1887
Research Open Access
Like 1

Abstract

Background

Tick-borne encephalitis (TBE) is a disease which can lead to severe neurological symptoms, caused by the TBE virus (TBEV). The natural transmission cycle occurs in foci and involves ticks as vectors and several key hosts that act as reservoirs and amplifiers of the infection spread. Recently, the incidence of TBE in Europe has been rising in both endemic and new regions.

Aim

In this study we want to provide comprehensive understanding of the main ecological and environmental factors that affect TBE spread across Europe.

Methods

We searched available literature on covariates linked with the circulation of TBEV in Europe. We then assessed the best predictors for TBE incidence in 11 European countries by means of statistical regression, using data on human infections provided by the European Surveillance System (TESSy), averaged between 2017 and 2021.

Results

We retrieved data from 62 full-text articles and identified 31 different covariates associated with TBE occurrence. Finally, we selected eight variables from the best model, including factors linked to vegetation cover, climate, and the presence of tick hosts.

Discussion

The existing literature is heterogeneous, both in study design and covariate types. Here, we summarised and statistically validated the covariates affecting the variability of TBEV across Europe. The analysis of the factors enhancing disease emergence is a fundamental step towards the identification of potential hotspots of viral circulation. Hence, our results can support modelling efforts to estimate the risk of TBEV infections and help decision-makers implement surveillance and prevention campaigns.

Loading

Article metrics loading...

/content/10.2807/1560-7917.ES.2023.28.42.2300121
2023-10-19
2024-02-22
http://instance.metastore.ingenta.com/content/10.2807/1560-7917.ES.2023.28.42.2300121
Loading
Loading full text...

Full text loading...

/deliver/fulltext/eurosurveillance/28/42/eurosurv-28-42-4.html?itemId=/content/10.2807/1560-7917.ES.2023.28.42.2300121&mimeType=html&fmt=ahah

References

  1. Gritsun TS, Lashkevich VA, Gould EA. Tick-borne encephalitis. Antiviral Res. 2003;57(1-2):129-46.  https://doi.org/10.1016/S0166-3542(02)00206-1  PMID: 12615309 
  2. Růžek D, Dobler G, Donoso Mantke O. Tick-borne encephalitis: pathogenesis and clinical implications. Travel Med Infect Dis. 2010;8(4):223-32.  https://doi.org/10.1016/j.tmaid.2010.06.004  PMID: 20970725 
  3. Ruzek D, Avšič Županc T, Borde J, Chrdle A, Eyer L, Karganova G, et al. Tick-borne encephalitis in Europe and Russia: Review of pathogenesis, clinical features, therapy, and vaccines. Antiviral Res. 2019;164:23-51.  https://doi.org/10.1016/j.antiviral.2019.01.014  PMID: 30710567 
  4. Ličková M, Fumačová Havlíková S, Sláviková M, Klempa B. Alimentary Infections by tick-borne encephalitis Virus. Viruses. 2021;14(1):56.  https://doi.org/10.3390/v14010056  PMID: 35062261 
  5. Dobler G, Hufert F, Pfeffer M, Essbauer S. Tick-borne encephalitis: from microfocus to human disease. In: Mehlhorn H, editor. Progress in Parasitology. Berlin, Heidelberg: Springer; 2011. p. 323-31.
  6. European Centre for Disease Prevention and Control (ECDC). Tick-borne encephalitis. In: ECDC. Annual epidemiological report for 2020. Stockholm: ECDC. 2022. Available from: https://www.ecdc.europa.eu/en/publications-data/tick-borne-encephalitis-annual-epidemiological-report-2020
  7. Beauté J, Spiteri G, Warns-Petit E, Zeller H. Tick-borne encephalitis in Europe, 2012 to 2016. Euro Surveill. 2018;23(45):1800201.  https://doi.org/10.2807/1560-7917.ES.2018.23.45.1800201  PMID: 30424829 
  8. Kreusch TM, Holding M, Hewson R, Harder T, Medlock JM, Hansford KM, et al. A probable case of tick-borne encephalitis (TBE) acquired in England, July 2019. Euro Surveill. 2019;24(47):1900679.  https://doi.org/10.2807/1560-7917.ES.2019.24.47.1900679  PMID: 31771699 
  9. Stoefs A, Heyndrickx L, De Winter J, Coeckelbergh E, Willekens B, Alonso-Jiménez A, et al. Autochthonous cases of tick-borne encephalitis, Belgium, 2020. Emerg Infect Dis. 2021;27(8):2179-82.  https://doi.org/10.3201/eid2708.211175  PMID: 34111382 
  10. Velay A, Solis M, Kack-Kack W, Gantner P, Maquart M, Martinot M, et al. A new hot spot for tick-borne encephalitis (TBE): A marked increase of TBE cases in France in 2016. Ticks Tick Borne Dis. 2018;9(1):120-5.  https://doi.org/10.1016/j.ttbdis.2017.09.015  PMID: 28988602 
  11. Randolph SE, Asokliene L, Avsic-Zupanc T, Bormane A, Burri C, Gern L, et al. Variable spikes in tick-borne encephalitis incidence in 2006 independent of variable tick abundance but related to weather. Parasit Vectors. 2008;1(1):44.  https://doi.org/10.1186/1756-3305-1-44  PMID: 19068106 
  12. Randolph SE, Rogers DJ. Fragile transmission cycles of tick-borne encephalitis virus may be disrupted by predicted climate change. Proc Biol Sci. 2000;267(1454):1741-4.  https://doi.org/10.1098/rspb.2000.1204  PMID: 12233771 
  13. Rubel F, Brugger K. Tick-borne encephalitis incidence forecasts for Austria, Germany, and Switzerland. Ticks Tick Borne Dis. 2020;11(5):101437.  https://doi.org/10.1016/j.ttbdis.2020.101437  PMID: 32723631 
  14. Brugger K, Walter M, Chitimia-Dobler L, Dobler G, Rubel F. Forecasting next season’s Ixodes ricinus nymphal density: the example of southern Germany 2018. Exp Appl Acarol. 2018;75(3):281-8.  https://doi.org/10.1007/s10493-018-0267-6  PMID: 29846854 
  15. Cagnacci F, Bolzoni L, Rosà R, Carpi G, Hauffe HC, Valent M, et al. Effects of deer density on tick infestation of rodents and the hazard of tick-borne encephalitis. I: empirical assessment. Int J Parasitol. 2012;42(4):365-72.  https://doi.org/10.1016/j.ijpara.2012.02.012  PMID: 22464896 
  16. Dub T, Ollgren J, Huusko S, Uusitalo R, Siljander M, Vapalahti O, et al. Game animal density, climate, and tick-borne encephalitis in Finland, 2007-2017. Emerg Infect Dis. 2020;26(12):2899-906.  https://doi.org/10.3201/eid2612.191282  PMID: 33219653 
  17. Rizzoli A, Hauffe HC, Tagliapietra V, Neteler M, Rosà R. Forest structure and roe deer abundance predict tick-borne encephalitis risk in Italy. PLoS One. 2009;4(2):e4336.  https://doi.org/10.1371/journal.pone.0004336  PMID: 19183811 
  18. Smura T, Tonteri E, Jääskeläinen A, von Troil G, Kuivanen S, Huitu O, et al. Recent establishment of tick-borne encephalitis foci with distinct viral lineages in the Helsinki area, Finland. Emerg Microbes Infect. 2019;8(1):675-83.  https://doi.org/10.1080/22221751.2019.1612279  PMID: 31084456 
  19. Agergaard CN, Rosenstierne MW, Bødker R, Rasmussen M, Andersen PHS, Fomsgaard A. New tick-borne encephalitis virus hot spot in Northern Zealand, Denmark, October 2019. Euro Surveill. 2019;24(43):1900639.  https://doi.org/10.2807/1560-7917.ES.2019.24.43.1900639  PMID: 31662158 
  20. Wallenhammar A, Lindqvist R, Asghar N, Gunaltay S, Fredlund H, Davidsson Å, et al. Revealing new tick-borne encephalitis virus foci by screening antibodies in sheep milk. Parasit Vectors. 2020;13(1):185.  https://doi.org/10.1186/s13071-020-04030-4  PMID: 32268924 
  21. Danielová V, Schwarzová L, Materna J, Daniel M, Metelka L, Holubová J, et al. Tick-borne encephalitis virus expansion to higher altitudes correlated with climate warming. Int J Med Microbiol. 2008;298:68-72.  https://doi.org/10.1016/j.ijmm.2008.02.005 
  22. Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467-73.  https://doi.org/10.7326/M18-0850  PMID: 30178033 
  23. European Commission. Regulation (EC) No 1059/2003 of the European Parliament and of the Council of 26 May 2003 on the establishment of a common classification of territorial units for statistics (NUTS). Official Journal of the European Union. Luxembourg: Publications Office of the European Union. 21.06.2003:L 155. Available from: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A32003R1059
  24. Tatem AJ. WorldPop, open data for spatial demography. Sci Data. 2017;4(1):170004.  https://doi.org/10.1038/sdata.2017.4  PMID: 28140397 
  25. Wan Z, Hook S, Hulley G. MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 0.05 Deg CMG. V061. Sioux Falls: National Aeronautics and Space Administration. [Accessed: 11 Apr 2022]. Available from: https://lpdaac.usgs.gov/products/mod11c1v061
  26. Wan Z, Hook S, Hulley G. MODIS/Terra Land Surface Temperature/Emissivity Monthly L3 Global 0.05 Deg CMG. V061. Sioux Falls: National Aeronautics and Space Administration. [Accessed: 11 Apr 2022]. Available from: https://lpdaac.usgs.gov/products/mod11c3v061
  27. Didan K. MOD13C2 MODIS/Terra Vegetation Indices Monthly L3 Global 0.05Deg CMG. V006. Sioux Falls: National Aeronautics and Space Administration, [Accessed: 11 Apr 2022]. Available from: https://lpdaac.usgs.gov/products/mod13c2v006
  28. Metz M, Haas J, Neteler M, William W, Jones P. Monthly time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2021) derived from ERA5-Land data. Zenodo; 2022. Available from: https://zenodo.org/record/6146384
  29. O’Donnell MS, Ignizio DA. Bioclimatic predictors for supporting ecological applications in the conterminous United States. U.S. Geological Survey Data Series. 2012;691:10 p. Available from: https://pubs.usgs.gov/ds/691
  30. Randolph SE, Green RM, Peacey MF, Rogers DJ. Seasonal synchrony: the key to tick-borne encephalitis foci identified by satellite data. Parasitology. 2000;121(Pt 1):15-23.  https://doi.org/10.1017/S0031182099006083  PMID: 11085221 
  31. Tuanmu MN, Jetz W. A global 1-km consensus land-cover product for biodiversity and ecosystem modelling. Glob Ecol Biogeogr. 2014;23(9):1031-45.  https://doi.org/10.1111/geb.12182 
  32. Danielson JJ, Gesch DB. Global multi-resolution terrain elevation data 2010 (GMTED2010). U.S. Geological Survey Open-File Report 2011-1073. 2011;26 p. Available from: https://pubs.usgs.gov/of/2011/1073
  33. Fabri ND, Sprong H, Hofmeester TR, Heesterbeek H, Donnars BF, Widemo F, et al. Wild ungulate species differ in their contribution to the transmission of Ixodes ricinus-borne pathogens. Parasit Vectors. 2021;14(1):360.  https://doi.org/10.1186/s13071-021-04860-w  PMID: 34246293 
  34. Alexander N, Morley D, Jolyon M, Searle K, Wint W. A first attempt at modelling roe deer (Capreolus capreolus) distributions over Europe. figshare; 2014. Available from: https://figshare.com/articles/dataset/A_first_attempt_at_modelling_roe_deer_Capreolus_capreolus_distributions_over_Europe/1008335/1
  35. Wint W, Morley D, Medlock J, Alexander N. A first attempt at modelling red deer (Cervus elaphus) distributions over Europe. figshare; 2014. Available from: https://figshare.com/articles/dataset/A_first_attempt_at_modelling_red_deer_Cervus_elaphus_distributions_over_Europe/1008334/1
  36. Zuur AF, Ieno EN, Elphick CS. A protocol for data exploration to avoid common statistical problems. Methods Ecol Evol. 2010;1(1):3-14.  https://doi.org/10.1111/j.2041-210X.2009.00001.x 
  37. Zuur AF, Ieno EN, Smith GM. Analysing ecological data. In: Statistics for Biology and Health Series. Gail M, Krickeberg K, Sarnet J, Tsiatis A, Wong W, editors. New York: Springer; 2007.
  38. Burnham KP, Anderson DR, Burnham KP. Model selection and multimodel inference: a practical information-theoretic approach. 2nd ed. New York: Springer; 2002. 488 p.
  39. Zuur AF, Ieno EN. A protocol for conducting and presenting results of regression-type analyses. Methods Ecol Evol. 2016;7(6):636-45.  https://doi.org/10.1111/2041-210X.12577 
  40. Luke SG. Evaluating significance in linear mixed-effects models in R. Behav Res Methods. 2017;49(4):1494-502.  https://doi.org/10.3758/s13428-016-0809-y  PMID: 27620283 
  41. R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2022. Available from: https://www.R-project.org/
  42. Wickham H, Romain F, Lionel H, Müller K. dplyr: A Grammar of data manipulation. R package version 1.0.8. Vienna: R Foundation for Statistical Computing; 2022. Available from: https://CRAN.R-project.org/package=dplyr
  43. Baston D. exactextractr: Fast extraction from raster datasets using polygons. R package version 0.7.2. Vienna: R Foundation for Statistical Computing; 2021. Available from: https://CRAN.R-project.org/package=exactextractr
  44. Hijmans RJ. raster: Geographic data analysis and modeling. R package version 3.5-15. Vienna: R Foundation for Statistical Computing; 2022. Available from: https://CRAN.R-project.org/package=raster
  45. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67(1).  https://doi.org/10.18637/jss.v067.i01 
  46. Kuznetsova A, Brockhoff PB, Christensen RHB. lmerTest Package: tests in linear mixed effects models. J Stat Softw. 2017;82(13).  https://doi.org/10.18637/jss.v082.i13 
  47. Barton K. MuMIn: multi-model inference. R package version 1.43.17. Vienna: R Foundation for Statistical Computing; 2020. Available from: https://CRAN.R-project.org/package=MuMIn
  48. Andreassen A, Jore S, Cuber P, Dudman S, Tengs T, Isaksen K, et al. Prevalence of tick borne encephalitis virus in tick nymphs in relation to climatic factors on the southern coast of Norway. Parasit Vectors. 2012;5(1):177.  https://doi.org/10.1186/1756-3305-5-177  PMID: 22913287 
  49. Hönig V, Svec P, Halas P, Vavruskova Z, Tykalova H, Kilian P, et al. Ticks and tick-borne pathogens in South Bohemia (Czech Republic)--Spatial variability in Ixodes ricinus abundance, Borrelia burgdorferi and tick-borne encephalitis virus prevalence. Ticks Tick Borne Dis. 2015;6(5):559-67.  https://doi.org/10.1016/j.ttbdis.2015.04.010  PMID: 25976235 
  50. Burri C, Bastic V, Maeder G, Patalas E, Gern L. Microclimate and the zoonotic cycle of tick-borne encephalitis virus in Switzerland. J Med Entomol. 2011;48(3):615-27.  https://doi.org/10.1603/ME10180  PMID: 21661323 
  51. Bolzoni L, Rosà R, Cagnacci F, Rizzoli A. Effect of deer density on tick infestation of rodents and the hazard of tick-borne encephalitis. II: population and infection models. Int J Parasitol. 2012;42(4):373-81.  https://doi.org/10.1016/j.ijpara.2012.02.006  PMID: 22429768 
  52. Bournez L, Umhang G, Moinet M, Richomme C, Demerson JM, Caillot C, et al. Tick-borne encephalitis virus: seasonal and annual variation of epidemiological parameters related to nymph-to-larva transmission and exposure of small mammals. Pathogens. 2020;9(7):518.  https://doi.org/10.3390/pathogens9070518  PMID: 32605114 
  53. Daniel M, Materna J, Hönig V, Metelka L, Danielová V, Harčarik J, et al. Vertical distribution of the tick Ixodes ricinus and tick-borne pathogens in the northern Moravian mountains correlated with climate warming (Jeseníky Mts., Czech Republic). Cent Eur J Public Health. 2009;17(3):139-45.  https://doi.org/10.21101/cejph.a3550  PMID: 20020603 
  54. Rosà R, Tagliapietra V, Manica M, Arnoldi D, Hauffe HC, Rossi C, et al. Changes in host densities and co-feeding pattern efficiently predict tick-borne encephalitis hazard in an endemic focus in northern Italy. Int J Parasitol. 2019;49(10):779-87.  https://doi.org/10.1016/j.ijpara.2019.05.006  PMID: 31348960 
  55. Knap N, Avšič-Županc T. Factors affecting the ecology of tick-borne encephalitis in Slovenia. Epidemiol Infect. 2015;143(10):2059-67.  https://doi.org/10.1017/S0950268815000485  PMID: 25918865 
  56. Kiffner C, Vor T, Hagedorn P, Niedrig M, Rühe F. Determinants of tick-borne encephalitis virus antibody presence in roe deer (Capreolus capreolus) sera. Med Vet Entomol. 2012;26(1):18-25.  https://doi.org/10.1111/j.1365-2915.2011.00961.x  PMID: 21592155 
  57. Knap N, Avšič-Županc T. Correlation of TBE incidence with red deer and roe deer abundance in Slovenia. PLoS One. 2013;8(6):e66380.  https://doi.org/10.1371/journal.pone.0066380  PMID: 23776668 
  58. Tkadlec E, Václavík T, Široký P. Rodent host abundance and climate variability as predictors of tickborne disease risk 1 year in advance. Emerg Infect Dis. 2019;25(9):1738-41.  https://doi.org/10.3201/eid2509.190684  PMID: 31441762 
  59. Brugger K, Boehnke D, Petney T, Dobler G, Pfeffer M, Silaghi C, et al. A density map of the tick-borne encephalitis and lyme borreliosis vector Ixodes ricinus (Acari: Ixodidae) for Germany. J Med Entomol. 2016;53(6):1292-302.  https://doi.org/10.1093/jme/tjw116  PMID: 27498885 
  60. Domşa C, Mihalca A, Sándor A. Modeling the distribution of Ixodes ricinus in Romania. North-West J Zool. 2018;14(1):25-9.
  61. Kjær LJ, Soleng A, Edgar KS, Lindstedt HEH, Paulsen KM, Andreassen ÅK, et al. Predicting and mapping human risk of exposure to Ixodes ricinus nymphs using climatic and environmental data, Denmark, Norway and Sweden, 2016. Euro Surveill. 2019;24(9):1800101.  https://doi.org/10.2807/1560-7917.ES.2019.24.9.1800101  PMID: 30862329 
  62. Porretta D, Mastrantonio V, Amendolia S, Gaiarsa S, Epis S, Genchi C, et al. Effects of global changes on the climatic niche of the tick Ixodes ricinus inferred by species distribution modelling. Parasit Vectors. 2013;6(1):271.  https://doi.org/10.1186/1756-3305-6-271  PMID: 24330500 
  63. Zeimes CB, Olsson GE, Hjertqvist M, Vanwambeke SO. Shaping zoonosis risk: landscape ecology vs. landscape attractiveness for people, the case of tick-borne encephalitis in Sweden. Parasit Vectors. 2014;7(1):370.  https://doi.org/10.1186/1756-3305-7-370  PMID: 25128197 
  64. Jaenson TGT, Petersson EH, Jaenson DGE, Kindberg J, Pettersson JHO, Hjertqvist M, et al. The importance of wildlife in the ecology and epidemiology of the TBE virus in Sweden: incidence of human TBE correlates with abundance of deer and hares. Parasit Vectors. 2018;11(1):477.  https://doi.org/10.1186/s13071-018-3057-4  PMID: 30153856 
  65. Palo RT. Tick-borne encephalitis transmission risk: its dependence on host population dynamics and climate effects. Vector Borne Zoonotic Dis. 2014;14(5):346-52.  https://doi.org/10.1089/vbz.2013.1386  PMID: 24745813 
  66. Cattadori IM, Haydon DT, Thirgood SJ, Hudson PJ. Are indirect measures of abundance a useful index of population density? The case of red grouse harvesting. Oikos. 2003;100(3):439-46.  https://doi.org/10.1034/j.1600-0706.2003.12072.x 
  67. Achazi K, Růžek D, Donoso-Mantke O, Schlegel M, Ali HS, Wenk M, et al. Rodents as sentinels for the prevalence of tick-borne encephalitis virus. Vector Borne Zoonotic Dis. 2011;11(6):641-7.  https://doi.org/10.1089/vbz.2010.0236  PMID: 21548766 
  68. Dizij A, Kurtenbach K. Clethrionomys glareolus, but not Apodemus flavicollis, acquires resistance to Ixodes ricinus L., the main European vector of Borrelia burgdorferi. Parasite Immunol. 1995;17(4):177-83.  https://doi.org/10.1111/j.1365-3024.1995.tb00887.x  PMID: 7624158 
  69. Carpi G, Cagnacci F, Neteler M, Rizzoli A. Tick infestation on roe deer in relation to geographic and remotely sensed climatic variables in a tick-borne encephalitis endemic area. Epidemiol Infect. 2008;136(10):1416-24.  https://doi.org/10.1017/S0950268807000039  PMID: 18081949 
  70. Hudson PJ, Rizzoli A, Rosà R, Chemini C, Jones LD, Gould EA. Tick-borne encephalitis virus in northern Italy: molecular analysis, relationships with density and seasonal dynamics of Ixodes ricinus. Med Vet Entomol. 2001;15(3):304-13.  https://doi.org/10.1046/j.0269-283x.2001.00317.x  PMID: 11583449 
  71. Kiffner C, Zucchini W, Schomaker P, Vor T, Hagedorn P, Niedrig M, et al. Determinants of tick-borne encephalitis in counties of southern Germany, 2001-2008. Int J Health Geogr. 2010;9(1):42.  https://doi.org/10.1186/1476-072X-9-42  PMID: 20707897 
  72. Kolář J, Potůčková M, Štefanová E. Tick-born encephalitis risk assessment based on satellite data. AUC GEOGRAPHICA.2016;51(2):155-67.  https://doi.org/10.14712/23361980.2016.13 
  73. Kriz B, Daniel M, Benes C, Maly M. The role of game (wild boar and roe deer) in the spread of tick-borne encephalitis in the Czech Republic. Vector Borne Zoonotic Dis. 2014;14(11):801-7.  https://doi.org/10.1089/vbz.2013.1569  PMID: 25409271 
  74. Rácz GR, Bán E, Ferenczi E, Berencsi G. A simple spatial model to explain the distribution of human tick-borne encephalitis cases in hungary. Vector Borne Zoonotic Dis. 2006;6(4):369-78.  https://doi.org/10.1089/vbz.2006.6.369  PMID: 17187571 
  75. Uusitalo R, Siljander M, Dub T, Sane J, Sormunen JJ, Pellikka P, et al. Modelling habitat suitability for occurrence of human tick-borne encephalitis (TBE) cases in Finland. Ticks Tick Borne Dis. 2020;11(5):101457.  https://doi.org/10.1016/j.ttbdis.2020.101457  PMID: 32723626 
  76. Vanwambeke SO, Sumilo D, Bormane A, Lambin EF, Randolph SE. Landscape predictors of tick-borne encephalitis in Latvia: land cover, land use, and land ownership. Vector Borne Zoonotic Dis. 2010;10(5):497-506.  https://doi.org/10.1089/vbz.2009.0116  PMID: 19877818 
  77. Stefanoff P, Rosinska M, Samuels S, White DJ, Morse DL, Randolph SE. A national case-control study identifies human socio-economic status and activities as risk factors for tick-borne encephalitis in Poland. PLoS One. 2012;7(9):e45511.  https://doi.org/10.1371/journal.pone.0045511  PMID: 23029063 
  78. Daniel M, Kříž B, Danielová V, Beneš Č. Sudden increase in tick-borne encephalitis cases in the Czech Republic, 2006. Int J Med Microbiol. 2008;298:81-7.  https://doi.org/10.1016/j.ijmm.2008.02.006 
  79. Borde JP, Kaier K, Hehn P, Matzarakis A, Frey S, Bestehorn M, et al. The complex interplay of climate, TBEV vector dynamics and TBEV infection rates in ticks-Monitoring a natural TBEV focus in Germany, 2009-2018. PLoS One. 2021;16(1):e0244668.  https://doi.org/10.1371/journal.pone.0244668  PMID: 33411799 
  80. Vollack K, Sodoudi S, Névir P, Müller K, Richter D. Influence of meteorological parameters during the preceding fall and winter on the questing activity of nymphal Ixodes ricinus ticks. Int J Biometeorol. 2017;61(10):1787-95.  https://doi.org/10.1007/s00484-017-1362-9  PMID: 28462449 
  81. Randolph SE, Storey K. Impact of microclimate on immature tick-rodent host interactions (Acari: Ixodidae): implications for parasite transmission. J Med Entomol. 1999;36(6):741-8.  https://doi.org/10.1093/jmedent/36.6.741  PMID: 10593075 
  82. Daniel M, Kříz˘ B, Valter J, Kott I, Danielová V. The influence of meteorological conditions of the preceding winter on the incidences of tick-borne encephalitis and Lyme borreliosis in the Czech Republic. Int J Med Microbiol. 2008;298:60-7.  https://doi.org/10.1016/j.ijmm.2008.05.001 
  83. Kiffner C, Vor T, Hagedorn P, Niedrig M, Rühe F. Factors affecting patterns of tick parasitism on forest rodents in tick-borne encephalitis risk areas, Germany. Parasitol Res. 2011;108(2):323-35.  https://doi.org/10.1007/s00436-010-2065-x  PMID: 20878183 
  84. Walter M, Vogelgesang JR, Rubel F, Brugger K. Tick-borne encephalitis virus and its European distribution in ticks and endothermic mammals. Microorganisms. 2020;8(7):1065.  https://doi.org/10.3390/microorganisms8071065  PMID: 32708877 
  85. Zeman P, Bene C. A tick-borne encephalitis ceiling in Central Europe has moved upwards during the last 30 years: possible impact of global warming? Int J Med Microbiol. 2004;293(Suppl 37):48-54. PMID: 15146984 
  86. Knap N, Durmiši E, Saksida A, Korva M, Petrovec M, Avšič-Županc T. Influence of climatic factors on dynamics of questing Ixodes ricinus ticks in Slovenia. Vet Parasitol. 2009;164(2-4):275-81.  https://doi.org/10.1016/j.vetpar.2009.06.001  PMID: 19560275 
  87. Hönig V, Švec P, Marek L, Mrkvička T, Dana Z, Wittmann MV, et al. Model of risk of exposure to Lyme borreliosis and tick-borne encephalitis virus-infected ticks in the border area of the Czech Republic (South Bohemia) and Germany (Lower Bavaria and Upper Palatinate). Int J Environ Res Public Health. 2019;16(7):1173.  https://doi.org/10.3390/ijerph16071173  PMID: 30986900 
  88. Stefanoff P, Rubikowska B, Bratkowski J, Ustrnul Z, Vanwambeke SO, Rosinska M. A predictive model has identified tick-borne encephalitis high-risk areas in regions where no cases were reported previously, Poland, 1999-2012. Int J Environ Res Public Health. 2018;15(4):677.  https://doi.org/10.3390/ijerph15040677  PMID: 29617333 
  89. Rosà R, Andreo V, Tagliapietra V, Baráková I, Arnoldi D, Hauffe HC, et al. Effect of climate and land use on the spatio-temporal variability of tick-borne bacteria in Europe. Int J Environ Res Public Health. 2018;15(4):732.  https://doi.org/10.3390/ijerph15040732  PMID: 29649132 
  90. Rosà R, Pugliese A, Ghosh M, Perkins SE, Rizzoli A. Temporal variation of Ixodes ricinus intensity on the rodent host Apodemus flavicollis in relation to local climate and host dynamics. Vector Borne Zoonotic Dis. 2007;7(3):285-95.  https://doi.org/10.1089/vbz.2006.0607  PMID: 17760511 
/content/10.2807/1560-7917.ES.2023.28.42.2300121
Loading

Data & Media loading...

Supplementary data

Submit comment
Close
Comment moderation successfully completed
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error