tetano
Editor, Senior Moderator
Int J Environ Res Public Health. 2016 Jun 16;13(6). pii: E600. doi: 10.3390/ijerph13060600.
[h=1]Ecological Niche Modeling of Risk Factors for H7N9 Human Infection in China.[/h] Xu M[SUP]1[/SUP], Cao C[SUP]2[/SUP], Li Q[SUP]3[/SUP], Jia P[SUP]4,[/SUP][SUP]5[/SUP], Zhao J[SUP]6[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] 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.
[h=4]KEYWORDS:[/h] H7N9; MaxEnt; avian flu; environmental factors; spatial modeling
PMID: 27322296 [PubMed - in process] Free full text
[h=1]Ecological Niche Modeling of Risk Factors for H7N9 Human Infection in China.[/h] Xu M[SUP]1[/SUP], Cao C[SUP]2[/SUP], Li Q[SUP]3[/SUP], Jia P[SUP]4,[/SUP][SUP]5[/SUP], Zhao J[SUP]6[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] 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.
[h=4]KEYWORDS:[/h] H7N9; MaxEnt; avian flu; environmental factors; spatial modeling
PMID: 27322296 [PubMed - in process] Free full text