Spatial prediction and validation of zoonotic hazard through micro-habitat properties: where does Puumala hantavirus hole – up?
Abstract Background To predict the risk of infectious diseases originating in wildlife, it is important to identify habitats that allow the co-occurrence of pathogens and their hosts. Puumala hantavirus (PUUV) is a directly-transmitted RNA virus that causes hemorrhagic fever in humans, and is carrie...
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ftdatacite:10.6084/m9.figshare.c.3836812 2023-05-15T16:12:17+02:00 Spatial prediction and validation of zoonotic hazard through micro-habitat properties: where does Puumala hantavirus hole – up? Khalil, Hussein Olsson, Gert Magnusson, Magnus Evander, Magnus Hörnfeldt, Birger Ecke, Frauke 2017 https://dx.doi.org/10.6084/m9.figshare.c.3836812 https://figshare.com/collections/Spatial_prediction_and_validation_of_zoonotic_hazard_through_micro-habitat_properties_where_does_Puumala_hantavirus_hole_up_/3836812 unknown Figshare https://dx.doi.org/10.1186/s12879-017-2618-z CC BY 4.0 https://creativecommons.org/licenses/by/4.0 CC-BY Medicine Microbiology FOS Biological sciences Biotechnology Ecology Sociology FOS Sociology Infectious Diseases FOS Health sciences Plant Biology Virology Computational Biology Collection article 2017 ftdatacite https://doi.org/10.6084/m9.figshare.c.3836812 https://doi.org/10.1186/s12879-017-2618-z 2021-11-05T12:55:41Z Abstract Background To predict the risk of infectious diseases originating in wildlife, it is important to identify habitats that allow the co-occurrence of pathogens and their hosts. Puumala hantavirus (PUUV) is a directly-transmitted RNA virus that causes hemorrhagic fever in humans, and is carried and transmitted by the bank vole (Myodes glareolus). In northern Sweden, bank voles undergo 3–4 year population cycles, during which their spatial distribution varies greatly. Methods We used boosted regression trees; a technique inspired by machine learning, on a 10 – year time-series (fall 2003–2013) to develop a spatial predictive model assessing seasonal PUUV hazard using micro-habitat variables in a landscape heavily modified by forestry. We validated the models in an independent study area approx. 200 km away by predicting seasonal presence of infected bank voles in a five-year-period (2007–2010 and 2015). Results The distribution of PUUV-infected voles varied seasonally and inter-annually. In spring, micro-habitat variables related to cover and food availability in forests predicted both bank vole and infected bank vole presence. In fall, the presence of PUUV-infected voles was generally restricted to spruce forests where cover was abundant, despite the broad landscape distribution of bank voles in general. We hypothesize that the discrepancy in distribution between infected and uninfected hosts in fall, was related to higher survival of PUUV and/or PUUV-infected voles in the environment, especially where cover is plentiful. Conclusions Moist and mesic old spruce forests, with abundant cover such as large holes and bilberry shrubs, also providing food, were most likely to harbor infected bank voles. The models developed using long-term and spatially extensive data can be extrapolated to other areas in northern Fennoscandia. To predict the hazard of directly transmitted zoonoses in areas with unknown risk status, models based on micro-habitat variables and developed through machine learning techniques in well-studied systems, could be used. Article in Journal/Newspaper Fennoscandia Northern Sweden DataCite Metadata Store (German National Library of Science and Technology) |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
Medicine Microbiology FOS Biological sciences Biotechnology Ecology Sociology FOS Sociology Infectious Diseases FOS Health sciences Plant Biology Virology Computational Biology |
spellingShingle |
Medicine Microbiology FOS Biological sciences Biotechnology Ecology Sociology FOS Sociology Infectious Diseases FOS Health sciences Plant Biology Virology Computational Biology Khalil, Hussein Olsson, Gert Magnusson, Magnus Evander, Magnus Hörnfeldt, Birger Ecke, Frauke Spatial prediction and validation of zoonotic hazard through micro-habitat properties: where does Puumala hantavirus hole – up? |
topic_facet |
Medicine Microbiology FOS Biological sciences Biotechnology Ecology Sociology FOS Sociology Infectious Diseases FOS Health sciences Plant Biology Virology Computational Biology |
description |
Abstract Background To predict the risk of infectious diseases originating in wildlife, it is important to identify habitats that allow the co-occurrence of pathogens and their hosts. Puumala hantavirus (PUUV) is a directly-transmitted RNA virus that causes hemorrhagic fever in humans, and is carried and transmitted by the bank vole (Myodes glareolus). In northern Sweden, bank voles undergo 3–4 year population cycles, during which their spatial distribution varies greatly. Methods We used boosted regression trees; a technique inspired by machine learning, on a 10 – year time-series (fall 2003–2013) to develop a spatial predictive model assessing seasonal PUUV hazard using micro-habitat variables in a landscape heavily modified by forestry. We validated the models in an independent study area approx. 200 km away by predicting seasonal presence of infected bank voles in a five-year-period (2007–2010 and 2015). Results The distribution of PUUV-infected voles varied seasonally and inter-annually. In spring, micro-habitat variables related to cover and food availability in forests predicted both bank vole and infected bank vole presence. In fall, the presence of PUUV-infected voles was generally restricted to spruce forests where cover was abundant, despite the broad landscape distribution of bank voles in general. We hypothesize that the discrepancy in distribution between infected and uninfected hosts in fall, was related to higher survival of PUUV and/or PUUV-infected voles in the environment, especially where cover is plentiful. Conclusions Moist and mesic old spruce forests, with abundant cover such as large holes and bilberry shrubs, also providing food, were most likely to harbor infected bank voles. The models developed using long-term and spatially extensive data can be extrapolated to other areas in northern Fennoscandia. To predict the hazard of directly transmitted zoonoses in areas with unknown risk status, models based on micro-habitat variables and developed through machine learning techniques in well-studied systems, could be used. |
format |
Article in Journal/Newspaper |
author |
Khalil, Hussein Olsson, Gert Magnusson, Magnus Evander, Magnus Hörnfeldt, Birger Ecke, Frauke |
author_facet |
Khalil, Hussein Olsson, Gert Magnusson, Magnus Evander, Magnus Hörnfeldt, Birger Ecke, Frauke |
author_sort |
Khalil, Hussein |
title |
Spatial prediction and validation of zoonotic hazard through micro-habitat properties: where does Puumala hantavirus hole – up? |
title_short |
Spatial prediction and validation of zoonotic hazard through micro-habitat properties: where does Puumala hantavirus hole – up? |
title_full |
Spatial prediction and validation of zoonotic hazard through micro-habitat properties: where does Puumala hantavirus hole – up? |
title_fullStr |
Spatial prediction and validation of zoonotic hazard through micro-habitat properties: where does Puumala hantavirus hole – up? |
title_full_unstemmed |
Spatial prediction and validation of zoonotic hazard through micro-habitat properties: where does Puumala hantavirus hole – up? |
title_sort |
spatial prediction and validation of zoonotic hazard through micro-habitat properties: where does puumala hantavirus hole – up? |
publisher |
Figshare |
publishDate |
2017 |
url |
https://dx.doi.org/10.6084/m9.figshare.c.3836812 https://figshare.com/collections/Spatial_prediction_and_validation_of_zoonotic_hazard_through_micro-habitat_properties_where_does_Puumala_hantavirus_hole_up_/3836812 |
genre |
Fennoscandia Northern Sweden |
genre_facet |
Fennoscandia Northern Sweden |
op_relation |
https://dx.doi.org/10.1186/s12879-017-2618-z |
op_rights |
CC BY 4.0 https://creativecommons.org/licenses/by/4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.6084/m9.figshare.c.3836812 https://doi.org/10.1186/s12879-017-2618-z |
_version_ |
1765997545263202304 |