Chinese Science Bulletin

, Volume 59, Issue 5–6, pp 554–562 | Cite as

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

  • Hong Xiao
  • Xiaoling Lin
  • Gerardo Chowell
  • Cunrui Huang
  • Lidong Gao
  • Biyun Chen
  • Zheng Wang
  • Liang Zhou
  • Xinguang He
  • Haining Liu
  • Xixing Zhang
  • Huisuo Yang
Article Geography


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.


Influenza A (H1N1) Spatial risk factors Spatial–temporal K functions Proximity model Logistic regression 



We wish to express our thanks to the anonymous referees for their helpful comments on an earlier draft of this article. This work was supported by the Key Discipline Construction Project in Hunan Province (2008001), the National Natural Science Foundation of China and the Scientific Research Fund of Hunan Provincial Education Department (13A051).


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Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Hong Xiao
    • 1
  • Xiaoling Lin
    • 1
  • Gerardo Chowell
    • 2
    • 3
  • Cunrui Huang
    • 4
  • Lidong Gao
    • 5
  • Biyun Chen
    • 5
  • Zheng Wang
    • 6
  • Liang Zhou
    • 1
  • Xinguang He
    • 1
  • Haining Liu
    • 1
  • Xixing Zhang
    • 7
  • Huisuo Yang
    • 8
  1. 1.College of Resources and Environment ScienceHunan Normal UniversityChangshaChina
  2. 2.Mathematical, Computational and Modeling Sciences Center, School of Human Evolution and Social ChangeArizona State UniversityTempeUSA
  3. 3.Division of International Epidemiology and Population Studies, Fogarty International CenterNational Institutes of HealthBethesdaUSA
  4. 4.Centre for Environment and Population Health, School of EnvironmentGriffith UniversityBrisbaneAustralia
  5. 5.Hunan Provincial Center for Disease Control and PreventionChangshaChina
  6. 6.Department of Respiratory and Critical Care MedicineBeijing Institute of Respiratory Medicine, Beijing Chaoyang Hospital Affiliated to Capital Medical UniversityBeijingChina
  7. 7.Changsha Municipal Center for Disease Prevention and ControlChangshaChina
  8. 8.Center for Disease Control and Prevention of Beijing Military RegionBeijingChina

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