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Urban structure and the risk of influenza A (H1N1) outbreaks in municipal districts

Changsha was one of the most affected areas during the 2009 A (H 1N 1) influenza pandemic in China. Here, we analyze the spatial-temporal dynamics of the 2009 pan- demic across Changsha municipal districts, evaluate the relationship between case incidence and the local urban spatial structure and pr...

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Bibliographic Details
Published in:中国科学通报:英文版 2014 (5), p.554-562
Main Author: Hong Xiao Xiaoling Lin Gerardo Chowell Cunrui Huang Lidong Gao Biyun Chen Zheng Wang Liang Zhou Xinguang He Haining Liu Xixing Zhang Huisuo Yang
Format: Article
Language:English
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Summary:Changsha was one of the most affected areas during the 2009 A (H 1N 1) influenza pandemic in China. Here, we analyze the spatial-temporal dynamics of the 2009 pan- demic across Changsha municipal districts, evaluate the relationship between case incidence and the local urban spatial structure and predict high-risk areas of influenza A (H1NI). We obtained epidemiological data on all cases of influenza A (H1NI) reported across municipal districts in Changsha dur- ing period May 2009-December 2010 and data on population density and basic geographic characteristics for 239 primary schools, 97 middle schools, 347 universities, 96 mails and markets, 674 business districts and 121 hospitals. Spatial- temporal K functions, proximity models and logistic regres- sion were used to analyze the spatial distribution pattern of influenza A (H1N1) incidence and the association between influenza A (HINI) cases and spatial risk factors and predict the infection risks. We found that the 2009 influenza A (H 1N 1 ) was driven by a transmission wave from the center of the study area to surrounding areas and reported cases increased significantly after September 2009. We also found that the distribution of influenza A (H 1N1) cases was associ- ated with population density and the presence of nearest public places, especially universities (OR = 10.166). The final pre- dictive risk map based on the multivariate logistic analysis showed high-risk areas concentrated in the center areas of the study area associated with high population density. Our find- ings support the identification of spatial risk factors and high- risk areas to guide the prioritization of preventive and miti- gation efforts against future influenza pandemics.
ISSN:1001-6538
1861-9541