FREE SHIPPING over $40
OVER 10000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
BMC veterinary research2026; 22(1); 407; doi: 10.1186/s12917-026-05298-x

Risk assessment of West Nile fever in equines in Europe based on an ecological niche model.

Abstract: West Nile fever is a zoonotic viral disease transmitted by Culex mosquitoes, capable of causing severe neurological illness in humans, equines, and birds. Over the past dozen years, Europe has experienced numerous WNF outbreaks of varying severity, leading to significant public health and economic impacts. Consequently, WNF has emerged as a major concern for both public health and veterinary sectors. In this study, we employed spatiotemporal analysis techniques alongside an optimized ecological niche model to investigate the spatiotemporal distribution patterns of historical WNF outbreaks, predict high-risk areas for equine WNF occurrence in Europe, and identify associated risk factors. The spatiotemporal analysis revealed a distinct seasonal pattern, with most outbreaks occurring in September. Between 2010 and 2023, the overall spread direction of equine WNF outbreaks in Europe trended from southeast to northwest, forming seven statistically significant spatiotemporal clusters. Ecological niche modeling indicated that southern Portugal, southern and eastern coastal regions of Spain, southwestern and southeastern regions of France, most parts of Italy, northeastern Germany, western Poland, southwestern coastal regions of Croatia, southwestern coastal regions of Bosnia and Herzegovina, southwestern coastal regions of Montenegro, western coastal regions of Albania, most parts of Greece, southwestern regions of Bulgaria, and Tekirdağ Province in Turkey are high-risk areas for WNF occurrence in equines in Europe. Isothermality, mean temperature of the warmest quarter, mean precipitation of the warmest quarter, elevation, and NDVI are important variables affecting WNF occurrence in equine. This study provides valuable insights to support targeted West Nile virus surveillance and optimized resource allocation across Europe.
© 2026. The Author(s).
Get Full Text
Publication Date: 2026-05-18 PubMed ID: 42151951PubMed Central: PMC13348124DOI: 10.1186/s12917-026-05298-xGoogle Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Risk assessment of West Nile fever in equines in Europe was conducted using an ecological niche model combined with spatiotemporal analyses to identify high-risk areas and key environmental factors influencing disease occurrence.

Introduction to West Nile Fever (WNF)

  • WNF is a zoonotic viral disease transmitted primarily by Culex mosquitoes.
  • It causes severe neurological illness in humans, horses (equines), and birds.
  • Europe has faced multiple WNF outbreaks over the last 12 years, causing public health concerns and economic losses.
  • WNF presents a significant challenge to both public health and veterinary sectors in Europe.

Objectives of the Study

  • To analyze the spatiotemporal distribution patterns of historical WNF outbreaks in equines throughout Europe.
  • To predict high-risk geographical areas for equine WNF occurrence using an ecological niche model.
  • To identify environmental and climatic risk factors associated with the presence of WNF in horses.

Methodology

  • Spatiotemporal analysis techniques examined data from documented equine WNF outbreaks between 2010 and 2023.
  • The direction and timing of outbreaks were studied to detect seasonal trends and clustering patterns.
  • An optimized ecological niche model was used integrating environmental variables such as temperature, precipitation, elevation, and vegetation index (NDVI).

Key Findings: Spatiotemporal Patterns

  • Outbreaks showed a clear seasonal peak, mostly in the month of September.
  • From 2010 to 2023, outbreak spread generally moved from southeast to northwest Europe.
  • Seven significant spatiotemporal clusters of equine WNF cases were identified, suggesting focal points of transmission.

Key Findings: Ecological Niche Modelling and High-Risk Areas

  • The model predicted high-risk zones for equine WNF occurrence including:
    • Southern Portugal
    • Southern and eastern coastal Spain
    • Southwestern and southeastern France
    • Most regions in Italy
    • Northeastern Germany
    • Western Poland
    • Southwestern coastal areas of Croatia, Bosnia and Herzegovina, Montenegro, and Albania
    • Most parts of Greece
    • Southwestern Bulgaria
    • Tekirdağ Province in Turkey
  • The identified risk areas correspond to ecological and climatic conditions favorable for the WNF virus transmission cycle.

Important Environmental and Climatic Risk Factors

  • Isothermality: Stability of temperature variations plays a critical role in mosquito and virus survival.
  • Mean temperature of the warmest quarter: Higher temperatures during summer months enhance mosquito activity and viral replication.
  • Mean precipitation of the warmest quarter: Rainfall affects mosquito breeding sites and population density.
  • Elevation: Variations impact local climate and habitat suitability for vector species.
  • Normalized Difference Vegetation Index (NDVI): Vegetation density influences mosquito habitats and bird reservoir presence.

Implications and Applications

  • The study offers detailed risk maps and seasonal insights that can optimize surveillance of WNF in equines.
  • Public health and veterinary authorities can use this information for targeted intervention strategies.
  • Resource allocation for prevention and control efforts can be more effectively planned based on identified risk areas and periods.
  • Enhanced understanding of environmental drivers assists in predicting and mitigating future outbreaks amid changing climate and landscape patterns.

Conclusion

  • This integrated spatiotemporal and ecological niche modeling approach provides valuable knowledge about WNF dynamics in equines across Europe.
  • It highlights critical regions and environmental conditions prone to outbreaks, supporting proactive disease management and surveillance systems.

Cite This Article

APA
Wang Y, An Q, Liu Y, Yang T, Sun Z. (2026). Risk assessment of West Nile fever in equines in Europe based on an ecological niche model. BMC Vet Res, 22(1), 407. https://doi.org/10.1186/s12917-026-05298-x

Publication

BMC veterinary research
ISSN: 1746-6148
NlmUniqueID: 101249759
Country: England
Language: English
Volume: 22
Issue: 1
PII: 407

Researcher Affiliations

Wang, Yue
  • College of Animal Science and Technology, Henan University of Science and Technology, Luoyang, People's Republic of China. wangyue629@haust.edu.cn.
  • College of Veterinary Medicine, Northeast Agricultural University, Harbin, People's Republic of China. wangyue629@haust.edu.cn.
An, Qi
  • College of Veterinary Medicine, Northeast Agricultural University, Harbin, People's Republic of China.
  • Key Laboratory of the Provincial Education Department of Heilongjiang for Common Animal Disease Prevention and Treatment, College of Veterinary Medicine, Northeast Agricultural University, Harbin, People's Republic of China.
Liu, Yifei
  • College of Animal Science and Technology, Henan University of Science and Technology, Luoyang, People's Republic of China.
Yang, Ting
  • College of Animal Science and Technology, Henan University of Science and Technology, Luoyang, People's Republic of China.
Sun, Zhuo
  • College of Veterinary Medicine, Northeast Agricultural University, Harbin, People's Republic of China.
  • Key Laboratory of the Provincial Education Department of Heilongjiang for Common Animal Disease Prevention and Treatment, College of Veterinary Medicine, Northeast Agricultural University, Harbin, People's Republic of China.

MeSH Terms

  • Animals
  • West Nile Fever / veterinary
  • West Nile Fever / epidemiology
  • Horses
  • Horse Diseases / epidemiology
  • Horse Diseases / virology
  • Europe / epidemiology
  • Risk Assessment
  • Disease Outbreaks / veterinary
  • Ecosystem
  • Seasons
  • Spatio-Temporal Analysis
  • Risk Factors
  • Mosquito-Borne Diseases

Conflict of Interest Statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

References

This article includes 70 references
  1. Pattan SR, Dighe NS, Bhawar SB, Gaware VM, Hole MB, Musmade DS, Bhosale MS. West nile fever: an overview.. J Biomed Sci Res 2009;1(1):33–48.
  2. Woah. Chapter 8.20. West Nile Fever. Terrestrial Animal Health Code. 2022.
  3. Autorino GL, Battisti A, Deubel V, Ferrari G, Forletta R, Giovannini A, Lelli R, Murri S, Scicluna MT. West nile virus epidemic in horses, Tuscany region, Italy.. Emerg Infect Dis 2002;8(12):1372.
    doi: 10.3201/eid0812.020234pmc: PMC2738505pubmed: 12498650google scholar: lookup
  4. Dauphin G, Zientara S, Zeller H, Murgue B. West nile: worldwide current situation in animals and humans.. Comp Immunol Microbiol Infect Dis 2004;27(5):343–55.
    doi: 10.1016/j.cimid.2004.03.009pubmed: 15225984google scholar: lookup
  5. Woah. Chapter 3.1.25. West Nile Fever. Woah Terrestrial Manual. 2018.
  6. Komar N, Langevin S, Hinten S, Nemeth N, Edwards E, Hettler D, Davis B, Bowen R, Bunning M. Experimental infection of North American birds with the new York 1999 strain of West nile virus.. Emerg Infect Dis 2003;9(3):311.
    doi: 10.3201/eid0903.020628pmc: PMC2958552pubmed: 12643825google scholar: lookup
  7. Leblond A, Hendrikx P, Sabatier P. West nile virus outbreak detection using syndromic monitoring in horses.. Vector-Borne Zoonotic Dis 2007;7(3):403–10.
    doi: 10.1089/vbz.2006.0593pubmed: 17767410google scholar: lookup
  8. Selim A, Radwan A, Arnaout F, Khater H. The recent update of the situation of West nile fever among equids in Egypt after three decades of missing information.. Pakistan Veterinary J 2020;40(3).
  9. Sellon DC, Long MT. Equine infectious diseases E-Book: equine infectious diseases E-Book.. Elsevier Health Sciences 2013.
  10. García-Bocanegra I, Jaen-Tellez JA, Napp S, Arenas-Montes A, Fernandez-Morente M, Fernandez-Molera V, Arenas A. West nile fever outbreak in horses and humans, Spain, 2010.. Emerg Infect Dis 2011;17(12):2397.
    doi: 10.3201/eid1712.110651pmc: PMC3311180pubmed: 22172565google scholar: lookup
  11. McLEAN RG, Ubico SR, Docherty DE, Hansen WR, Sileo L, McNAMARA TS. West nile virus transmission and ecology in birds.. Ann N Y Acad Sci 2001;951(1):54–7.
    doi: 10.1111/j.1749-6632.2001.tb02684.xpubmed: 11797804google scholar: lookup
  12. Colpitts TM, Conway MJ, Montgomery RR, Fikrig E. West nile virus: biology, transmission, and human infection.. Clin Microbiol Rev 2012;25(4):635–48.
    doi: 10.1128/CMR.00045-12pmc: PMC3485754pubmed: 23034323google scholar: lookup
  13. Smithburn K, Hughes T, Burke A, Paul J. A neurotropic virus isolated from the blood of a native of Uganda.. 1940.
  14. Murgue B, Zeller H, Deubel V. The ecology and epidemiology of West nile virus in Africa, Europe and Asia.. Japanese encephalitis and West nile viruses 2002;pp. 195–221.
    pubmed: 12082990
  15. Taylor R, Work T, Hurlbut H, Rizk F. A study of the ecology of West nile virus in Egypt.. Am J Trop Med Hyg 1956;5(4):579–620.
    doi: 10.4269/ajtmh.1956.5.579pubmed: 13354882google scholar: lookup
  16. Abutarbush S, Al-Majali A. West nile virus infection in horses in jordan: clinical cases, Seroprevalence and risk factors. Transbound Emerg Dis 2014;61:1–6.
    doi: 10.1111/tbed.12191pubmed: 24393369google scholar: lookup
  17. Eidson M, Komar N, Sorghage F, Nelson R, Talbot T, Mostashari F, McLean R, Group WNVAMS. Crow deaths as a Sentinel surveillance system for West nile virus in the Northeastern united States, 1999. Emerg Infect Dis 2001;7(4):615.
    doi: 10.3201/eid0704.017402pmc: PMC2631775pubmed: 11585521google scholar: lookup
  18. Kramer LD, Styer LM, Ebel GD. A global perspective on the epidemiology of West nile virus. Annu Rev Entomol 2008;53:61–81.
    doi: 10.1146/annurev.ento.53.103106.093258pubmed: 17645411google scholar: lookup
  19. Aharonson-Raz K, Lichter-Peled A, Tal S, Gelman B, Cohen D, Klement E, Steinman A. Spatial and Temporal distribution of West nile virus in horses in Israel (1997–2013)-From endemic to epidemics. PLoS ONE 2014;9(11):e113149.
    doi: 10.1371/journal.pone.0113149pmc: PMC4234662pubmed: 25402217google scholar: lookup
  20. Bakonyi T, Ivanics É, Erdélyi K, Ursu K, Ferenczi E, Weissenböck H, Nowotny N. Lineage 1 and 2 strains of encephalitic West nile virus, central Europe. Emerg Infect Dis 2006;12(4):618.
    doi: 10.3201/eid1204.051379pmc: PMC3294705pubmed: 16704810google scholar: lookup
  21. Hernández-Triana LM, Jeffries CL, Mansfield KL, Carnell G, Fooks AR, Johnson N. Emergence of West nile virus lineage 2 in europe: a review on the introduction and spread of a mosquito-borne disease. Front Public Health 2014;2:271.
    doi: 10.3389/fpubh.2014.00271pmc: PMC4258884pubmed: 25538937google scholar: lookup
  22. Bakonyi T, Haussig JM. West nile virus keeps on moving up in Europe. Eurosurveillance 2020;25(46):2001938.
    doi: 10.2807/1560-7917.ES.2020.25.46.2001938pmc: PMC7678036pubmed: 33213684google scholar: lookup
  23. Bergmann F, Trachsel D, Stoeckle S, Bernis Sierra J, Lübke S, Groschup M, Gehlen H, Ziegler U. Seroepidemiological survey of West nile virus infections in horses from Berlin/Brandenburg and North Rhine-Westphalia. Ger Viruses 2022;14(2):243.
    doi: 10.3390/v14020243pmc: PMC8877243pubmed: 35215837google scholar: lookup
  24. Bertram F-M, Thompson PN, Venter M. Epidemiology and clinical presentation of West nile virus infection in horses in South africa, 2016–2017. Pathogens 2020;10(1):20.
    doi: 10.3390/pathogens10010020pmc: PMC7823741pubmed: 33396935google scholar: lookup
  25. Herrador Z. West nile virus risk assessment tool Stockholm. Eur Centre Disease Prev Control 2013.
  26. Humblet M-F, Vandeputte S, Fecher-Bourgeois F, Léonard P, Gosset C, Balenghien T, Durand B, Saegerman C. Estimating the economic impact of a possible equine and human epidemic of West nile virus infection in Belgium. Eurosurveillance 2016;21(31):30309.
    doi: 10.2807/1560-7917.ES.2016.21.31.30309pmc: PMC4998509pubmed: 27526394google scholar: lookup
  27. Lan Y, Delmelle E. Space-time cluster detection techniques for infectious diseases: A systematic review. Spat Spatio-temporal Epidemiol 2023;44:100563.
    doi: 10.1016/j.sste.2022.100563pubmed: 36707196google scholar: lookup
  28. Chan T-C, King C-C. Surveillance and epidemiology of infectious diseases using Spatial and Temporal lustering methods. Infectious disease informatics and biosurveillance: research, systems and case studies 2010. pp. 207–34.
  29. Escobar LE. Ecological niche modeling: an introduction for veterinarians and epidemiologists.. Front Veterinary Sci 2020;7:519059.
    doi: 10.3389/fvets.2020.519059pmc: PMC7641643pubmed: 33195507google scholar: lookup
  30. Ward MP, Iglesias RM, Brookes VJ. Autoregressive models applied to Time-Series data in veterinary science.. Front Veterinary Sci 2020;7.
    pmc: PMC7527444pubmed: 33094106
  31. Hyndman RJ, Athanasopoulos G. Forecasting: principles and practice.. OTexts 2018.
  32. Niu B, Liang R, Zhang S, Sun X, Li F, Qiu S, Zhang H, Bao S, Zhong J, Li X. Spatiotemporal characteristics analysis and potential distribution prediction of peste des petits ruminants (PPR) in China from 2007–2018.. Transbound Emerg Dis 2022;69(5):2747–63.
    doi: 10.1111/tbed.14426pubmed: 34936210google scholar: lookup
  33. Kulldorff M. Prospective time periodic geographical disease surveillance using a scan statistic.. J Roy Stat Soc Ser (Stat Soc) 2001;164(1):61–72.
    doi: 10.1111/1467-985X.00186google scholar: lookup
  34. Kulldorff M, Heffernan R, Hartman J, Assunçao R, Mostashari F. A space–time permutation scan statistic for disease outbreak detection.. PLoS Med 2005;2(3):e59.
    doi: 10.1371/journal.pmed.0020059pmc: PMC548793pubmed: 15719066google scholar: lookup
  35. Kulldorff M. A Spatial scan statistic.. Commun Statistics-Theory Methods 1997;26(6):1481–96.
    doi: 10.1080/03610929708831995google scholar: lookup
  36. Warren DL, Glor RE, Turelli M. ENMTools: a toolbox for comparative studies of environmental niche models.. Ecography 2010;33(3):607–11.
    doi: 10.1111/j.1600-0587.2009.06142.xgoogle scholar: lookup
  37. Paz S, Semenza JC. Environmental drivers of West nile fever epidemiology in Europe and Western Asia—a review.. Int J Env Res Public Health 2013;10(8):3543–62.
    doi: 10.3390/ijerph10083543pmc: PMC3774453pubmed: 23939389google scholar: lookup
  38. TPDC. Global GIMMS NDVI3g v1 dataset (1981–2015). National Tibetan Plateau Data Center 2018.
  39. Holben BN. Characteristics of maximum-value composite images from Temporal AVHRR data.. Int J Remote Sens 1986;7(11):1417–34.
    doi: 10.1080/01431168608948945google scholar: lookup
  40. Julien Y, Sobrino JA. Optimizing and comparing gap-filling techniques using simulated NDVI time series from remotely sensed global data.. Int J Appl Earth Obs Geoinf 2019;76:93–111.
  41. Vignali S, Barras AG, Arlettaz R, Braunisch V. SDMtune: an R package to tune and evaluate species distribution models.. Ecol Evol 2020;10(20):11488–506.
    doi: 10.1002/ece3.6786pmc: PMC7593178pubmed: 33144979google scholar: lookup
  42. Kass JM, Muscarella R, Galante PJ, Bohl CL, Pinilla-Buitrago GE, Boria RA, Soley‐Guardia M, Anderson RP. ENMeval 2.0: redesigned for customizable and reproducible modeling of species’ niches and distributions. Methods Ecol Evol 2021;12(9):1602–8.
    doi: 10.1111/2041-210X.13628google scholar: lookup
  43. Burnham KP, Anderson DR. Multimodel inference: Understanding AIC and BIC in model selection. Sociol Methods Res 2004;33(2):261–304.
    doi: 10.1177/0049124104268644google scholar: lookup
  44. Warren DL, Seifert SN. Ecological niche modeling in maxent: the importance of model complexity and the performance of model selection criteria. Ecol Appl 2011;21(2):335–42.
    doi: 10.1890/10-1171.1pubmed: 21563566google scholar: lookup
  45. Abad-Cobo A, Llorente F, Barbero MC, Cruz‐López F, Forés P, Jiménez‐Clavero MÁ. Serosurvey reveals exposure to West nile virus in asymptomatic horse populations in central Spain prior to recent disease foci. Transbound Emerg Dis 2017;64(5):1387–92.
    doi: 10.1111/tbed.12510pubmed: 27156847google scholar: lookup
  46. Assaid N, Mousson L, Moutailler S, Arich S, Akarid K, Monier M, Beck C, Lecollinet S, Failloux A-B, Sarih MH. Evidence of circulation of West nile virus in culex pipiens mosquitoes and horses in Morocco. Acta Trop 2020;205:105414.
    doi: 10.1016/j.actatropica.2020.105414pubmed: 32088277google scholar: lookup
  47. Young JJ, Coulombier D, Domanović D, Group EUWNFW, Zeller H, Gossner CM. One health approach for West nile virus surveillance in the European union: relevance of equine data for blood safety. Eurosurveillance 2019;24(16):1800349.
    doi: 10.2807/1560-7917.ES.2019.24.16.1800349pmc: PMC6826348pubmed: 31014416google scholar: lookup
  48. Castillo-Olivares J, Wood J. West nile virus infection of horses. Vet Res 2004;35(4):467–83.
    doi: 10.1051/vetres:2004022pubmed: 15236677google scholar: lookup
  49. Kaky E, Nolan V, Alatawi A, Gilbert F. A comparison between ensemble and maxent species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants. Ecol Inf 2020;60:101150.
    doi: 10.1016/j.ecoinf.2020.101150google scholar: lookup
  50. Thibaud E, Petitpierre B, Broennimann O, Davison AC, Guisan A. Measuring the relative effect of factors affecting species distribution model predictions. Methods Ecol Evol 2014;5(9):947–55.
    doi: 10.1111/2041-210X.12203google scholar: lookup
  51. Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ. A statistical explanation of maxent for ecologists. Divers Distrib 2011;17(1):43–57.
    doi: 10.1111/j.1472-4642.2010.00725.xgoogle scholar: lookup
  52. Merow C, Smith MJ, Silander JA Jr. A practical guide to maxent for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 2013;36(10):1058–69.
    doi: 10.1111/j.1600-0587.2013.07872.xgoogle scholar: lookup
  53. Radosavljevic A, Anderson RP. Making better maxent models of species distributions: complexity, overfitting and evaluation. J Biogeogr 2014;41(4):629–43.
    doi: 10.1111/jbi.12227google scholar: lookup
  54. Hahn MB, Monaghan AJ, Hayden MH, Eisen RJ, Delorey MJ, Lindsey NP, Nasci RS, Fischer M. Meteorological conditions associated with increased incidence of West nile virus disease in the united States, 2004–2012. Am J Trop Med Hyg 2015;92(5):1013.
    doi: 10.4269/ajtmh.14-0737pmc: PMC4426558pubmed: 25802435google scholar: lookup
  55. Myer MH, Johnston JM. Spatiotemporal bayesian modeling of West nile virus: identifying risk of infection in mosquitoes with local-scale predictors. Sci Total Environ 2019;650:2818–29.
    doi: 10.1016/j.scitotenv.2018.09.397pmc: PMC7676626pubmed: 30373059google scholar: lookup
  56. Ciota AT, Matacchiero AC, Kilpatrick AM, Kramer LD. The effect of temperature on life history traits of culex mosquitoes. J Med Entomol 2014;51(1):55–62.
    doi: 10.1603/ME13003pmc: PMC3955846pubmed: 24605453google scholar: lookup
  57. Dohm DJ, O’Guinn ML, Turell MJ. Effect of environmental temperature on the ability of culex pipiens (Diptera: Culicidae) to transmit West nile virus. J Med Entomol 2002;39(1):221–5.
    doi: 10.1603/0022-2585-39.1.221pubmed: 11931261google scholar: lookup
  58. Kilpatrick AM, Meola MA, Moudy RM, Kramer LD. Temperature, viral genetics, and the transmission of West nile virus by culex pipiens mosquitoes. PLoS Path 2008;4(6):e1000092.
    doi: 10.1371/journal.ppat.1000092pmc: PMC2430533pubmed: 18584026google scholar: lookup
  59. Vogels CB, Fros JJ, Göertz GP, Pijlman GP, Koenraadt CJ. Vector competence of Northern European culex pipiens biotypes and hybrids for West nile virus is differentially affected by temperature. Parasites Vectors 2016;9:1–7.
    doi: 10.1186/s13071-016-1677-0pmc: PMC4937539pubmed: 27388451google scholar: lookup
  60. Gardner AM, Hamer GL, Hines AM, Newman CM, Walker ED, Ruiz MO. Weather variability affects abundance of larval culex (Diptera: Culicidae) in storm water catch basins in suburban Chicago. J Med Entomol 2012;49(2):270–6.
    doi: 10.1603/ME11073pmc: PMC4053168pubmed: 22493843google scholar: lookup
  61. Koenraadt C, Harrington L. Flushing effect of rain on container-inhabiting mosquitoes Aedes aegypti and culex pipiens (Diptera: Culicidae). J Med Entomol 2008;45(1):28–35.
    doi: 10.1093/jmedent/45.1.28pubmed: 18283939google scholar: lookup
  62. Shaman J, Day JF, Stieglitz M. Drought-induced amplification and epidemic transmission of West nile virus in Southern Florida. J Med Entomol 2005;42(2):134–41.
    doi: 10.1093/jmedent/42.2.134pubmed: 15799522google scholar: lookup
  63. Eisen L, Bolling BG, Blair CD, Beaty BJ, Moore CG. Mosquito species richness, composition, and abundance along habitat-climate-elevation gradients in the Northern Colorado front range. J Med Entomol 2008;45(4):800–11.
    doi: 10.1093/jmedent/45.4.800pubmed: 18714885google scholar: lookup
  64. Bargaoui R, Lecollinet S, Lancelot RJT, Diseases E. Mapping the serological prevalence rate of West nile fever in equids. T Unisia 2015;62(1):55–66.
    pubmed: 23906318
  65. García-Carrasco J-M, Muñoz A-R, Olivero J, Segura M, García-Bocanegra I, Real RJE. West nile virus in the Iberian peninsula: using equine cases to identify high-risk areas for humans. 2023;28(40):2200844.
    pmc: PMC10557382pubmed: 37796440
  66. Di Pol G, Crotta M, Taylor RAJT, Diseases e. modelling the temperature suitability for the risk of West nile virus establishment in European culex pipiens populations. 2022;69(5):e1787–99.
    pmc: PMC9790397pubmed: 35304820
  67. Farooq Z, Rocklöv J, Wallin J, Abiri N, Sewe MO, Sjödin H, Semenza JC. Artificial intelligence to predict West nile virus outbreaks with eco-climatic drivers. JTLRHE 2022, 17.
    pmc: PMC8971633pubmed: 35373173
  68. Lu L, Zhang F, Oude Munnink BB, Munger E, Sikkema RS, Pappa S, Tsioka K, Sinigaglia A, Dal Molin E, Shih. West nile virus spread in europe: phylogeographic pattern analysis and key drivers. bbjpp 2024;20(1):e1011880.
    pmc: PMC10810478pubmed: 38271294
  69. Marini G, Manica M, Delucchi L, Pugliese A, Rosà. Spring temperature shapes West nile virus transmission in Europe. RJAT 2021;215:105796.
    pubmed: 33310078
  70. Marini G, Pugliese A, Wint W, Alexander NS, Rizzoli A, Rosa. Modelling the West nile virus force of infection in the European human population. RJOH 2022;15:100462.
    pmc: PMC9754970pubmed: 36532665

Citations

This article has been cited 0 times.
Subscribe to our newsletter
Join 106,497 horse owners like you who receive our email updates on horse health and nutrition.