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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2017-01-01
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Series: | Infection Ecology & Epidemiology |
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Online Access: | http://dx.doi.org/10.1080/20008686.2017.1287986 |
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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 |
work_keys_str_mv |
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