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Landscape and regional environmental analysis of the spatial distribution of hantavirus human cases in europe.

Zeimes CB, Quoilin S, Henttonen H, Lyytikäinen O, Vapalahti O, Reynes JM, Reusken C, Swart AN, Vainio K, Hjertqvist M, Vanwambeke SO - Front Public Health (2015)

Bottom Line: Climate and forest connectivity have a stronger effect at the regional level.At large scale, the effect of one variable on disease may follow three response scenarios: the effect may be the same across the entire study area, the effect can change according to the variable value, and the effect can change depending on local specificities.Each of these scenarios impacts large-scale modeling differently.

View Article: PubMed Central - PubMed

Affiliation: Georges Lemaître Centre for Earth and Climate Research, Earth and Life Institute, Université Catholique de Louvain (UCL) , Louvain-la-Neuve , Belgium.

ABSTRACT

Background: In Europe, the most prevalent hantavirus, Puumala virus, is transmitted by bank voles and causes nephropathia epidemica in human. The European spatial distribution of nephropathia epidemica is investigated here for the first time with a rich set of environmental variables.

Methods: The influence of variables at the landscape and regional level is studied through multilevel logistic regression, and further information on their effects across the different European ecoregions is obtained by comparing an overall niche model (boosted regression trees) with regressions by ecoregion.

Results: The presence of nephropathia epidemica is likely in populated regions with well-connected forests, more intense vegetation activity, low soil water content, mild summers, and cold winters. In these regions, landscapes with a higher proportion of built-up areas in forest ecotones and lower minimum temperature in winter are expected to be more at risk. Climate and forest connectivity have a stronger effect at the regional level. If variables are staying at their current values, the models predict that nephropathia epidemica may know intensification but should not spread (although southern Sweden, the Norwegian coast, and the Netherlands should be kept under watch).

Conclusion: Models indicate that large-scale modeling can lead to a very high predictive power. At large scale, the effect of one variable on disease may follow three response scenarios: the effect may be the same across the entire study area, the effect can change according to the variable value, and the effect can change depending on local specificities. Each of these scenarios impacts large-scale modeling differently.

No MeSH data available.


Related in: MedlinePlus

Three scenarios that can happen when modeling variables over a large study area, squared frames represent different subregions of a large study area.
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Figure 6: Three scenarios that can happen when modeling variables over a large study area, squared frames represent different subregions of a large study area.

Mentions: Both the multilevel and the BRT models have very good predictive powers. The distribution of zoonoses at a large-scale using a single database built from national databases is successfully modeled. However, large-scale models can obscure the local effect of environmental factors or how they may differ from place to place. With these models, the interpretation of the variables may generate misleading conclusion as wide range of conditions cannot be summarized into one explanation. We identify three scenarios of response to a variable that can be encountered in modeling studies at a large-scale (Figure 6). These scenarios are illustrated by variables from this study, but are relevant to other disease modeling studies.


Landscape and regional environmental analysis of the spatial distribution of hantavirus human cases in europe.

Zeimes CB, Quoilin S, Henttonen H, Lyytikäinen O, Vapalahti O, Reynes JM, Reusken C, Swart AN, Vainio K, Hjertqvist M, Vanwambeke SO - Front Public Health (2015)

Three scenarios that can happen when modeling variables over a large study area, squared frames represent different subregions of a large study area.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4379737&req=5

Figure 6: Three scenarios that can happen when modeling variables over a large study area, squared frames represent different subregions of a large study area.
Mentions: Both the multilevel and the BRT models have very good predictive powers. The distribution of zoonoses at a large-scale using a single database built from national databases is successfully modeled. However, large-scale models can obscure the local effect of environmental factors or how they may differ from place to place. With these models, the interpretation of the variables may generate misleading conclusion as wide range of conditions cannot be summarized into one explanation. We identify three scenarios of response to a variable that can be encountered in modeling studies at a large-scale (Figure 6). These scenarios are illustrated by variables from this study, but are relevant to other disease modeling studies.

Bottom Line: Climate and forest connectivity have a stronger effect at the regional level.At large scale, the effect of one variable on disease may follow three response scenarios: the effect may be the same across the entire study area, the effect can change according to the variable value, and the effect can change depending on local specificities.Each of these scenarios impacts large-scale modeling differently.

View Article: PubMed Central - PubMed

Affiliation: Georges Lemaître Centre for Earth and Climate Research, Earth and Life Institute, Université Catholique de Louvain (UCL) , Louvain-la-Neuve , Belgium.

ABSTRACT

Background: In Europe, the most prevalent hantavirus, Puumala virus, is transmitted by bank voles and causes nephropathia epidemica in human. The European spatial distribution of nephropathia epidemica is investigated here for the first time with a rich set of environmental variables.

Methods: The influence of variables at the landscape and regional level is studied through multilevel logistic regression, and further information on their effects across the different European ecoregions is obtained by comparing an overall niche model (boosted regression trees) with regressions by ecoregion.

Results: The presence of nephropathia epidemica is likely in populated regions with well-connected forests, more intense vegetation activity, low soil water content, mild summers, and cold winters. In these regions, landscapes with a higher proportion of built-up areas in forest ecotones and lower minimum temperature in winter are expected to be more at risk. Climate and forest connectivity have a stronger effect at the regional level. If variables are staying at their current values, the models predict that nephropathia epidemica may know intensification but should not spread (although southern Sweden, the Norwegian coast, and the Netherlands should be kept under watch).

Conclusion: Models indicate that large-scale modeling can lead to a very high predictive power. At large scale, the effect of one variable on disease may follow three response scenarios: the effect may be the same across the entire study area, the effect can change according to the variable value, and the effect can change depending on local specificities. Each of these scenarios impacts large-scale modeling differently.

No MeSH data available.


Related in: MedlinePlus