNA viruses like West Nile virus (WNV), dengue virus, tick-borne encephalitis virus, yellow fever virus, Zika virus, and other encephalitis-causing viruses [Citation13], was transmitted by the largest number of mosquitoes. It has been identified in 15 species, primarily Aedes (60%) and Culex (33.3%) mosquitoes, with only one Anopheles species, An. maculipennis (Meigen, 1830), carrying the virus. Among these pathogens, WNV, recognised as the most widely distributed mosquito-borne flavivirus [Citation14,Citation15], was the most generalist virus found (). Culex mosquitoes play a significant role in transmitting WNV to humans [Citation15,Citation16], even though it has been found in 65 mosquito species (with 14 confirmed in this review, , and Table S1). While certain mosquito species like Ae. caspius have been detected with WNV infections in natural settings, their competence as vectors remains a subject of debate [Citation17] and is generally considered lower than that of Culex mosquitoes. Particularly, ornithophilic species like Cx. modestus (Ficalbi, 1890), Cx. perexiguus (Theobald, 1903), and Cx. pipiens regarded as the primary WNV vectors in Europe [Citation18,Citation19].
Mosquitoes, with their vast diversity of species, play a central role in disease transmission, but the ability to transmit specific pathogens varies widely between species. Throughout this review, I will highlight the importance of considering the identity of mosquitoes on their capacity to transmit a wide range of pathogens, emphasising the need for targeted research efforts and comprehensive studies to fully grasp the complexities of vector-pathogen specificity.
Unravelling mosquito-pathogen specificity: the complex interplay between specialist versus generalist strategies
The range of pathogens that a mosquito species is able to transmit is the product of complex co-evolutionary processes. As a result, the involvement of the mosquito species in filtering this association is evident, as some pathogens can infect a single species, while others show a more generalist pattern (, Table S1) [Citation20]. However, understanding the evolution of pathogen specialisation at the invertebrate vector levels, as well as the nature of different strategies, remains challenging and under intense debate.
On top of the interface between mosquitoes and pathogens, there are two main potential strategies, i.e. specialist vs. generalist. To date, while transmission mechanisms between vertebrate hosts and pathogens have been extensively studied, interactions in the case of vectors, especially those affecting wildlife, have received less attention. Based on mosquito list provided by Wilkerson et al. [Citation6], and further corroborated with Wilkerson et al. [Citation12], I observed a remarkable heterogeneity among mosquito species and the pathogens they transmit. Most mosquito species were found to be vectors for an average of 13 distinct pathogens (range 2 – 56, Table S1), with only a single specialist relationship (). However, this potential specialist relationship was unidirectional (pathogen-to-vector), as the same mosquito species (i.e. Ae. caspius), was also responsible for transmitting at least two different bacteria (i.e. Spiroplasma sabaudiense, Francisella tularensis) and five different viruses (i.e. ISFV, RFRV, IKV, TAHV, and WNV; see Table S1 for the complete pathogen name and acronym correspondences).
Parasite specialisation may limit further evolution as pathogen transmitted by a single vector species experience selective pressure to become highly efficient in replication [Citation21,Citation22]. However, over time, the pathogen can evolve to be transmitted by multiple vectors [Citation23], with great implication in its transmission patterns. Indeed, generalist pathogens possess the advantage of being transmitted by a wide range of insect vectors, increasing their transmission success. Human Plasmodium parasites are considered highly generalist parasites that can be transmitted by a variety of mosquito species, but for a limited number of genera. An excellent example of a multi-vector pathogen is the human malarial protozoa, which consists of different strains of the parasite species (such as P. falciparum) that vary in their ability to infect Anopheles vectors and vertebrate hosts. There are currently 465 formally recognised anopheline species and more than 50 species complexes worldwide [Citation24], of which almost 70 species are capable of transmitting human malaria parasites. Among them, 41 species or species complexes are considered to be epidemiologically important vectors at a level of major concern to public health [Citation25,Citation26]. One well-known example is the An. gambiae complex, which was initially considered as a single species. However, clear differences across its distribution prompted further examination, and it is now considered to include up to eight species [Citation24,Citation27] including, among other, An. gambiae (Giles, 1902), An. melas (Theobald, 1903), An. merus (Dönitz, 1902), and An. arabiensis (Patton 1905). Only the presence and propensity to rest outdoors of the latter has been attributed to the failure of the massive indoor residual spraying program to control malaria in the Garki project in Nigeria [Citation28].
Despite its crucial role in animal and human epidemiology, our current understanding of the specificity between vectors and pathogens is limited. A notable deficiency exists in comprehending interactions between a single vector and multiple pathogens simultaneously, as comprehensive studies exploring such interactions are lacking on both fronts. The existing literature often relies on a major assumption that many vectors have been screened for individual pathogens, resulting in pathogen-specific research that rarely encompasses tests for various pathogens, such as avian malaria studies that hardly include virus screens, and vice versa [Citation16]. This limited perspective hinders our ability to fully grasp the complexity of vector-pathogen interactions and their implications for disease transmission and control. To bridge this knowledge gap, future studies should aim to investigate the interactions between vectors and diverse pathogens simultaneously, employing advanced molecular tools and sequencing techniques to gain comprehensive insights into these complex relationships (). Therefore, identifying the competent vectors of pathogens and understanding the mechanisms that determine infection patterns are crucial for the development of effective control strategies, an aspect that is particularly important in the case of pathogens with public health significance [Citation29,Citation30].
Exploring the mathematical aspects of mosquito-borne disease transmission: from the historical context of Ronald Ross’s studies to current epidemiological models
Progress in understanding and fighting mosquito-borne diseases requires quantitative modelling that consider pathogen life history and incorporate information on the different vector species involved. Professor Ronald Ross was a pioneer in the study of mosquito-borne disease transmission and infectious diseases [Citation31]. His early work on malaria in the early 1900s involved mathematical models detailing mosquito movement and control measures [Citation32]. This represent one of the first time that quantitative predictions about the qualitative behaviour of malaria epidemics was made, considering the mosquito population density required for transmission. Despite his groundbreaking conceptual advances, Ross recognised the need for a comprehensive quantitative theory to measure transmission [Citation33], but failed to develop useful metrics to measure the important components of mosquito-borne transmission. This was the first malaria model and was further analysed by Waite (1910) [Citation34], Lotka (1923) [Citation35], and Fine (1975) [Citation36]. Unfortunately, all these modelling approaches have a significant limitation in that they assume only one mosquito vector species, which does not reflect the reality of pathogen transmission involving multiple species. Nowadays, this remains a prevalent issue, although there is a growing recognition of vectors as one of the primary drivers of pathogen transmission [Citation37,Citation38]. In the past, researchers have tried to introduce more complexity into the classic vector-borne models by incorporating multiple pathogens [Citation39,Citation40] or multiple hosts [Citation41], but relatively few models have incorporated any information on the species of vectors. Thus, the lack of a method that focuses on considering the identity of the different vectors involved in the transmission of a particular pathogen in the area, hinders the accuracy and generalisability of those models to multi-vector pathogens.
While mathematical models offer a rigorous and quantifiable method for encoding, refining, and conveying the quantitative logic behind mosquito-borne pathogen transmission, there is still considerable progress to be made. Despite the recent proliferation of modelling techniques, the Ross-Macdonald framework continues to exert a significant influence, with many of its assumptions persisting in most of mathematical models for mosquito-transmitted pathogens. One of the earliest works to investigate mosquito species composition in the context of malaria risk estimation was conducted by Davey and Gordon [Citation42], who compared transmission metrics of infective anophelines. This empirical study represents one of the first attempts to compare transmission metrics, especially in cases where different species of mosquitoes were involved. Additionally, Roche and colleagues [Citation38], developed a multispecies Susceptible-Infectious-Recovered transmission model for WNV that included both vertebrate and vector species richness (i.e. number of different species). This is one of the few models available today that considers the mosquito species in a mathematical framework. However, this approach was built on the biased assumption that the most abundant mosquitoes were also the most susceptible (i.e. likely to become infected) for pathogen transmission. Consequently, it may not fully account for the complexity of vector-borne disease transmission in natural ecosystems, where coexist different species of susceptible and non-susceptible mosquitoes within species-rich communities [Citation43].
In a more recent study, Hoi and colleagues [Citation44] focused on changes in human malaria concerning mosquito diversity defined as the number of distinct species present and their relative proportions, reflecting the richness and evenness of species in the area. They examined contributions from different mosquito species to understand the effects of vector abundance, species richness, and community composition on malaria prevalence. The findings revealed positive associations between mosquito abundance, species richness, and malaria infections, where the presence of specific Anopheles species, significantly influenced parasite prevalence, particularly when occurring in combination at high mosquito species richness [Citation44]. Indeed, the composition of vector species can significantly impact disease dynamics by affecting the pathogen transmission rates. The presence of a higher number of competent mosquito species, i.e. able to efficiently transmit a pathogen, each with unique characteristics and requirements (e.g. environmental and feeding preferences, aggregation behaviour), can broaden the overall pathogen host range, providing additional colonisation opportunities for the pathogens [Citation38]. However, the opposite patter has been also found, where a higher number of vector species in the communities can also reduce the risk of disease amplification and spread [Citation45].
Overall, quantitative modelling can be very insightful in identifying parameters of greatest importance where theoretical and empirical works on mosquito diversity have provided support for some predictions derived from general ecological theory. By integrating life-history traits of vectors and hosts, such as insect feeding preferences or bird birth rates and mean longevity, with an understanding of pathogen ecology (e.g. transmission rate or the mean duration of infection) within and between these communities, and considering how habitat characteristics may influence these interactions [Citation46], we can enhance our surveillance strategies. This was demonstrated in the case of WNV [Citation47]. In a recent epidemiological model based on the three main vectors of WNV, Cx. pipiens (pipiens form), Cx. pip. (molestus form), and their hybrids, and two vertebrate hosts, birds (as amplifying hosts) and humans (as dead-end hosts), I described how mosquito feeding preferences and the transmission rate between mosquitoes and birds were the parameters that most influenced WNV invasion risk, particularly in natural habitats [Citation47]. Additionally, contrary to common opinion, hybrid mosquitoes showed minimal involvement in WNV transmission, underscoring again the importance of considering the identity of vectors in modelling infectious diseases.
These findings underscore the need for a comprehensive understanding of vector species implication to effectively control and manage mosquito-borne diseases. However, these approaches should also consider the factors affecting the interactions between vectors and pathogens, such as insect feeding preferences [Citation47]. Forecasting mosquito-borne disease hazards under a global change scenario is particularly challenging due to the high number of factors involved in pathogen transmission and the complexity of their transmission cycles. Considering this, contemporary research by Cleveland and colleagues [Citation48] demonstrates that changes in temperature, precipitation, land use, and biodiversity can alter the composition and abundance of mosquito communities, leading to modifications in the transmission patterns of infectious diseases. The authors highlighted the need for proactive and adaptive approaches to disease management that account for changing insect communities, as the identity of mosquito species and their relative abundance in a given community can significantly impact disease risk [Citation48].
Exploring diversity metrics: importance, challenges, and considerations for understanding mosquito-borne disease transmission
Accurately characterising the composition of mosquito species in the study area represent a key step in contemporary studies which consider vector diversity metrics. This requires the use of appropriate diversity indices to quantify the extent of vector species diversity within a given biological community. Nowadays, several widely recognised ecological diversity indices find application in studies focused on mosquito communities, including:
Species richness, defined as the count of different species in a community, does not take the abundance of the species into account, such as used by Roche et al. [Citation38] or in Hoy et al. [Citation44].
Simpson’s index, which measures the probability of randomly selecting two individuals from the same species. This measure gives more weight to abundant species and addition of rare species causes only limited changes in the diversity index, as used in Zahouli et al. [Citation49].
Shannon-Wiener index, which considers how many species there are (species richness) and additionally accounts for the evenness of the distribution of individuals over species (abundance), as used in Chaves et al. [Citation45] or in Zahouli et al. [Citation49].
Evenness index, which examines the even distribution of individuals among different species, as used in Ferraguti et al. [Citation43].
These indices serve varying purposes and their suitability may depend on the research question and data nature. However, researchers often rely solely on one metric, like species richness, as a proxy for diversity index, assuming the correlation between these variables [Citation50–53]. Unfortunately, this approach can lead to biased results (see [Citation43] for a case study), as sampling bias can impact richness estimation significantly. This limitation could be partially solved by the use of contemporary rarefaction approaches, which account for variations in the number of individuals and samples collected [Citation54]. Additionally, the relationship between species richness/diversity and pathogen transmission may be linked to the presence and relative abundance of a key species within the community [Citation51]. For example, the impact of mosquito diversity on WNV transmission risk, in the absence of changes in host abundance, depends on the order in which insect species are introduced to the system. It also relies on the covariation between their rarity in the local distribution and their vectorial competence [Citation38]. Thus, using the appropriate diversity index depending on the research objective ensures considering the relative proportions of different species in the area [Citation55], while promoting the use of multiple indices in combination offers a more comprehensive picture of diversity in an ecosystem.
In the context of disease dynamics, considering functional diversity alongside taxonomic diversity is also relevant. Species identity may play a crucial role, as differences in mosquito behaviour and life history, as well as transmission mechanisms linked to vector taxonomy, can significantly affect the spread of the disease [Citation56]. For instance, variations in the seasonal phenology of mosquito emergence from overwintering stages can influence pathogen dynamics differently among mosquito species. Insect univoltine species with a single generation per year may affect pathogen spread differently than multivoltine species. In the case of species responsible for WNV transmission, both univoltine and multivoltine mosquitoes are involved, each playing critical roles in the spread of this disease [Citation57]. In summary, accurately characterising vector diversity, especially in the case of mosquito community studies, is essential for understanding disease transmission dynamics, and it necessitates the judicious selection of diversity indices aligned with research objectives.
Concluding remarks
The complexity of mosquito community composition, characterised by a vast number of species, presents inherent challenges. By investigating the efficiency with which various mosquito species can transmit different pathogens, researchers can gain valuable insights into the vector-borne transmission dynamics. While considerable attention has been given to modelling pathogens with simpler life cycles, less emphasis has been placed on multi-vector transmitted infections. In terms of disease ecology, there is a need for more studies focused on characterising the identity of specific vector species, which is crucial for understanding disease transmission dynamics and establishing effective control and surveillance programs for mosquito-borne pathogens. Mathematical modelling approaches provide essential tools for understanding the transmission dynamics of these pathogens, enabling the comparison and evaluation of prevention and control measures, a task that would be unfeasible with data-driven models alone [Citation58]. Integrating empirical and theoretical approaches is therefore essential in multidisciplinary research, emphasising the necessity for clear communication among researchers with diverse expertise and the importance of iterative feedback loops between modelling and fieldwork. Restif and colleagues [Citation59] provide practical guidelines for conducting multidisciplinary research on wildlife ecological and epidemiological dynamics, emphasising the essential aspect of integrating field and modelling approaches. Ultimately, interdisciplinary collaboration and innovative approaches play a pivotal role in advancing our understanding of the complex dynamics of mosquito-borne diseases and in developing effective interventions and policies. By bridging the gaps between different fields of study, researchers can combine their expertise and perspectives to tackle these intricate issues more comprehensively.
Acknowledgments
I would like to thank Josué Martínez-de la Puente and Jordi Figuerola for the enriching discussions and insights in to the world of mosquitoes, which have greatly enhanced this review. Thanks to Francisco J. Oficialdegui for his inspiration and guidance in creating the figures. Finally, thanks to Yael Artzy-Randrup for helping me to develop as an independent researcher during my time at the University of Amsterdam.
Author contributions
MF conceived the idea of the review, extracted the data, drew the figures and wrote the manuscript.
Ethics
This work involved no human subjects.
Supplemental Material
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Data accessibility statement
Data supporting the conclusions of this review are available in the tables and Supporting Information of this article.
Additional information
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References
- World Health Organization. 2020. Vector-borne diseases[Online]. Available: https://www.who.int/news-room/fact-sheets/detail/vector-borne-diseases. [Accessed: 11-Aug-2022]
- World Health Organization. 2023. Malaria. Fact sheets.
- Tolle MA. Mosquito-borne diseases. Curr Probl Pediatr Adolesc Health Care. 2009;39(4):97–140. doi: 10.1016/j.cppeds.2009.01.001.
- WHO, W.H.O. and UNICEF. 2017. Global vector control response 2017-2030
- Bakonyi T, Haussig JM. West Nile virus keeps on moving up in Europe. Eurosurveillance. 2020;25(46):2001938. doi: 10.2807/1560-7917.ES.2020.25.46.2001938.
- Wilkerson RC, Linton YM, Strickman D. Mosquitoes of the world. Baltimore: Johns Hopkins University Press; 2021.
- Knols BGJ. Review of “Mosquitoes of the World. by Richard C. Wilkerson, Yvonne-Marie Linton, and Daniel Strickman. Parasit vectors. 2021;14: 341.
- Yee DA, Yee S. Robust network stability of mosquitoes and human pathogens of medical importance. Parasit Vectors. 2022;15:216.
- Hebert PDN, Cywinska A, Ball SL, et al. Biological identifications through DNA barcodes. Proc Royal Society B: Biol Sci. 2003:270;313–321.
- Ruzzante L, Reijnders MJ, Waterhouse RM. Of genes and genomes: mosquito evolution and diversity. Trends Parasitol. 2019;35:32–51.
- Lambrechts L, Halbert J, Durand P, et al. Host genotype by parasite genotype interactions underlying the resistance of anopheline mosquitoes to Plasmodium falciparum. Malar J. 2005;4(1):3. doi: 10.1186/1475-2875-4-3.
- Wilkerson RC, Linton Y-M, Fonseca DM, et al. Making mosquito taxonomy useful: a stable classification of tribe aedini that balances utility with current knowledge of evolutionary relationships. PLoS One. 2015;10(7):e0133602. doi: 10.1371/journal.pone.0133602.
- Pei-Yong S. Molecular virology and control of flaviviruses. Singapore: Caister Academic Press; 2012.
- Weissenböck H, Hubálek Z, Bakonyi T, et al. Zoonotic mosquito-borne flaviviruses: worldwide presence of agents with proven pathogenicity and potential candidates of future emerging diseases. Vet Microbiol. 2010;140:271–280.
- Engler O, Savini G, Papa A, et al. European surveillance for West Nile virus in mosquito populations. Int J Environ Res Public Health. 2013;10:4869–4895.
- Ferraguti M, Martínez-de la Puente J, Figuerola J, et al. Ecological effects on the dynamics of West Nile virus and avian plasmodium: the importance of mosquito communities and landscape. Viruses. 2021;13(7):1208. doi: 10.3390/v13071208.
- Ergunay K, Gunay F, Oter K, et al. Arboviral surveillance of field-collected mosquitoes reveals circulation of West Nile virus lineage 1 strains in Eastern thrace, Turkey. Vector Borne Zoonotic Dis. 2013;13(10):744–752. doi: 10.1089/vbz.2012.1288.
- Balenghien T, Vazeille M, Grandadam M, et al. Vector competence of some French culex and aedes mosquitoes for West Nile virus. Vector Borne Zoonotic Dis. 2008;8(5):589–595. doi: 10.1089/vbz.2007.0266.
- Figuerola J, Jiménez-Clavero MÁ, Ruíz-López MJ, et al. A one health view of the West Nile virus outbreak in Andalusia (Spain) in 2020. Emerg Microbes Infect. 2022;11(1):2570–2578. doi: 10.1080/22221751.2022.2134055.
- Poisot T, Kéfi S, Morand S, et al. A continuum of specialists and generalists in empirical communities. PLoS One. 2015;10(5):e0114674. doi: 10.1371/journal.pone.0114674.
- Kelley S, Farrell B. Is specialization a dead end? The phylogeny of host use in dendroctonus bark beetles (scolytidae). Evolution 1998;52(6):1731–1743. doi: 10.2307/2411346.
- Nosil P. Transition rates between specialization and generalization in phytophagous insects. Evolution. 2002;56:1701–1706. doi: 10.1111/j.0014-3820.2002.tb01482.x.
- Powell JR. An evolutionary perspective on vector-borne diseases. Front Genet. 2019;10:1266. doi: 10.3389/fgene.2019.01266.
- Harbach R. 2020. Genus Anopheles Meigen, 1818. Mosquito Taxonomic Inventory. [Online]. Available: http://mosquito-taxonomic-inventory.info/genus-emanophelesem-meigen-1818-0. [Accessed: 11-Aug-2021]
- Service M, Townson H. The Anopheles vector. In: Gilles H, Warrell D, editors. Essential Malariology. 4th ed. London: CRC Press; 2017. pp. 59–84.
- Sinka ME, Bangs MJ, Manguin S, et al. A global map of dominant malaria vectors. Parasit Vectors. 2012;5:69.
- Harbach R. The classification of genus anopheles (diptera: culicidae): a working hypothesis of phylogenetic relationships. Bull Entomol Res. 2004;94(6):537–553. doi: 10.1079/ber2004321.
- Collins FH, Paskewitz SM. Malaria: current and future prospects for control. Annu Rev Entomol. 1995;40(1):195–219. doi: 10.1146/annurev.en.40.010195.001211.
- Gutiérrez-López R, Martínez-de la Puente J, Gangoso L, et al. Do mosquitoes transmit the avian malaria-like parasite haemoproteus? An experimental test of vector competence using mosquito saliva. Parasit Vectors. 2016;9:1–7.
- Kilpatrick AM, Randolph SE. Drivers, dynamics, and control of emerging vector-borne zoonotic diseases. The Lancet. 2012;380(9857):1946–1955. doi: 10.1016/S0140-6736(12)61151-9.
- Ross R. Some quantitative studies in epidemiology. Nature. 1911;87(2188):466–467. doi: 10.1038/087466a0.
- Ross R. The logical basis of the sanitary policy of mosquito reduction. Science. 1905;22(570):689–699. doi: 10.1126/science.22.570.689.
- Ross R. The prevention of malaria. London: John Murray; 1910.
- Waite H. Mosquitoes and malaria. A study of the relation between the number of mosquitoes in a locality and the malaria rate. Biometrika. 1910;7(4):421–436. doi: 10.2307/2345376.
- Lotka AJ. Contributions to the analysis of malaria epidemiology. II. General part (continued). Comparison of two formulae given by sir ronald ross. Am J Hyg. 1923;3(supp1):38–54. doi: 10.1093/oxfordjournals.aje.a118965.
- Fine PEM. Tropical disease - A challenge for epidemiology: ross’s a priori pathometry - A perspective. J R Soc Med. 1975;68:547–551.
- Power AG, Flecker AS. The role of vector diversity in disease dynamics. Infect Dis Ecol. 2011;:534–545.
- Roche B, Rohani P, Dobson AP, et al. The impact of community organization on vector-borne pathogens. Am Nat. 2013;181:1–11.
- Roberts M, Dobson A. The population dynamics of communities of parasitic helminths. Math Biosci. 1995;126(2):191–214. doi: 10.1016/0025-5564(94)00036-y.
- Dobson A, Roberts M. The population dynamics of parasitic helminth communities. Parasitology. 1994;109 Suppl(S1):S97–S108. doi: 10.1017/s0031182000085115.
- Dobson A, Foufopoulos J. Emerging infectious pathogens of wildlife. Philosophical Transactions of the Royal Society B: Biological Sciences. 2001;356:1001–1012.
- Davey TH, Gordon RM. The estimation of the density of infective anophelines as a method of calculating the relative risk of inoculation with malaria from different species or in different localities. Ann Trop Med Parasitol. 1933;27:27–52.
- Ferraguti M, Martínez-de la Puente J, Jiménez-Clavero MÁ, et al. A field test of the dilution effect hypothesis in four avian multi-host pathogens. PLoS Pathog. 2021;17(6):e1009637. doi: 10.1371/journal.ppat.1009637.
- Hoi A, et al. Deconstructing the impact of malaria vector diversity on disease risk. American Naturalist. 2020;196: e 61–E70.
- Chaves LF, Hamer GL, Walker ED, et al. Climatic variability and landscape heterogeneity impact urban mosquito diversity and vector abundance and infection. Ecosphere. 2011;2(6):art70. doi: 10.1890/ES11-00088.1.
- Ferraguti M, Magallanes S, Suarez-Rubio M, et al. Does land-use and land cover affect vector-borne diseases? A systematic review and meta-analysis. Landsc Ecol. 2023;38(10):2433–2451. doi: 10.1007/s10980-023-01746-3.
- Ferraguti M, Dimas Martins A, Artzy-Randrup Y. Quantifying the invasion risk of West Nile virus: insights from a multi-vector and multi-host SEIR model. One Health. 2023;17:100638. doi: 10.1016/j.onehlt.2023.100638.
- Cleveland CA, Dallas TA, Vigil S, et al. Vector communities under global change may exacerbate and redistribute infectious disease risk. Parasitol Res. 2023;122(4):963–972. doi: 10.1007/s00436-023-07799-2.
- Zahouli JBZ, et al. Urbanization is a main driver for the larval ecology of aedes mosquitoes in arbovirus-endemic settings in South-Eastern côte d’Ivoire. PLoS Negl. Trop. Dis. 2017;11:e0005751.
- Ostfeld R, Keesing F. The function of biodiversity in the ecology of vector-borne zoonotic diseases. Can J Zool. 2000;78:2061–2078.
- LoGiudice K, Duerr STK, Newhouse MJ, et al. Impact of host community composition on lyme disease risk. Ecology. 2008;89(10):2841–2849. doi: 10.1890/07-1047.1.
- Johnson P, Thieltges D. Diversity, decoys and the dilution effect: how ecological communities affect disease risk. J Exp Biol. 2010;213(6):961–970. doi: 10.1242/jeb.037721.
- Civitello DJ, et al. Biodiversity inhibits parasites: broad evidence for the dilution effect. PNAS. 2015;112:8667–8671.
- Gotelli NJ, Colwell RK. Estimating species richness. In: Biological diversity: frontiers in measurement and assessment. Oxford: Oxford University Press; 2011. pp. 39–54.
- Agrawal A, Gopal K. Application of diversity index in measurement of species diversity. In Biomonitoring of water and waste water. New Delhi: Springer India; 2013. pp. 41–48.
- Gray S, Banerjee N. Mechanisms of arthropod transmission of plant and animal viruses. Microbiol Mol Biol Rev. 1999;63(1):128–148. doi: 10.1128/MMBR.63.1.128-148.1999.
- Crans W. A classification system for mosquito life cycles: life cycle types for mosquitoes of the northeastern United States. J Vector Ecol. 2004;29(1):1–10.
- Huppert A, Katriel G. Mathematical modelling and prediction in infectious disease epidemiology. Clin Microbiol Infect. 2013;19(11):999–1005. doi: 10.1111/1469-0691.12308.
- Restif O, et al. Model-guided fieldwork: practical guidelines for multidisciplinary research on wildlife ecological and epidemiological dynamics. Ecol Lett. 2012;15:1083–1094.