Modelling human Puumala hantavirus infection in relation to bank vole abundance and masting intensity in the Netherlands

This paper deals with modelling the relationship between human Puumala hantavirus (PUUV) infection, the abundance and prevalence of infection of the host (the bank vole), mast, and temperature. These data were used to build and parametrise generalised regression models, and parametrise them using da...

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Main Authors: Arno Swart, Dick L. Bekker, Miriam Maas, Ankje de Vries, Roan Pijnacker, Chantal B. E. M. Reusken, Joke W. B. van der Giessen
Format: Article
Language:English
Published: Taylor & Francis Group 2017-01-01
Series:Infection Ecology & Epidemiology
Subjects:
Online Access:http://dx.doi.org/10.1080/20008686.2017.1287986
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spelling doaj-bca2b687bc704eb6a51bcba52133c9d22020-11-24T21:21:36ZengTaylor & Francis GroupInfection Ecology & Epidemiology2000-86862017-01-017110.1080/20008686.2017.12879861287986Modelling human Puumala hantavirus infection in relation to bank vole abundance and masting intensity in the NetherlandsArno Swart0Dick L. Bekker1Miriam Maas2Ankje de Vries3Roan Pijnacker4Chantal B. E. M. Reusken5Joke W. B. van der Giessen6National Institute for Public Health and the EnvironmentDutch Mammal SocietyNational Institute for Public Health and the EnvironmentNational Institute for Public Health and the EnvironmentNational Institute for Public Health and the EnvironmentErasmus University Medical CentreNational Institute for Public Health and the EnvironmentThis paper deals with modelling the relationship between human Puumala hantavirus (PUUV) infection, the abundance and prevalence of infection of the host (the bank vole), mast, and temperature. These data were used to build and parametrise generalised regression models, and parametrise them using datasets on these factors pertaining to the Netherlands. The performance of the models was assessed by considering their predictive power. Models including mast and monthly temperature performed well, and showed that mast intensity influences vole abundance and hence human exposure for the following year. Thus, the model can aid in forecasting of human illness cases, since (1) mast intensity influences the vole abundance and hence human exposure for the following year and (2) monitoring of mast is much more feasible than determining bank vole abundance.http://dx.doi.org/10.1080/20008686.2017.1287986Puumalahuman casespredictionenvironmentclimate
collection DOAJ
language English
format Article
sources DOAJ
author Arno Swart
Dick L. Bekker
Miriam Maas
Ankje de Vries
Roan Pijnacker
Chantal B. E. M. Reusken
Joke W. B. van der Giessen
spellingShingle Arno Swart
Dick L. Bekker
Miriam Maas
Ankje de Vries
Roan Pijnacker
Chantal B. E. M. Reusken
Joke W. B. van der Giessen
Modelling human Puumala hantavirus infection in relation to bank vole abundance and masting intensity in the Netherlands
Infection Ecology & Epidemiology
Puumala
human cases
prediction
environment
climate
author_facet Arno Swart
Dick L. Bekker
Miriam Maas
Ankje de Vries
Roan Pijnacker
Chantal B. E. M. Reusken
Joke W. B. van der Giessen
author_sort Arno Swart
title Modelling human Puumala hantavirus infection in relation to bank vole abundance and masting intensity in the Netherlands
title_short Modelling human Puumala hantavirus infection in relation to bank vole abundance and masting intensity in the Netherlands
title_full Modelling human Puumala hantavirus infection in relation to bank vole abundance and masting intensity in the Netherlands
title_fullStr Modelling human Puumala hantavirus infection in relation to bank vole abundance and masting intensity in the Netherlands
title_full_unstemmed Modelling human Puumala hantavirus infection in relation to bank vole abundance and masting intensity in the Netherlands
title_sort modelling human puumala hantavirus infection in relation to bank vole abundance and masting intensity in the netherlands
publisher Taylor & Francis Group
series Infection Ecology & Epidemiology
issn 2000-8686
publishDate 2017-01-01
description This paper deals with modelling the relationship between human Puumala hantavirus (PUUV) infection, the abundance and prevalence of infection of the host (the bank vole), mast, and temperature. These data were used to build and parametrise generalised regression models, and parametrise them using datasets on these factors pertaining to the Netherlands. The performance of the models was assessed by considering their predictive power. Models including mast and monthly temperature performed well, and showed that mast intensity influences vole abundance and hence human exposure for the following year. Thus, the model can aid in forecasting of human illness cases, since (1) mast intensity influences the vole abundance and hence human exposure for the following year and (2) monitoring of mast is much more feasible than determining bank vole abundance.
topic Puumala
human cases
prediction
environment
climate
url http://dx.doi.org/10.1080/20008686.2017.1287986
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