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).
Google Scholar
Karesh, W. B. et al. Ecology of zoonoses: Natural and unnatural histories. Lancet 380, 1936–1945 (2012).
Google Scholar
Jones, K. E. et al. Global trends in emerging infectious diseases. Nature 451, 990–993 (2008).
Google Scholar
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).
Google Scholar
Lu, R. et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: Implications for virus origins and receptor binding. Lancet 395, 565–574 (2020).
Google Scholar
Morse, S. S. et al. Prediction and prevention of the next pandemic zoonosis. Lancet 380, 1956–1965 (2012).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Maes, P. et al. Taxonomy of the order Bunyavirales: Second update 2018. Arch. Virol. 164, 927–941 (2019).
Google Scholar
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).
Google Scholar
Vaheri, A. et al. Hantavirus infections in Europe and their impact on public health. Rev. Med. Virol. 23, 35–49 (2013).
Google Scholar
Avšič-Županc, T., Saksida, A. & Korva, M. Hantavirus infections. Clin. Microbiol. Infect. 21, e6–e16 (2019).
Google Scholar
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).
Google Scholar
Cook, M. J. Lyme borreliosis: A review of data on transmission time after tick attachment. Int. J. Gen. Med. 8, 1–8 (2014).
Google Scholar
Sykes, R. A. & Makiello, P. An estimate of Lyme borreliosis incidence in Western Europe†. J. Public Health (Bangkok) 39, 74–81 (2016).
Kuehn, B. M. CDC estimates 300000 US cases of lyme disease annually. JAMA J. Am. Med. Assoc. 310, 1110 (2013).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Kallio, E. R. et al. Cyclic hantavirus epidemics in humans: Predicted by rodent host dynamics. Epidemics 1, 101–107 (2009).
Google Scholar
Olival, K. J. et al. Host and viral traits predict zoonotic spillover from mammals. Nature 546, 646–650 (2017).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Hanski, I., Henttonen, H., Korpimäki, E., Oksanen, L. & Turchin, P. Small-rodent dynamics and predation. Ecology 82, 1505–1520 (2001).
Google Scholar
Hansson, L. & Henttonen, H. Rodent dynamics as community processes. Trends Ecol. Evol. 3, 195–200 (1988).
Google Scholar
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).
Google Scholar
Vapalahti, O. et al. Hantavirus infections in Europe. Lancet Infect. Dis. 3, 653–661 (2003).
Google Scholar
Olsson, G. E., Hjertqvist, M., Lundkvist, Å. & Hörnfeldt, B. Predicting high risk for human hantavirus infections, Sweden. Emerg. Infect. Dis. 15, 104–106 (2009).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Bregnard, C., Rais, O. & Voordouw, M. J. Masting by beech trees predicts the risk of Lyme disease. Parasit. Vectors 14, 1–22 (2021).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Pietiäinen, H., Sundell, J., Valkama, J. & Huitu, O. vole interactions in northern Europe: The role of− Predator. (2014).
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).
Google Scholar
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).
Google Scholar
Sajanti, E. et al. Lyme borreliosis in Finland in 1995–2014. Emerg. Infect. Dis. 23, 128–1288 (2017).
Google Scholar
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
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).
Google Scholar
Kurtenbach, K. et al. Fundamental processes in the evolutionary ecology of Lyme borreliosis. Nat. Rev. Microbiol. 4, 660–669 (2006).
Google Scholar
Hanincová, K. et al. Association of Borrelia afzelii with rodents in Europe. Parasitology 126, 11–20 (2003).
Google Scholar
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).
Google Scholar
Gern, L. et al. European reservoir hosts of Borrelia burgdorferi sensu lato. Zentralblatt fur Bakteriol. 287, 196–204 (1998).
Google Scholar
Tersago, K. et al. Hantavirus outbreak in Western Europe: Reservoir host infection dynamics related to human disease patterns. Epidemiol. Infect. 139, 381–390 (2011).
Google Scholar
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).
Google Scholar
Kurokawa, C. et al. Interactions between Borrelia burgdorferi and ticks. Nat. Rev. Microbiol. 18, 587–600 (2020).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Rösch, A. & Schmidbauer, H. WaveletComp: Computational Wavelet Analysis. R package version 1.1. (2018).
R Core Team. R: A Language and Environment for Statistical Computing. R Found. Stat. Comput. Vienna, Austria (2019).
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).
Google Scholar
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).
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).
Google Scholar
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).
Google Scholar
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 .
Google Scholar
Barton, K. MuMIn: Multi-Model Inference. (2019).
Hyndman, R. J. & Khandakar, Y. Automatic time series forecasting: The forecast package for R. J. Stat. Softw. 26, 1–22 (2008).
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).
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Amirpour Haredasht, S. et al. Modelling seasonal and multi-annual variation in bank vole populations and nephropathia epidemica. Biosyst. Eng. 121, 25–37 (2014).
Google Scholar
Hardestam, J. et al. Puumala hantavirus excretion kinetics in bank voles (Myodes glareolus). Emerg. Infect. Dis. 14, 1209–1215 (2008).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Otranto, D. et al. Ticks infesting humans in Italy and associated pathogens. Parasit. Vectors 7, 328 (2014).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Massey, F., Smith, M., Lambin, X. & Hartley, S. Are silica defences in grasses driving vole population cycles?. Biol. Lett. 4, 419–422 (2008).
Google Scholar
Kołodziej-Sobocińska, M. Factors affecting the spread of parasites in populations of wild European terrestrial mammals. Mammal Res. 64, 301–318 (2019).
Google Scholar
Mysterud, A. et al. Contrasting emergence of Lyme disease across ecosystems. Nat. Commun. 7, 1 (2016).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Halsey, S. J. & Miller, J. R. Maintenance of Borrelia burgdorferi among vertebrate hosts: A test of dilution effect mechanisms. Ecosphere 11, e03048 (2020).
Google Scholar
Ostfeld, R. S. & Keesing, F. Biodiversity and disease risk: The case of Lyme disease. Conserv. Biol. 14, 722–728 (2000).
Google Scholar
Murray, T. S. & Shapiro, E. D. Lyme disease. Clin. Lab. Med. 30, 311–328 (2010).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Klemola, T., Korpimaki, E. & Koivula, M. Rate of population change in voles from different phases of the population cycle. Oikos 96, 291–298 (2002).
Google Scholar
Source: Ecology - nature.com