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Forecasting risk of...
Forecasting risk of tick-borne encephalitis (TBE): Using data from wildlife and climate to predict next year's number of human victims
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- Haemig, Paul D. (author)
- Linnéuniversitetet,Institutionen för naturvetenskap, NV
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- Sjöstedt de Luna, Sara (author)
- Umeå universitet,Institutionen för matematik och matematisk statistik,Arcum
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- Grafström, Anton (author)
- Umeå universitet,Institutionen för matematik och matematisk statistik
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- Lithner, Stefan (author)
- Linnéuniversitetet,Institutionen för naturvetenskap, NV
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- Lundkvist, Ake (author)
- Karolinska Institutet
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- Waldenström, Jonas (author)
- Linnéuniversitetet,Lund University,Lunds universitet,Biologiska institutionen,Naturvetenskapliga fakulteten,Department of Biology,Faculty of Science,Linneaus University,Institutionen för naturvetenskap, NV,Zoonotic Ecology and Epidemiology
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- Kindberg, Jonas (author)
- Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för vilt, fisk och miljö,Department of Wildlife, Fish and Environmental Studies,Swedish University of Agricultural Sciences, Umeå
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- Stedt, Johan (author)
- Linnéuniversitetet,Institutionen för naturvetenskap, NV
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- Olsen, Björn (author)
- Uppsala universitet,Infektionssjukdomar
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(creator_code:org_t)
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- 2011-01-24
- 2011
- English.
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In: Scandinavian Journal of Infectious Diseases. - : Informa UK Limited. - 1651-1980 .- 0036-5548. ; 43:5, s. 366-372
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Abstract
Subject headings
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- 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.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Infektionsmedicin (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Infectious Medicine (hsv//eng)
- NATURVETENSKAP -- Biologi -- Annan biologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Other Biological Topics (hsv//eng)
- LANTBRUKSVETENSKAPER -- Annan lantbruksvetenskap -- Miljö- och naturvårdsvetenskap (hsv//swe)
- AGRICULTURAL SCIENCES -- Other Agricultural Sciences -- Environmental Sciences related to Agriculture and Land-use (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Andra medicinska och farmaceutiska grundvetenskaper (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Other Basic Medicine (hsv//eng)
- NATURVETENSKAP -- Biologi -- Ekologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Ecology (hsv//eng)
Keyword
- TBE
- tick-borne encephalitis
- tick-borne diseases
- prediction
- forecasting
- early warning
- TBE
- Terrestrial, freshwater and marine ecology
- Virology
Publication and Content Type
- art (subject category)
- ref (subject category)
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