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 (H1N1) influenza pandemic in China. Here, we analyze the spatial–temporal dynamics of the 2009 pandemic across Changsha municipal districts, evaluate the relationship between case incidence and the local urban spatial structure and predic...

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Veröffentlicht in:Chinese science bulletin 2014-02, Vol.59 (5-6), p.554-562
Hauptverfasser: Xiao, Hong, Lin, Xiaoling, Chowell, Gerardo, Huang, Cunrui, Gao, Lidong, Chen, Biyun, Wang, Zheng, Zhou, Liang, He, Xinguang, Liu, Haining, Zhang, Xixing, Yang, Huisuo
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Sprache:eng
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Zusammenfassung:Changsha was one of the most affected areas during the 2009 A (H1N1) influenza pandemic in China. Here, we analyze the spatial–temporal dynamics of the 2009 pandemic across Changsha municipal districts, evaluate the relationship between case incidence and the local urban spatial structure and predict high-risk areas of influenza A (H1N1). We obtained epidemiological data on all cases of influenza A (H1N1) reported across municipal districts in Changsha during period May 2009–December 2010 and data on population density and basic geographic characteristics for 239 primary schools, 97 middle schools, 347 universities, 96 malls and markets, 674 business districts and 121 hospitals. Spatial–temporal K functions, proximity models and logistic regression were used to analyze the spatial distribution pattern of influenza A (H1N1) incidence and the association between influenza A (H1N1) cases and spatial risk factors and predict the infection risks. We found that the 2009 influenza A (H1N1) 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 (H1N1) cases was associated with population density and the presence of nearest public places, especially universities (OR = 10.166). The final predictive 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 findings support the identification of spatial risk factors and high-risk areas to guide the prioritization of preventive and mitigation efforts against future influenza pandemics.
ISSN:1001-6538
2095-9273
1861-9541
2095-9281
DOI:10.1007/s11434-013-0084-6