in

Rodent host population dynamics drive zoonotic Lyme Borreliosis and Orthohantavirus infections in humans in Northern Europe

  • 1.

    Taylor, L. H., Latham, S. M. & Woolhouse, M. E. J. Risk factors for human disease emergence. Philos. Trans. R. Soc. B Biol. Sci. 356, 983–989 (2001).

    CAS 
    Article 

    Google Scholar 

  • 2.

    Karesh, W. B. et al. Ecology of zoonoses: Natural and unnatural histories. Lancet 380, 1936–1945 (2012).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 3.

    Jones, K. E. et al. Global trends in emerging infectious diseases. Nature 451, 990–993 (2008).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 4.

    Zhang, T., Wu, Q. & Zhang, Z. Probable pangolin origin of SARS-CoV-2 associated with the COVID-19 outbreak. Curr. Biol. 30, 1346-1351.e2 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 5.

    Lu, R. et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: Implications for virus origins and receptor binding. Lancet 395, 565–574 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 6.

    Morse, S. S. et al. Prediction and prevention of the next pandemic zoonosis. Lancet 380, 1956–1965 (2012).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 7.

    Han, B. A., Schmidt, J. P., Bowden, S. E. & Drake, J. M. Rodent reservoirs of future zoonotic diseases. Proc. Natl. Acad. Sci. 112, 7039–7044 (2015).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 8.

    Mollentze, N. & Streicker, D. G. Viral zoonotic risk is homogenous among taxonomic orders of mammalian and avian reservoir hosts. Proc. Natl. Acad. Sci. 117, 9423 LP – 9430 (2020).

    Article 
    CAS 

    Google Scholar 

  • 9.

    Luis, A. D. et al. A comparison of bats and rodents as reservoirs of zoonotic viruses: Are bats special?. Proc. R. Soc. B Biol. Sci. 280, 20122753 (2013).

    Article 

    Google Scholar 

  • 10.

    Wardeh, M., Sharkey, K. J. & Baylis, M. Integration of shared-pathogen networks and machine learning reveals the key aspects of zoonoses and predicts mammalian reservoirs. Proc. R. Soc. B Biol. Sci. 287, 20192882 (2020).

    CAS 
    Article 

    Google Scholar 

  • 11.

    Maes, P. et al. Taxonomy of the order Bunyavirales: Second update 2018. Arch. Virol. 164, 927–941 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 12.

    Vapalahti, K., Virtala, A.-M., Vaheri, A. & Vapalahti, O. Case-control study on Puumala virus infection: Smoking is a risk factor. Epidemiol. Infect. 138, 576–584 (2010).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 13.

    Vaheri, A. et al. Hantavirus infections in Europe and their impact on public health. Rev. Med. Virol. 23, 35–49 (2013).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 14.

    Avšič-Županc, T., Saksida, A. & Korva, M. Hantavirus infections. Clin. Microbiol. Infect. 21, e6–e16 (2019).

    Article 

    Google Scholar 

  • 15.

    Olsson, G. E., Leirs, H. & Henttonen, H. Hantaviruses and their hosts in Europe: Reservoirs here and there, but not everywhere?. Vector-Borne Zoonotic Dis. 10, 549–561 (2010).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 16.

    Cook, M. J. Lyme borreliosis: A review of data on transmission time after tick attachment. Int. J. Gen. Med. 8, 1–8 (2014).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 17.

    Sykes, R. A. & Makiello, P. An estimate of Lyme borreliosis incidence in Western Europe†. J. Public Health (Bangkok) 39, 74–81 (2016).

    Google Scholar 

  • 18.

    Kuehn, B. M. CDC estimates 300000 US cases of lyme disease annually. JAMA J. Am. Med. Assoc. 310, 1110 (2013).

    CAS 
    Article 

    Google Scholar 

  • 19.

    Davis, S., Calvet, E. & Leirs, H. Review fluctuating rodent populations and risk to humans from rodent-borne zoonoses. Vector-Borne Zoonotic Dis. 5, 305–314 (2005).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 20.

    Tian, H. Y. et al. Changes in rodent abundance and weather conditions potentially drive hemorrhagic fever with renal syndrome outbreaks in Xi’an, China, 2005–2012. PLoS Negl. Trop. Dis. 9, 2005–2012 (2015).

    Article 
    CAS 

    Google Scholar 

  • 21.

    Kallio, E. R. et al. Cyclic hantavirus epidemics in humans: Predicted by rodent host dynamics. Epidemics 1, 101–107 (2009).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 22.

    Olival, K. J. et al. Host and viral traits predict zoonotic spillover from mammals. Nature 546, 646–650 (2017).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 23.

    Korpela, K. et al. Predator–vole interactions in northern Europe: The role of small mustelids revised. Proc. R. Soc. B Biol. Sci. 281, 20142119 (2014).

    Article 

    Google Scholar 

  • 24.

    Korpimäki, E., Norrdahl, K., Huitu, O. & Klemola, T. Predator-induced synchrony in population oscillations of coexisting small mammal species. Proc. R. Soc. B Biol. Sci. 272, 193–202 (2005).

    Article 

    Google Scholar 

  • 25.

    Hanski, I., Henttonen, H., Korpimäki, E., Oksanen, L. & Turchin, P. Small-rodent dynamics and predation. Ecology 82, 1505–1520 (2001).

    Article 

    Google Scholar 

  • 26.

    Hansson, L. & Henttonen, H. Rodent dynamics as community processes. Trends Ecol. Evol. 3, 195–200 (1988).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 27.

    Sane, J. et al. Regional differences in long-term cycles and seasonality of Puumala virus infections, Finland, 1995–2014. Epidemiol. Infect. 144, 2883–2888 (2016).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 28.

    Vapalahti, O. et al. Hantavirus infections in Europe. Lancet Infect. Dis. 3, 653–661 (2003).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 29.

    Olsson, G. E., Hjertqvist, M., Lundkvist, Å. & Hörnfeldt, B. Predicting high risk for human hantavirus infections, Sweden. Emerg. Infect. Dis. 15, 104–106 (2009).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 30.

    Khalil, H., Ecke, F., Evander, M., Bucht, G. & Hörnfeldt, B. Population dynamics of bank voles predicts human puumala hantavirus risk. EcoHealth 16, 545–557 (2019).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 31.

    Jones, C. G., Ostfeld, R. S., Richard, M. P., Schauber, E. M. & Wolff, J. O. Chain reactions linking acorns to gypsy moth outbreaks and Lyme disease risk. Science 279, 1023–1026 (1998).

    ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 32.

    LoGiudice, K., Ostfeld, R. S., Schmidt, K. A. & Keesing, F. The ecology of infectious disease: Effects of host diversity and community composition on lyme disease risk. Proc. Natl. Acad. Sci. U. S. A. 100, 567–571 (2003).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 33.

    Ostfeld, R. S., Canham, C. D., Oggenfuss, K., Winchcombe, R. J. & Keesing, F. Climate, deer, rodents, and acorns as determinants of variation in Lyme-disease risk. PLoS Biol. 4, 1058–1068 (2006).

    CAS 
    Article 

    Google Scholar 

  • 34.

    Van Duijvendijk, G., Sprong, H. & Takken, W. Multi-trophic interactions driving the transmission cycle of Borrelia afzelii between Ixodes ricinus and rodents: A review. Parasit. Vectors 8, 1 (2015).

    Article 
    CAS 

    Google Scholar 

  • 35.

    Krawczyk, A. I. et al. Effect of rodent density on tick and tick-borne pathogen populations: Consequences for infectious disease risk. Parasit. Vectors 13, 34 (2020).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 36.

    Bregnard, C., Rais, O. & Voordouw, M. J. Climate and tree seed production predict the abundance of the European Lyme disease vector over a 15-year period. Parasit. Vectors 13, 1–12 (2020).

    Article 

    Google Scholar 

  • 37.

    Bregnard, C., Rais, O. & Voordouw, M. J. Masting by beech trees predicts the risk of Lyme disease. Parasit. Vectors 14, 1–22 (2021).

    Article 
    CAS 

    Google Scholar 

  • 38.

    Schauber, E. M., Ostfeld, R. S. & Evans, A. S. What is the best predictor of annual lyme disease incidence: Weather, mice, or acorns?. Ecol. Appl. 15, 575–586 (2005).

    Article 

    Google Scholar 

  • 39.

    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. 25, 1738–1741 (2019).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 40.

    Bogdziewicz, M. & Szymkowiak, J. Oak acorn crop and Google search volume predict Lyme disease risk in temperate Europe. Basic Appl. Ecol. 17, 300–307 (2016).

    Article 

    Google Scholar 

  • 41.

    Pietiäinen, H., Sundell, J., Valkama, J. & Huitu, O. vole interactions in northern Europe: The role of− Predator. (2014).

  • 42.

    Lindgren, E. & Jaenson, T. G. T. Lyme borreliosis in Europe: Influences of climate and climate change, epidemiology, ecology and adaptation measures. World Heal. Org. https://doi.org/10.1093/ntr/ntu261 (2006).

    Article 

    Google Scholar 

  • 43.

    Laaksonen, M. et al. Tick-borne pathogens in Finland: Comparison of Ixodes ricinus and I. persulcatus in sympatric and parapatric areas. Parasit. Vectors 11, 556 (2018).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 44.

    Sajanti, E. et al. Lyme borreliosis in Finland in 1995–2014. Emerg. Infect. Dis. 23, 128–1288 (2017).

    Article 

    Google Scholar 

  • 45.

    Amori, G. et al. Myodes glareolus. In:The IUCN Red List of Threatened Species. (2007) (accessed 28 February 2020). https://www.iucnredlist.org/species/4973/11105168

  • 46.

    Brummer-Korvenkontio, M. et al. Nephropathia epidemica: Detection of antigen in bank voles and serologic diagnosis of human infection. J. Infect. Dis. 141, 131–134 (1980).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 47.

    Kurtenbach, K. et al. Fundamental processes in the evolutionary ecology of Lyme borreliosis. Nat. Rev. Microbiol. 4, 660–669 (2006).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 48.

    Hanincová, K. et al. Association of Borrelia afzelii with rodents in Europe. Parasitology 126, 11–20 (2003).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 49.

    Tälleklint, L., Jaenson, T. G. T. & Mather, T. N. Seasonal variation in the capacity of the bank vole to infect larval ticks (Acari: Ixodidae) with the lyme disease spirochete, Borrelia burgdorferi. J. Med. Entomol. 30, 812–815 (1993).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 50.

    Gern, L. et al. European reservoir hosts of Borrelia burgdorferi sensu lato. Zentralblatt fur Bakteriol. 287, 196–204 (1998).

    CAS 
    Article 

    Google Scholar 

  • 51.

    Tersago, K. et al. Hantavirus outbreak in Western Europe: Reservoir host infection dynamics related to human disease patterns. Epidemiol. Infect. 139, 381–390 (2011).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 52.

    Jaenson, T. G. T., Hjertqvist, M., Bergström, T. & Lundkvist, Å. Why is tick-borne encephalitis increasing? A review of the key factors causing the increasing incidence of human TBE in Swedena. Parasit. Vectors 5, 184 (2012).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 53.

    Kurokawa, C. et al. Interactions between Borrelia burgdorferi and ticks. Nat. Rev. Microbiol. 18, 587–600 (2020).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 54.

    Korpela, K. et al. Nonlinear effects of climate on boreal rodent dynamics: Mild winters do not negate high-amplitude cycles. Glob. Chang. Biol. 19, 697–710 (2013).

    ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 55.

    Koivula, M., Koskela, E., Mappes, T. & Oksanen, T. A. Cost of reproduction in the wild: Manipulation of reproductive effort in the bank vole. Ecology 84, 398–405 (2003).

    Article 

    Google Scholar 

  • 56.

    Cayol, C., Koskela, E., Mappes, T., Siukkola, A. & Kallio, E. R. Temporal dynamics of the tick Ixodes ricinus in northern Europe: Epidemiological implications. Parasit. Vectors 10, 1–11 (2017).

    Article 

    Google Scholar 

  • 57.

    Rösch, A. & Schmidbauer, H. WaveletComp: Computational Wavelet Analysis. R package version 1.1. (2018).

  • 58.

    R Core Team. R: A Language and Environment for Statistical Computing. R Found. Stat. Comput. Vienna, Austria (2019).

  • 59.

    Cazelles, B., Chavez, M., De Magny, G. C., Guégan, J. F. & Hales, S. Time-dependent spectral analysis of epidemiological time-series with wavelets. J. R. Soc. Interface 4, 625–636 (2007).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 60.

    Pinherio, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. nlme: Linear and nonlinear mixed effects models. R package Version 3. 1–142 (2019).

  • 61.

    Ostfeld, R. S. et al. Effects of acorn production and mouse abundance on abundance and Borrelia burgdorferi infection prevalence of nymphal Ixodes scapularis ticks. Vector Borne Zoonotic Dis. 1, 55–63 (2001).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 62.

    Kallio, E. R. et al. Prolonged survival of Puumala hantavirus outside the host: Evidence for indirect transmission via the environment. J. Gen. Virol. 87, 2127–2134 (2006).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 63.

    Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference, A Practical Information-Theoretic Approach 2nd edn. (Springer, 2002).https://doi.org/10.1007/978-0-387-22456-5_7 .

    Book 
    MATH 

    Google Scholar 

  • 64.

    Barton, K. MuMIn: Multi-Model Inference. (2019).

  • 65.

    Hyndman, R. J. & Khandakar, Y. Automatic time series forecasting: The forecast package for R. J. Stat. Softw. 26, 1–22 (2008).

    Google Scholar 

  • 66.

    Estrada-Peña, A., Gray, J. S., Kahl, O., Lane, R. S. & Nijhof, A. M. Research on the ecology of ticks and tick-borne pathogens-methodological principles and caveats. Front. Cell. Infect. Microbiol. 4, 1–12 (2013).

    Google Scholar 

  • 67.

    Lindgren, E., Tälleklint, L. & Polfeldt, T. Impact of climatic change on the northern latitude limit and population density of the disease-transmitting European tick Ixodes ricinus. Environ. Health Perspect. 108, 119–123 (2000).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 68.

    Tian, H. et al. Interannual cycles of Hantaan virus outbreaks at the human-animal interface in Central China are controlled by temperature and rainfall. Proc. Natl. Acad. Sci. U. S. A. 114, 8041–8046 (2017).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 69.

    Xiao, H. et al. Atmospheric moisture variability and transmission of hemorrhagic fever with renal syndrome in Changsha City, Mainland China, 1991–2010. PLoS Negl. Trop. Dis. 7, 1–7 (2013).

    CAS 

    Google Scholar 

  • 70.

    Guan, P. et al. Investigating the effects of climatic variables and reservoir on the incidence of hemorrhagic fever with renal syndrome in Huludao City, China: A 17-year data analysis based on structure equation model. BMC Infect. Dis. 9, 1 (2009).

    Article 

    Google Scholar 

  • 71.

    Amirpour Haredasht, S. et al. Modelling seasonal and multi-annual variation in bank vole populations and nephropathia epidemica. Biosyst. Eng. 121, 25–37 (2014).

    Article 

    Google Scholar 

  • 72.

    Hardestam, J. et al. Puumala hantavirus excretion kinetics in bank voles (Myodes glareolus). Emerg. Infect. Dis. 14, 1209–1215 (2008).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 73.

    Levi, T., Kilpatrick, A. M., Mangel, M. & Wilmers, C. C. Deer, predators, and the emergence of Lyme disease. Proc. Natl. Acad. Sci. 109, 10942–10947 (2012).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 74.

    Ostfeld, R. S., Levi, T., Keesing, F., Oggenfuss, K. & Canham, C. D. Tick-borne disease risk in a forest food web. Ecology 99, 1562–1573 (2018).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 75.

    Wilhelmsson, P. et al. Ixodes ricinus ticks removed from humans in Northern Europe: Seasonal pattern of infestation, attachment sites and duration of feeding. Parasit. Vectors 6, 362 (2013).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 76.

    Radolf, J. D., Caimano, M. J., Stevenson, B. & Hu, L. T. Of ticks, mice and men: Understanding the dual-host lifestyle of Lyme disease spirochaetes. Nat. Rev. Microbiol. 10, 87–99 (2012).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 77.

    Otranto, D. et al. Ticks infesting humans in Italy and associated pathogens. Parasit. Vectors 7, 328 (2014).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 78.

    Faulde, M. K. et al. Human tick infestation pattern, tick-bite rate, and associated Borrelia burgdorferi s.l. infection risk during occupational tick exposure at the Seedorf military training area, northwestern Germany. Ticks Tick. Borne. Dis. 5, 594–599 (2014).

    PubMed 
    Article 

    Google Scholar 

  • 79.

    Gustav, T., Jaenson, T., Lundqvist, L., Olsen, B. & Chirico, J. Geographical distribution, host associations, and vector roles of ticks (Acari: Ixodidae, Argasidae) in Sweden mites and insects view project flavivirus view project. Artic. J. Med. Entomol. https://doi.org/10.1093/jmedent/31.2.240 (1994).

    Article 

    Google Scholar 

  • 80.

    Jaenson, T. G. T. et al. First evidence of established populations of the taiga tick Ixodes persulcatus (Acari: Ixodidae) in Sweden. Parasit. Vectors 9, 1–8 (2016).

    Article 
    CAS 

    Google Scholar 

  • 81.

    Jaenson, T. G. T. & Wilhelmsson, P. First records of tick-borne pathogens in populations of the taiga tick Ixodes persulcatus in Sweden. Parasit. Vectors 12, 559 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 82.

    Pakanen, V. M., Sormunen, J. J., Sippola, E., Blomqvist, D. & Kallio, E. R. Questing abundance of adult taiga ticks Ixodes persulcatus and their Borrelia prevalence at the north-western part of their distribution. Parasit. Vectors 13, 384 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 83.

    Laaksonen, M. et al. Crowdsourcing-based nationwide tick collection reveals the distribution of Ixodes ricinus and I. persulcatus and associated pathogens in Finland. Emerg. Microbes Infect. 6, 1–7 (2017).

    Article 
    CAS 

    Google Scholar 

  • 84.

    Kovalevskii, Y. V. & Korenberg, E. I. Differences in Borrelia infections in adult Ixodes persulcatus and Ixodes ricinus ticks (Acari: Ixodidae) in populations of north-western Russia. Exp. Appl. Acarol. 19, 19–29 (1995).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 85.

    Hanski, I., Hansson, L. & Henttonen, H. Specialist predators, generalist predators, and the microtine rodent cycle. J. Anim. Ecol. https://doi.org/10.2307/5465 (1991).

    Article 

    Google Scholar 

  • 86.

    Massey, F., Smith, M., Lambin, X. & Hartley, S. Are silica defences in grasses driving vole population cycles?. Biol. Lett. 4, 419–422 (2008).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 87.

    Kołodziej-Sobocińska, M. Factors affecting the spread of parasites in populations of wild European terrestrial mammals. Mammal Res. 64, 301–318 (2019).

    Article 

    Google Scholar 

  • 88.

    Mysterud, A. et al. Contrasting emergence of Lyme disease across ecosystems. Nat. Commun. 7, 1 (2016).

    Article 
    CAS 

    Google Scholar 

  • 89.

    Rosà, R. & Pugliese, A. Effects of tick population dynamics and host densities on the persistence of tick-borne infections. Math. Biosci. 208, 216–240 (2007).

    MathSciNet 
    PubMed 
    MATH 
    Article 
    PubMed Central 

    Google Scholar 

  • 90.

    Rosà, R., Pugliese, A., Ghosh, M., Perkins, S. E. & 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. 7, 285–295 (2007).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 91.

    Halsey, S. J. & Miller, J. R. Maintenance of Borrelia burgdorferi among vertebrate hosts: A test of dilution effect mechanisms. Ecosphere 11, e03048 (2020).

    Article 

    Google Scholar 

  • 92.

    Ostfeld, R. S. & Keesing, F. Biodiversity and disease risk: The case of Lyme disease. Conserv. Biol. 14, 722–728 (2000).

    Article 

    Google Scholar 

  • 93.

    Murray, T. S. & Shapiro, E. D. Lyme disease. Clin. Lab. Med. 30, 311–328 (2010).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 94.

    Kramski, M., Achazi, K., Klempa, B. & Krüger, D. H. Nephropathia epidemica with a 6-week incubation period after occupational exposure to Puumala hantavirus. J. Clin. Virol. 44, 99–101 (2009).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 95.

    Voutilainen, L., Kallio, E. R., Niemimaa, J., Vapalahti, O. & Henttonen, H. Temporal dynamics of Puumala hantavirus infection in cyclic populations of bank voles. Sci. Rep. 6, 1–15 (2016).

    Article 
    CAS 

    Google Scholar 

  • 96.

    Klemola, T., Korpimaki, E. & Koivula, M. Rate of population change in voles from different phases of the population cycle. Oikos 96, 291–298 (2002).

    Article 

    Google Scholar 


  • Source: Ecology - nature.com

    Gene drives gaining speed

    Principles of seed banks and the emergence of complexity from dormancy