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Ecological niche modeling to determine potential niche of Vaccinia virus: a case only study
BACKGROUND: Emerging and understudied pathogens often lack information that most commonly used analytical tools require, such as negative controls or baseline data; thus, new analytical strategies are needed to analyze transmission patterns and drivers of disease emergence. Zoonotic infections with...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5547515/ https://www.ncbi.nlm.nih.gov/pubmed/28784125 http://dx.doi.org/10.1186/s12942-017-0100-1 |
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author | Quiner, Claire A. Nakazawa, Yoshinori |
author_facet | Quiner, Claire A. Nakazawa, Yoshinori |
author_sort | Quiner, Claire A. |
collection | PubMed |
description | BACKGROUND: Emerging and understudied pathogens often lack information that most commonly used analytical tools require, such as negative controls or baseline data; thus, new analytical strategies are needed to analyze transmission patterns and drivers of disease emergence. Zoonotic infections with Vaccinia virus (VACV) were first reported in Brazil in 1999, VACV is an emerging zoonotic Orthopoxvirus, which primarily infects dairy cattle and farmers in close contact with infected cows. Prospective studies of emerging pathogens could provide critical data that would inform public health planning and response to outbreaks. By using the location of 87-recorded outbreaks and publicly available bioclimatic data, we demonstrate one such approach. Using an ecological niche model (ENM) algorithm, we identify the environmental conditions under which VACV outbreaks have occurred, and determine additional locations in two affected countries that may be susceptible to transmission. Further, we show how suitability for the virus responds to different levels of various environmental factors and highlight the most important factors in determining its transmission. METHODS: A literature review was performed and the geospatial coordinates of 87 molecularly confirmed VACV outbreaks in Brazil were identified. An ENM was generated using MaxENT software by combining principal component analysis results of 19 bioclim spatial layers, and 25 randomly selected subsets of the original list of 87 outbreaks. RESULTS: The final ENM predicted all areas where Brazilian outbreaks occurred, one out of five of the Colombian outbreak regions and identified new regions within Brazil that are suitable for transmission based on bioclimatic factors. Further, the most important factors in determining transmission suitability are precipitation of the wettest quarter, annual precipitation, mean temperature of the coldest quarter and mean diurnal range. CONCLUSION: The analyses here provide a means by which to study patterns of an emerging infectious disease and identify regions that are potentially suitable for its transmission, in spite of the paucity of high-quality critical data. Policy and methods for the control of infectious diseases often use a reactionary model, addressing diseases only after significant impact on human health has ensued. The methodology used in the present work allows the identification of areas where disease is likely to appear, which could be used for directed intervention. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12942-017-0100-1) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5547515 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-55475152017-08-09 Ecological niche modeling to determine potential niche of Vaccinia virus: a case only study Quiner, Claire A. Nakazawa, Yoshinori Int J Health Geogr Research BACKGROUND: Emerging and understudied pathogens often lack information that most commonly used analytical tools require, such as negative controls or baseline data; thus, new analytical strategies are needed to analyze transmission patterns and drivers of disease emergence. Zoonotic infections with Vaccinia virus (VACV) were first reported in Brazil in 1999, VACV is an emerging zoonotic Orthopoxvirus, which primarily infects dairy cattle and farmers in close contact with infected cows. Prospective studies of emerging pathogens could provide critical data that would inform public health planning and response to outbreaks. By using the location of 87-recorded outbreaks and publicly available bioclimatic data, we demonstrate one such approach. Using an ecological niche model (ENM) algorithm, we identify the environmental conditions under which VACV outbreaks have occurred, and determine additional locations in two affected countries that may be susceptible to transmission. Further, we show how suitability for the virus responds to different levels of various environmental factors and highlight the most important factors in determining its transmission. METHODS: A literature review was performed and the geospatial coordinates of 87 molecularly confirmed VACV outbreaks in Brazil were identified. An ENM was generated using MaxENT software by combining principal component analysis results of 19 bioclim spatial layers, and 25 randomly selected subsets of the original list of 87 outbreaks. RESULTS: The final ENM predicted all areas where Brazilian outbreaks occurred, one out of five of the Colombian outbreak regions and identified new regions within Brazil that are suitable for transmission based on bioclimatic factors. Further, the most important factors in determining transmission suitability are precipitation of the wettest quarter, annual precipitation, mean temperature of the coldest quarter and mean diurnal range. CONCLUSION: The analyses here provide a means by which to study patterns of an emerging infectious disease and identify regions that are potentially suitable for its transmission, in spite of the paucity of high-quality critical data. Policy and methods for the control of infectious diseases often use a reactionary model, addressing diseases only after significant impact on human health has ensued. The methodology used in the present work allows the identification of areas where disease is likely to appear, which could be used for directed intervention. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12942-017-0100-1) contains supplementary material, which is available to authorized users. BioMed Central 2017-08-07 /pmc/articles/PMC5547515/ /pubmed/28784125 http://dx.doi.org/10.1186/s12942-017-0100-1 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Quiner, Claire A. Nakazawa, Yoshinori Ecological niche modeling to determine potential niche of Vaccinia virus: a case only study |
title | Ecological niche modeling to determine potential niche of Vaccinia virus: a case only study |
title_full | Ecological niche modeling to determine potential niche of Vaccinia virus: a case only study |
title_fullStr | Ecological niche modeling to determine potential niche of Vaccinia virus: a case only study |
title_full_unstemmed | Ecological niche modeling to determine potential niche of Vaccinia virus: a case only study |
title_short | Ecological niche modeling to determine potential niche of Vaccinia virus: a case only study |
title_sort | ecological niche modeling to determine potential niche of vaccinia virus: a case only study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5547515/ https://www.ncbi.nlm.nih.gov/pubmed/28784125 http://dx.doi.org/10.1186/s12942-017-0100-1 |
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