Cargando…
Ecological Niche Modeling of Risk Factors for H7N9 Human Infection in China
China was attacked by a serious influenza A (H7N9) virus in 2013. The first human infection case was confirmed in Shanghai City and soon spread across most of eastern China. Using the methods of Geographic Information Systems (GIS) and ecological niche modeling (ENM), this research quantitatively an...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4924057/ https://www.ncbi.nlm.nih.gov/pubmed/27322296 http://dx.doi.org/10.3390/ijerph13060600 |
_version_ | 1782439799047585792 |
---|---|
author | Xu, Min Cao, Chunxiang Li, Qun Jia, Peng Zhao, Jian |
author_facet | Xu, Min Cao, Chunxiang Li, Qun Jia, Peng Zhao, Jian |
author_sort | Xu, Min |
collection | PubMed |
description | China was attacked by a serious influenza A (H7N9) virus in 2013. The first human infection case was confirmed in Shanghai City and soon spread across most of eastern China. Using the methods of Geographic Information Systems (GIS) and ecological niche modeling (ENM), this research quantitatively analyzed the relationships between the H7N9 occurrence and the main environmental factors, including meteorological variables, human population density, bird migratory routes, wetland distribution, and live poultry farms, markets, and processing factories. Based on these relationships the probability of the presence of H7N9 was predicted. Results indicated that the distribution of live poultry processing factories, farms, and human population density were the top three most important determinants of the H7N9 human infection. The relative contributions to the model of live poultry processing factories, farms and human population density were 39.9%, 17.7% and 17.7%, respectively, while the maximum temperature of the warmest month and mean relative humidity had nearly no contribution to the model. The paper has developed an ecological niche model (ENM) that predicts the spatial distribution of H7N9 cases in China using environmental variables. The area under the curve (AUC) values of the model were greater than 0.9 (0.992 for the training samples and 0.961 for the test data). The findings indicated that most of the high risk areas were distributed in the Yangtze River Delta. These findings have important significance for the Chinese government to enhance the environmental surveillance at multiple human poultry interfaces in the high risk area. |
format | Online Article Text |
id | pubmed-4924057 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-49240572016-07-05 Ecological Niche Modeling of Risk Factors for H7N9 Human Infection in China Xu, Min Cao, Chunxiang Li, Qun Jia, Peng Zhao, Jian Int J Environ Res Public Health Article China was attacked by a serious influenza A (H7N9) virus in 2013. The first human infection case was confirmed in Shanghai City and soon spread across most of eastern China. Using the methods of Geographic Information Systems (GIS) and ecological niche modeling (ENM), this research quantitatively analyzed the relationships between the H7N9 occurrence and the main environmental factors, including meteorological variables, human population density, bird migratory routes, wetland distribution, and live poultry farms, markets, and processing factories. Based on these relationships the probability of the presence of H7N9 was predicted. Results indicated that the distribution of live poultry processing factories, farms, and human population density were the top three most important determinants of the H7N9 human infection. The relative contributions to the model of live poultry processing factories, farms and human population density were 39.9%, 17.7% and 17.7%, respectively, while the maximum temperature of the warmest month and mean relative humidity had nearly no contribution to the model. The paper has developed an ecological niche model (ENM) that predicts the spatial distribution of H7N9 cases in China using environmental variables. The area under the curve (AUC) values of the model were greater than 0.9 (0.992 for the training samples and 0.961 for the test data). The findings indicated that most of the high risk areas were distributed in the Yangtze River Delta. These findings have important significance for the Chinese government to enhance the environmental surveillance at multiple human poultry interfaces in the high risk area. MDPI 2016-06-16 2016-06 /pmc/articles/PMC4924057/ /pubmed/27322296 http://dx.doi.org/10.3390/ijerph13060600 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Xu, Min Cao, Chunxiang Li, Qun Jia, Peng Zhao, Jian Ecological Niche Modeling of Risk Factors for H7N9 Human Infection in China |
title | Ecological Niche Modeling of Risk Factors for H7N9 Human Infection in China |
title_full | Ecological Niche Modeling of Risk Factors for H7N9 Human Infection in China |
title_fullStr | Ecological Niche Modeling of Risk Factors for H7N9 Human Infection in China |
title_full_unstemmed | Ecological Niche Modeling of Risk Factors for H7N9 Human Infection in China |
title_short | Ecological Niche Modeling of Risk Factors for H7N9 Human Infection in China |
title_sort | ecological niche modeling of risk factors for h7n9 human infection in china |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4924057/ https://www.ncbi.nlm.nih.gov/pubmed/27322296 http://dx.doi.org/10.3390/ijerph13060600 |
work_keys_str_mv | AT xumin ecologicalnichemodelingofriskfactorsforh7n9humaninfectioninchina AT caochunxiang ecologicalnichemodelingofriskfactorsforh7n9humaninfectioninchina AT liqun ecologicalnichemodelingofriskfactorsforh7n9humaninfectioninchina AT jiapeng ecologicalnichemodelingofriskfactorsforh7n9humaninfectioninchina AT zhaojian ecologicalnichemodelingofriskfactorsforh7n9humaninfectioninchina |