Animals; Europe/epidemiology; Humans; Arvicolinae/virology; Climate; Bayes Theorem; Hemorrhagic Fever with Renal Syndrome/epidemiology; Hemorrhagic Fever with Renal Syndrome/virology; Disease Outbreaks; Arvicolinae; Europe; Hemorrhagic Fever with Renal Syndrome; Public Health, Environmental and Occupational Health; Health, Toxicology and Mutagenesis
Abstract :
[en] Environmental factors, such as fluctuations of climatic conditions and land cover, play a pivotal role in driving infectious disease epidemics, particularly those originating from wildlife reservoirs. Orthohantavirus puumalaense, hosted by bank voles in Europe, is the causative agent of a form of hemorrhagic fever and renal syndrome called nephropathia epidemica. Despite two decades of consistent presence in western Europe, nephropathia epidemica outbreaks still pose challenges due to localized periodic occurrences and a lack of understanding of its environmental drivers.
OBJECTIVE: Our study aims to bridge this gap by investigating the specific ecological and climatic factors influencing nephropathia epidemica outbreaks in western Europe.
METHODS: We compiled monthly, serologically confirmed nephropathia epidemica case data obtained from public health authorities in Belgium, France, Germany, and the Netherlands for the period 2004-2012. Cases were georeferenced to the finest available administrative unit. We selected 28 covariates, including climatic variables, land cover, tree species distributions, and human population, and implemented a Bayesian spatiotemporal model using integrated nested Laplace approximation (INLA) with zero-inflated Poisson distribution, including fixed effects and spatial, temporal, and nonstructured random effects.
RESULTS: We identified key triggers for nephropathia epidemica outbreaks, particularly climate-mediated changes in all seasons up to 2 years before, favoring tree mast impacting bank vole abundance. Our findings revealed that while land-cover factors mostly determine hotspot locations, climatic fluctuation patterns rather tend to modulate outbreak intensity.
DISCUSSION: Crucially, our model allows for the generation of yearly maps showcasing nephropathia epidemica incidence and risk factors, aiding in public health preparedness against climate change-induced disease emergence. This work represents a significant step toward developing targeted forecasting tools for Orthohantavirus puumalaense outbreaks, offering valuable insights for epidemic control strategies.
Vincenti-Gonzalez, Maria Fernanda; Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Brussels, Belgium
Ghisbain, Guillaume ; Université de Mons - UMONS > Faculté des Sciences > Service de Zoologie ; Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Brussels, Belgium
Faber, Mirko; Department for Infectious Disease Epidemiology, Robert Koch-Institute, Berlin, Germany
Reusken, Chantal; Department Virology, Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
Sauvage, Virginie; Université Paris Cité, Unité Environnement et Risques Infectieux, Centre National de Référence des Hantavirus, Institut Pasteur, Paris, France
Wint, William; Department of Biology, Environmental Research Group Oxford Ltd, Oxford, UK
Leirs, Herwig; Evolutionary Ecology Group, University of Antwerp, Antwerp, Belgium
Dellicour, Simon; Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Brussels, Belgium ; Laboratory for Clinical and Epidemiological Virology, Rega Institute, Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium
Tersago, Katrien; Evolutionary Ecology Group, University of Antwerp, Antwerp, Belgium ; Epidemiology Unit, Scientific Institute of Public Health, Brussels, Belgium
Language :
English
Title :
Impact of Environmental Factors on the Distribution Patterns of Nephropathia Epidemica Cases in Western Europe.
Publication date :
May 2025
Journal title :
Environmental Health Perspectives
ISSN :
0091-6765
Publisher :
Public Health Services, US Dept of Health and Human Services, United States
We are grateful to two anonymous reviewers for their constructive and helpful comments, as well as to Markus Neteler, Els Ducheyne, Luigi Sedda, and David Rogers for their support in the initial phase of the project. We also acknowledge all persons involved in the data collection conducted by a) the Belgian network of sentinel laboratories and the service Epidemiology of Infectious Diseases at Sciensano, the National Public Health Institute of Belgium; b) Robert Koch Institute, Department for Infectious Disease Epidemiology in Germany; c) the Virology Department, Centre for Infectious Disease Control, National Institute for Public Health and the Environment in The Netherlands; and d) the Centre National de R\u00E9f\u00E9rence des Hantavirus, Unit\u00E9 de Environnement et Risques Infectieux at Institut Pasteur in France. D.E. acknowledges support from the European Union\u2019s Horizon 2020 research and innovation program under the Marie Sk\u0142odowska Curie grant agreement number 801505 and from the Fonds National de la Recherche Scientifique (F.R.S.-FNRS, Belgium). H.L., K.T., and W.W. acknowledge support from the EDENext project (Biology and control of vector-borne infections in Europe; EU grant FP7261504 EDENext). S.D. acknowledges support from the Fonds National de la Recherche Scientifique (F.R.S.-FNRS, Belgium; grant number F.4515.22) from the Research Foundation\u2014Flanders (Fonds voor Wetenschappelijk Onderzoek \u2014 Vlaanderen, FWO, Belgium; grant number G098321N) and from the European Union Horizon 2020 projects MOOD (grant agreement number 874850) and LEAPS (grant agreement number 101094685).
Kilpatrick AM, Randolph SE. 2012. Drivers, dynamics, and control of emerging vector-borne zoonotic diseases. Lancet 380(9857):1946–1955, PMID: 23200503, https://doi.org/10.1016/S0140-6736(12)61151-9.
Estrada-Peña A, Ostfeld RS, Peterson AT, Poulin R, De La Fuente J. 2014. Effects of environmental change on zoonotic disease risk: an ecological primer. Trends Parasitol 30(4):205–214, PMID: 24636356, https://doi.org/10. 1016/j.pt.2014.02.003.
Altizer S, Dobson A, Hosseini P, Hudson P, Pascual M, Rohani P. 2006. Seasonality and the dynamics of infectious diseases. Ecol Lett 9(4):467–484, PMID: 16623732, https://doi.org/10.1111/j.1461-0248.2005.00879.x.
Gottdenker NL, Streicker DG, Faust CL, Carroll CR. 2014. Anthropogenic land use change and infectious diseases: a review of the evidence. EcoHealth 11(4):619–632, PMID: 24854248, https://doi.org/10.1007/s10393-014-0941-z.
Altizer S, Ostfeld RS, Johnson PTJ, Kutz S, Harvell CD. 2013. Climate change and infectious diseases: from evidence to a predictive framework. Science 341(6145):514–519, PMID: 23908230, https://doi.org/10.1126/science.1239401.
Lafferty KD. 2009. The ecology of climate change and infectious diseases. Ecology 90(4):888–900, PMID: 19449681, https://doi.org/10.1890/08-0079.1.
Morand S, Owers KA, Waret-Szkuta A, McIntyre KM, Baylis M. 2013. Climate variability and outbreaks of infectious diseases in Europe. Sci Rep 3(1):1774, PMID: 23639950, https://doi.org/10.1038/srep01774.
Semenza JC, Sudre B, Oni T, Suk JE, Giesecke J. 2013. Linking environmental drivers to infectious diseases: the European environment and epidemiology network. PLoS Negl Trop Dis 7(7):e2323, PMID: 23936561, https://doi.org/10. 1371/journal.pntd.0002323.
Tersago K, Verhagen R, Servais A, Heyman P, Ducoffre G, Leirs H. 2009. Hantavirus disease (nephropathia epidemica) in Belgium: effects of tree seed production and climate. Epidemiol Infect 137(2):250–256, PMID: 18606026, https://doi.org/10.1017/S0950268808000940.
Metcalf CJE, Walter KS, Wesolowski A, Buckee CO, Shevliakova E, Tatem AJ, et al. 2017. Identifying climate drivers of infectious disease dynamics: recent advances and challenges ahead. Proc R Soc B Biol B 284(1860):20170901, https://doi.org/10.1098/rspb.2017.0901.
Stewart-Ibarra AM, Rollock L, Best S, Brown T, Diaz AR, Dunbar W, et al. 2022. Co-learning during the co-creation of a dengue early warning system for the health sector in Barbados. BMJ Glob Health 7(1):e007842, PMID: 34992079, https://doi.org/10.1136/bmjgh-2021-007842.
Finch E, Lotto Batista M, Alcayna T, Lee SA, Fletcher IK, Lowe R. 2024. Early warning systems for vector-borne diseases: engagement, methods and implementation. In: Planetary Health Approaches to Understand and Control VectorBorne Diseases. Wageningen, the Netherlands: Wageningen Academic, 347– 386.
Mills JN, Gage KL, Khan AS. 2010. Potential influence of climate change on vector-borne and zoonotic diseases: a review and proposed research plan. Environ Health Perspect 118(11):1507–1514, PMID: 20576580, https://doi.org/10. 1289/ehp.0901389.
Dearing MD, Dizney L. 2010. Ecology of hantavirus in a changing world. Ann NY Acad Sci 1195(1):99–112, PMID: 20536819, https://doi.org/10.1111/j.17496632.2010.05452.x.
Kuhn JH, Schmaljohn CS. 2023. A brief history of bunyaviral family Hantaviridae. Diseases 11(1):38, PMID: 36975587, https://doi.org/10.3390/diseases11010038.
Vaheri A, Strandin T, Hepojoki J, Sironen T, Henttonen H, Mäkelä S, et al. 2013. Uncovering the mysteries of hantavirus infections. Nat Rev Microbiol 11(8):539– 550, PMID: 24020072, https://doi.org/10.1038/nrmicro3066.
Yanagihara R, Amyx HL, Gajdusek DC. 1985. Experimental infection with Puumala virus, the etiologic agent of nephropathia epidemica, in bank voles (Clethrionomys glareolus). J Virol 55(1):34–38, PMID: 2861296, https://doi.org/10. 1128/JVI.55.1.34-38.1985.
Kallio ER, Klingström J, Gustafsson E, Manni T, Vaheri A, Henttonen H, et al. 2006. Prolonged survival of Puumala hantavirus outside the host: evidence for indirect transmission via the environment. J Gen Virol 87(pt 8):2127–2134, PMID: 16847107, https://doi.org/10.1099/vir.0.81643-0.
Hardestam J, Karlsson M, Falk KI, Olsson G, Klingström J, Lundkvist Å. 2008. Puumala hantavirus excretion kinetics in bank voles (Myodes glareolus). Emerg Infect Dis 14(8):1209–1215, PMID: 18680643, https://doi.org/10.3201/eid1408.080221.
Settergren B. 2000. Clinical aspects of nephropathia epidemica (Puumala virus infection) in Europe: a review. Scand J Infect Dis 32(2):125–132, PMID: 10826895, https://doi.org/10.1080/003655400750045204.
Vapalahti O, Mustonen J, Lundkvist Å, Henttonen H, Plyusnin A, Vaheri A. 2003. Hantavirus infections in Europe. Lancet Infect Dis 3(10):653–661, PMID: 14522264, https://doi.org/10.1016/s1473-3099(03)00774-6.
Heyman P, Thoma BR, Marié JL, Cochez C, Essbauer SS. 2012. In search for factors that drive hantavirus epidemics. Front Physiol 3:237, PMID: 22934002, https://doi.org/10.3389/fphys.2012.00237.
European Centre for Disease Prevention and Control. 2024. Surveillance Atlas of Infectious Diseases. https://atlas.ecdc.europa.eu/public/[accessed 1 April 2024].
Reusken C, Heyman P. 2013. Factors driving hantavirus emergence in Europe. Curr Opin Virol 3(1):92–99, PMID: 23384818, https://doi.org/10.1016/j.coviro.2013.01.002.
Reil D, Imholt C, Eccard JA, Jacob J. 2015. Beech fructification and bank vole population dynamics-combined analyses of promoters of human Puumala virus infections in Germany. PLoS One 10(7):e0134124, PMID: 26214509, https://doi.org/10.1371/journal.pone.0134124.
Andreassen HP, Sundell J, Ecke F, Halle S, Haapakoski M, Henttonen H, et al. 2021. Population cycles and outbreaks of small rodents: ten essential questions we still need to solve. Oecologia 195(3):601–622, PMID: 33369695, https://doi.org/10.1007/s00442-020-04810-w.
Hansson L, Henttonen H. 1985. Gradients in density variations of small rodents: the importance of latitude and snow cover. Oecologia 67(3):394–402, PMID: 28311574, https://doi.org/10.1007/BF00384946.
Olsson GE, Leirs H, Henttonen H. 2010. Hantaviruses and their hosts in Europe: reservoirs here and there, but not everywhere? Vector Borne Zoonotic Dis 10(6):549–561, PMID: 20795916, https://doi.org/10.1089/vbz.2009.0138.
Imholt C, Reil D, Eccard JA, Jacob D, Hempelmann N, Jacob J. 2015. Quantifying the past and future impact of climate on outbreak patterns of bank voles (Myodes glareolus). Pest Manag Sci 71(2):166–172, PMID: 24889216, https://doi.org/10.1002/ps.3838.
Cunze S, Kochmann J, Kuhn T, Frank R, Dörge DD, Klimpel S. 2018. Spatial and temporal patterns of human puumala virus (PUUV) infections in Germany. PeerJ 6:e4255, PMID: 29404206, https://doi.org/10.7717/peerj.4255.
Clement J, Maes P, Van Ranst M. 2006. Hantaviruses in the old and new world. Perspect Med Virol 16:161–177, https://doi.org/10.1016/S0168-7069(06)16008-5.
Clement J, Vercauteren J, Verstraeten WW, Ducoffre G, Barrios JM, Vandamme A-M, et al. 2009. Relating increasing hantavirus incidences to the changing climate: the mast connection. Int J Health Geogr 8:1, PMID: 19149870, https://doi.org/10.1186/1476-072X-8-1.
Piovesan G, Adams JM. 2001. Masting behaviour in beech: linking reproduction and climatic variation. Can J Bot 79(9):1039–1047.
Smyth M. 1966. Winter breeding in woodland mice, Apodemus sylvaticus, and voles, Clethrionomys glareolus and Microtus agrestis, near Oxford. J Anim Ecol 35(3):471–485, https://doi.org/10.2307/2486.
Ylonen H, Viitala J. 1985. Social organization of an enclosed winter population of the bank vole Clethrionomys glareolus. Ann Zool Fennici 22(3):353–358.
Jensen TS. 1982. Seed production and outbreaks of non-cyclic rodent populations in deciduous forests. Oecologia 54(2):184–192, PMID: 28311427, https://doi.org/10. 1007/BF00378391.
Pucek Z, JeR drzejewski W, JeR drzejewska B, Pucek M. 1993. Rodent population dynamics in a primeval deciduous Forest (Białowie_za National Park) in relation to weather, seed crop, and predation. Acta Theriol 38:199–232, https://doi.org/10.4098/AT.arch.93-18.
Crespin L, Verhagen R, Stenseth NC, Yoccoz NG, Prévot-Julliard A, Lebreton J. 2002. Survival in fluctuating bank vole populations: seasonal and yearly variations. Oikos 98(3):467–479, https://doi.org/10.1034/j.1600-0706.2002.980311.x.
Sipari S, Khalil H, Magnusson M, Evander M, Hörnfeldt B, Ecke F. 2022. Climate change accelerates winter transmission of a zoonotic pathogen. Ambio 51(3):508–517, PMID: 34228253, https://doi.org/10.1007/s13280-021-01594-y.
Sauvage F, Penalba C, Vuillaume P, Boue F, Coudrier D, Pontier D, et al. 2002. Infection in humans and in the reservoir host, Ardennes region, France. Emerg Infect Dis 8(12):1509–1511, PMID: 12498675, https://doi.org/10.3201/eid0812.010518.
Huston MA, Wolverton S. 2009. The global distribution of net primary production: resolving the paradox. Ecol Monogr 79(3):343–377, https://doi.org/10.1890/08-0588.1.
Loehman RA, Elias J, Douglass RJ, Kuenzi AJ, Mills JN, Wagoner K. 2012. Prediction of Peromyscus maniculatus (deer mouse) population dynamics in Montana, USA, using satellite-driven vegetation productivity and weather data. J Wildl Dis 48(2):348–360, PMID: 22493110, https://doi.org/10.7589/00903558-48.2.348.
Barrios JM, Verstraeten WW, Maes P, Clement J, Aerts J-M, Haredasht SA, et al. 2010. Satellite derived Forest phenology and its relation with nephropathia epidemica in Belgium. Int J Environ Res Public Health 7(6):2486–2500, PMID: 20644685, https://doi.org/10.3390/ijerph7062486.
Barrios JM, Verstraeten WW, Maes P, Aerts JM, Farifteh J, Coppin P. 2013. Relating land cover and spatial distribution of nephropathia epidemica and Lyme borreliosis in Belgium. Int J Environ Health Res 23(2):132–154, PMID: 22894742, https://doi.org/10.1080/09603123.2012.708918.
Ganguly S, Friedl MA, Tan B, Zhang X, Verma M. 2010. Land surface phenology from MODIS: characterization of the collection 5 global land cover dynamics product. Remote Sens Environ 114(8):1805–1816, https://doi.org/10.1016/j.rse.2010.04.005.
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, et al. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol Evol 24(3):127–135, PMID: 19185386, https://doi.org/10.1016/j.tree.2008.10.008.
Bujalska G. 1990. Social system of the bank vole, Clethrionomys glareolus. In: Social Systems and Population Cycles in Voles. Tamarin RH, Ostfeld RS, Pugh SR, Bujalska G, eds. Basel, Switzerland: Birkhäuser, 155–167.
Spitzenberger F. 2000. Clethrionomys glareolus. In: The Atlas of European Mammals, vol. 75. Cambridge, MA: Academic Press.
Alain B, Gilles P, Yannick D. 2006. Factors driving small rodents assemblages from field boundaries in agricultural landscapes of Western France. Landscape Ecol 21(3):449–461, https://doi.org/10.1007/s10980-005-4118-6.
Gliwicz J, Ims RA. 2000. Dispersal in the bank vole. Polish J Ecol 48:51–61.
Kozakiewicz M, Chołuj A, Kozakiewicz A. 2007. Long-distance movements of individuals in a free-living bank vole population: an important element of male breeding strategy. Acta Theriol 52(4):339–348, https://doi.org/10.1007/BF03194231.
Mazurkiewicz M. 1994. Factors influencing the distribution of the bank vole in Forest habitats. Acta Theriol 39(2):113–126, https://doi.org/10.4098/AT.arch.94-16.
Van Apeldoorn RC, Oostenbrink WT, Van Winden A, Van Der Zee FF. 1992. Effects of habitat fragmentation on the bank vole, Clethrionomys glareolus, in an agricultural landscape. Oikos 65(2):265–274, https://doi.org/10.2307/3545018.
Birkedal M, Fischer A, Karlsson M, Löf M, Madsen P. 2009. Rodent impact on establishment of direct-seeded Fagus sylvatica, Quercus robur and Quercus petraea on forest land. Scand J For Res 24(4):298–307, https://doi.org/10. 1080/02827580903055125.
Shrestha H, McCulloch K, Hedtke SM, Grant WN. 2022. Geospatial modeling of pre-intervention nodule prevalence of Onchocerca volvulus in Ethiopia as an aid to onchocerciasis elimination. PLoS Negl Trop Dis 16(7):e0010620, PMID: 35849615, https://doi.org/10.1371/journal.pntd.0010620.
Moraga P, Cano J, Baggaley RF, Gyapong JO, Njenga SM, Nikolay B, et al. 2015. Modelling the distribution and transmission intensity of lymphatic filariasis in sub-Saharan Africa prior to scaling up interventions: integrated use of geostatistical and mathematical modelling. Parasit Vectors 8(1):560, PMID: 26496983, https://doi.org/10.1186/s13071-015-1166-x.
Kang SY, Battle KE, Gibson HS, Ratsimbasoa A, Randrianarivelojosia M, Ramboarina S, et al. 2018. Spatio-temporal mapping of Madagascar’s malaria indicator survey results to assess Plasmodium falciparum endemicity trends between 2011 and 2016. BMC Med 16(1):71, PMID: 29788968.
Blangiardo M, Cameletti M. 2015. Spatial and Spatio-temporal Bayesian Models with R-INLA. 1st ed. Hoboken, NJ: Wiley.
Rue H, Martino S, Chopin N. 2009. Approximate Bayesian inference for latent gaussian models by using integrated nested Laplace approximations. J R Stat Soc Ser B Stat Methodol 71(2):319–392, https://doi.org/10.1111/j.1467-9868.2008.00700.x.
Karagiannis-Voules DA, Scholte RGC, Guimarães LH, Utzinger J, Vounatsou P. 2013. Bayesian geostatistical modeling of Leishmaniasis incidence in Brazil. PLoS Negl Trop Dis 7(5):e2213, https://doi.org/10.1371/journal.pntd.0002213.
Osgood-Zimmerman A, Millear AI, Stubbs RW, Shields C, Pickering BV, Earl L, et al. 2018. Mapping child growth failure in Africa between 2000 and 2015. Nature 555(7694):41–47, PMID: 29493591, https://doi.org/10.1038/nature25760.
Martins TG, Simpson D, Lindgren F, Rue H. 2013. Bayesian computing with INLA: new features. Comput Stat Data Anal 67:68–83, https://doi.org/10.1016/j.csda.2013.04.014.
Moraga P. 2019. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny. 1st ed. Boca Raton, FL: Chapman and Hall/CRC.
Nyandwi E, Osei FB, Veldkamp T, Amer S. 2020. Modeling schistosomiasis spatial risk dynamics over time in Rwanda using zero-inflated Poisson regression. Sci Rep 10(1):19276, PMID: 33159143, https://doi.org/10.1038/s41598-020-76288-8.
Lindgren F, Rue H, Lindström J. 2011. An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. J R Stat Soc Ser B Stat Methodol 73(4):423–498, https://doi.org/10. 1111/j.1467-9868.2011.00777.x.
Cameletti M, Lindgren F, Simpson D, Rue H. 2013. Spatio-temporal modeling of particulate matter concentration through the SPDE approach. AStA Adv Stat Anal 97(2):109–131, https://doi.org/10.1007/s10182-012-0196-3.
Zuur AF, Ieno EN, Saveliev AA. 2018. GAM and Zero-Inflated Models. Newburgh, United Kingdom: Highland Statistics Ltd.
Moraga P, Dean C, Inoue J, Morawiecki P, Noureen SR, Wang F. 2021. Bayesian spatial modelling of geostatistical data using INLA and SPDE methods: a case study predicting malaria risk in Mozambique. Spat Spatiotemporal Epidemiol 39:100440, PMID: 34774255, https://doi.org/10.1016/j.sste.2021.100440.
Leach CB, Webb CT, Cross PC. 2016. When environmentally persistent pathogens transform good habitat into ecological traps. R Soc Open Sci 3(3):160051, PMID: 27069672, https://doi.org/10.1098/rsos.160051.
Dougherty ER, Seidel DP, Blackburn JK, Turner WC, Getz WM. 2022. A framework for integrating inferred movement behavior into disease risk models. Mov Ecol 10(1):31, PMID: 35871637, https://doi.org/10.1186/s40462-022-00331-8.
Kazasidis O, Geduhn A, Jacob J. 2024. High-resolution early warning system for human Puumala hantavirus infection risk in Germany. Sci Rep 14(1):9602, PMID: 38671000, https://doi.org/10.1038/s41598-024-60144-0.
Swart A, Bekker DL, Maas M, de Vries A, Pijnacker R, Reusken CBEM, et al. 2017. Modelling human Puumala hantavirus infection in relation to bank vole abundance and masting intensity in the Netherlands. Infect Ecol Epidemiol 7(1):1287986, PMID: 28567209, https://doi.org/10.1080/20008686.2017.1287986.
Sauvage F, Langlais M, Pontier D. 2007. Predicting the emergence of human hantavirus disease using a combination of viral dynamics and rodent demographic patterns. Epidemiol Infect 135(1):46–56, PMID: 16753079, https://doi.org/10.1017/S0950268806006595.
Tersago K, Verhagen R, Vapalahti O, Heyman P, Ducoffre G, Leirs H. 2011. Hantavirus outbreak in Western Europe: reservoir host infection dynamics related to human disease patterns. Epidemiol Infect 139(3):381–390, PMID: 20450527, https://doi.org/10.1017/S0950268810000956.
Tersago K, Schreurs A, Linard C, Verhagen R, Van Dongen S, Leirs H. 2008. Population, environmental, and community effects on local bank vole (Myodes glareolus) Puumala virus infection in an area with low human incidence. Vector Borne Zoonotic Dis 8(2):235–244, PMID: 18370592, https://doi.org/10. 1089/vbz.2007.0160.
Piechotowski I, Brockmann SO, Schwarz C, Winter CH, Ranft U, Pfaff G. 2008. Emergence of hantavirus in South Germany: rodents, climate and human infections. Parasitol Res 103(suppl 1):131–137, PMID: 19030895, https://doi.org/10.1007/s00436-008-1055-8.
Faber M, Wollny T, Schlegel M, Wanka KM, Thiel J, Frank C, et al. 2013. Puumala virus outbreak in Western Thuringia, Germany, 2010: epidemiology and strain identification. Zoonoses Public Health 60(8):549–554, PMID: 23398736, https://doi.org/10. 1111/zph.12037.
Ettinger J, Hofmann J, Enders M, Tewald F, Oehme RM, Rosenfeld UM, et al. 2012. Multiple synchronous outbreaks of Puumala virus, Germany, 2010. Emerg Infect Dis 18(9):1461–1464, PMID: 22932394, https://doi.org/10.3201/eid1809.111447.
Gurnell J. 1993. Tree seed production and food conditions for rodents in an oak wood in Southern England. Forestry 66(3):291–315, https://doi.org/10.1093/forestry/66.3.291.
Schauber EM, Kelly D, Turchin P, Simon C, Lee WG, Allen RB, et al. 2002. Masting by eighteen New Zealand plant species: the role of temperature as a synchronizing cue. Ecology 83(5):1214–1225.
Klempa B. 2009. Hantaviruses and climate change. Clin Microbiol Infect 15(6):518–523, PMID: 19604276, https://doi.org/10.1111/j.1469-0691.2009.02848.x.
Linard C, Lamarque P, Heyman P, Ducoffre G, Luyasu V, Tersago K, et al. 2007. Determinants of the geographic distribution of Puumala virus and Lyme borreliosis infections in Belgium. Int J Health Geogr 6(1):15, PMID: 17474974, https://doi.org/10.1186/1476-072X-6-15.
Linard C, Tersago K, Leirs H, Lambin EF. 2007. Environmental conditions and Puumala virus transmission in Belgium. Int J Health Geogr 6(1):55, PMID: 18078526, https://doi.org/10.1186/1476-072X-6-55.
Van Loock F, Thomas I, Clement J, Ghoos S, Colson P. 1999. A case-control study after a hantavirus infection outbreak in the south of Belgium: who is at risk? Clin Infect Dis 28(4):834–839, PMID: 10825047, https://doi.org/10. 1086/515196.
Abu Sin M, Stark K, van Treeck U, Dieckmann H, Uphoff H, Hautmann W, et al. 2007. Risk factors for hantavirus infection in Germany, 2005. Emerg Infect Dis 13(9):1364–1366, PMID: 18252110, https://doi.org/10.3201/eid1309.070552.
Haredasht SA, Taylor CJ, Maes P, Verstraeten WW, Clement J, Barrios M, et al. 2013. Model-based prediction of nephropathia epidemica outbreaks based on climatological and vegetation data and bank vole population dynamics. Zoonoses Public Health 60(7):461–477, PMID: 23176630, https://doi.org/10. 1111/zph.12021.
Vanwambeke SO, Zeimes CB, Drewes S, Ulrich RG, Reil D, Jacob J. 2019. Spatial dynamics of a zoonotic orthohantavirus disease through heterogenous data on rodents, rodent infections, and human disease. Sci Rep 9(1):2329, PMID: 30787344, https://doi.org/10.1038/s41598-019-38802-5.