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Impacts of Road Traffic Network and Socioeconomic Factors on the Diffusion of 2009 Pandemic Influenza A (H1N1) in Mainland China

Author

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  • Bo Xu

    (Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
    Joint Center for Global Change Studies, Beijing 100875, China)

  • Huaiyu Tian

    (State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China)

  • Clive Eric Sabel

    (Department of Environmental Science, Aarhus University, 4000 Roskilde, Denmark)

  • Bing Xu

    (Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
    Joint Center for Global Change Studies, Beijing 100875, China)

Abstract

The 2009 pandemic influenza virus caused the majority of the influenza A virus infections in China in 2009. It arrived in several Chinese cities from imported cases and then spread as people travelled domestically by all means of transportation, among which road traffic was the most commonly used for daily commuting. Spatial variation in socioeconomic status not only accelerates migration across regions but also partly induces the differences in epidemic processes and in responses to epidemics across regions. However, the roles of both road travel and socioeconomic factors have not received the attention they deserve. Here, we constructed a national highway network for and between 333 cities in mainland China and extracted epidemiological variables and socioeconomic factors for each city. We calculated classic centrality measures for each city in the network and proposed two new measures ( SumRatio and Multicenter Distance ). We evaluated the correlation between the centrality measures and epidemiological features and conducted a spatial autoregression to quantify the impacts of road network and socioeconomic factors during the outbreak. The results showed that epidemics had more significant relationships with both our new measures than the classic ones. Higher population density, higher per person income, larger SumRatio and Multicenter Distance , more hospitals and college students, and lower per person GDP were associated with higher cumulative incidence. Higher population density and number of slaughtered pigs were found to advance epidemic arrival time. Higher population density, more colleges and slaughtered pigs, and lower Multicenter Distance were associated with longer epidemic duration. In conclusion, road transport and socioeconomic status had significant impacts and should be considered for the prevention and control of future pandemics.

Suggested Citation

  • Bo Xu & Huaiyu Tian & Clive Eric Sabel & Bing Xu, 2019. "Impacts of Road Traffic Network and Socioeconomic Factors on the Diffusion of 2009 Pandemic Influenza A (H1N1) in Mainland China," IJERPH, MDPI, vol. 16(7), pages 1-14, April.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:7:p:1223-:d:220307
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    References listed on IDEAS

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    1. Jun Cai & Bo Xu & Karen Kie Yan Chan & Xueying Zhang & Bing Zhang & Ziyue Chen & Bing Xu, 2019. "Roles of Different Transport Modes in the Spatial Spread of the 2009 Influenza A(H1N1) Pandemic in Mainland China," IJERPH, MDPI, vol. 16(2), pages 1-15, January.
    2. Tini Garske & Hongjie Yu & Zhibin Peng & Min Ye & Hang Zhou & Xiaowen Cheng & Jiabing Wu & Neil Ferguson, 2011. "Travel Patterns in China," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-9, February.
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    Cited by:

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    2. Mubarak Alrumaidhi & Hesham A. Rakha, 2024. "An Econometric Analysis to Explore the Temporal Variability of the Factors Affecting Crash Severity Due to COVID-19," Sustainability, MDPI, vol. 16(3), pages 1-26, February.
    3. Jacqueline D. Seufert & Andre Python & Christoph Weisser & Elías Cisneros & Krisztina Kis‐Katos & Thomas Kneib, 2022. "Mapping ex ante risks of COVID‐19 in Indonesia using a Bayesian geostatistical model on airport network data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2121-2155, October.
    4. Chich-Ping Hu, 2022. "The COVID-19 Epidemic Spreading Effects," Sustainability, MDPI, vol. 14(15), pages 1-11, August.
    5. Kangwei Tu & Andras Reith, 2023. "Changes in Urban Planning in Response to Pandemics: A Comparative Review from H1N1 to COVID-19 (2009–2022)," Sustainability, MDPI, vol. 15(12), pages 1-20, June.

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