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Forecasting risk of tick-borne encephalitis (TBE): Using data from wildlife and climate to predict next year's number of human victims

Haemig, Paul D.; de Luna, S. Sjostedt; Grafstrom, A.; Lithner, Stefan; Lundkvist, Ake; Waldenström, Jonas LU ; Kindberg, Jonas; Stedt, Johan and Olsen, Bjorn (2011) In Scandinavian Journal of Infectious Diseases 43(5). p.366-372
Abstract
Background: Over the past quarter century, the incidence of tick-borne encephalitis (TBE) has increased in most European nations. However, the number of humans stricken by the disease varies from year to year. A method for predicting major increases and decreases is needed. Methods: We assembled a 25-y database (1984-2008) of the number of human TBE victims and wildlife and climate data for the Stockholm region of Sweden, and used it to create easy-to-use mathematical models that predict increases and decreases in the number of humans stricken by TBE. Results: Our best model, which uses December precipitation and mink (Neovison vison, formerly Mustela vison) bagging figures, successfully predicted every major increase or decrease in TBE... (More)
Background: Over the past quarter century, the incidence of tick-borne encephalitis (TBE) has increased in most European nations. However, the number of humans stricken by the disease varies from year to year. A method for predicting major increases and decreases is needed. Methods: We assembled a 25-y database (1984-2008) of the number of human TBE victims and wildlife and climate data for the Stockholm region of Sweden, and used it to create easy-to-use mathematical models that predict increases and decreases in the number of humans stricken by TBE. Results: Our best model, which uses December precipitation and mink (Neovison vison, formerly Mustela vison) bagging figures, successfully predicted every major increase or decrease in TBE during the past quarter century, with a minimum of false alarms. However, this model was not efficient in predicting small increases and decreases. Conclusions: Predictions from our models can be used to determine when preventive and adaptive programmes should be implemented. For example, in years when the frequency of TBE in humans is predicted to be high, vector control could be intensified where infested ticks have a higher probability of encountering humans, such as at playgrounds, bathing lakes, barbecue areas and camping facilities. Because our models use only wildlife and climate data, they can be used even when the human population is vaccinated. Another advantage is that because our models employ data from previously-established databases, no additional funding for surveillance is required. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
TBE, tick-borne encephalitis, tick-borne diseases, prediction, forecasting, early warning
in
Scandinavian Journal of Infectious Diseases
volume
43
issue
5
pages
366 - 372
publisher
Informa Healthcare
external identifiers
  • wos:000289560500009
  • scopus:79954506817
ISSN
1651-1980
DOI
10.3109/00365548.2011.552072
language
English
LU publication?
yes
id
06640a14-4164-4f01-8f10-47b900dd09f7 (old id 1965176)
date added to LUP
2011-05-23 10:51:11
date last changed
2017-10-01 04:12:15
@article{06640a14-4164-4f01-8f10-47b900dd09f7,
  abstract     = {Background: Over the past quarter century, the incidence of tick-borne encephalitis (TBE) has increased in most European nations. However, the number of humans stricken by the disease varies from year to year. A method for predicting major increases and decreases is needed. Methods: We assembled a 25-y database (1984-2008) of the number of human TBE victims and wildlife and climate data for the Stockholm region of Sweden, and used it to create easy-to-use mathematical models that predict increases and decreases in the number of humans stricken by TBE. Results: Our best model, which uses December precipitation and mink (Neovison vison, formerly Mustela vison) bagging figures, successfully predicted every major increase or decrease in TBE during the past quarter century, with a minimum of false alarms. However, this model was not efficient in predicting small increases and decreases. Conclusions: Predictions from our models can be used to determine when preventive and adaptive programmes should be implemented. For example, in years when the frequency of TBE in humans is predicted to be high, vector control could be intensified where infested ticks have a higher probability of encountering humans, such as at playgrounds, bathing lakes, barbecue areas and camping facilities. Because our models use only wildlife and climate data, they can be used even when the human population is vaccinated. Another advantage is that because our models employ data from previously-established databases, no additional funding for surveillance is required.},
  author       = {Haemig, Paul D. and de Luna, S. Sjostedt and Grafstrom, A. and Lithner, Stefan and Lundkvist, Ake and Waldenström, Jonas and Kindberg, Jonas and Stedt, Johan and Olsen, Bjorn},
  issn         = {1651-1980},
  keyword      = {TBE,tick-borne encephalitis,tick-borne diseases,prediction,forecasting,early warning},
  language     = {eng},
  number       = {5},
  pages        = {366--372},
  publisher    = {Informa Healthcare},
  series       = {Scandinavian Journal of Infectious Diseases},
  title        = {Forecasting risk of tick-borne encephalitis (TBE): Using data from wildlife and climate to predict next year's number of human victims},
  url          = {http://dx.doi.org/10.3109/00365548.2011.552072},
  volume       = {43},
  year         = {2011},
}