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Identifying Risk Factors Of A(H7N9) Outbreak by Wavelet Analysis and Generalized Estimating Equation

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  • Qinling Yan

    (School of Mathematics and Information Science, Shaanxi Normal University, Xi’an 710119, China)

  • Sanyi Tang

    (School of Mathematics and Information Science, Shaanxi Normal University, Xi’an 710119, China)

  • Zhen Jin

    (Complex System Research center, Shanxi University, Taiyuan 030006, China)

  • Yanni Xiao

    (Department of Applied Mathematics, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

Five epidemic waves of A(H7N9) occurred between March 2013 and May 2017 in China. However, the potential risk factors associated with disease transmission remain unclear. To address the spatial–temporal distribution of the reported A(H7N9) human cases (hereafter referred to as “cases”), statistical description and geographic information systems were employed. Based on long-term observation data, we found that males predominated the majority of A(H7N9)-infected individuals and that most males were middle-aged or elderly. Further, wavelet analysis was used to detect the variation in time-frequency between A(H7N9) cases and meteorological factors. Moreover, we formulated a Poisson regression model to explore the relationship among A(H7N9) cases and meteorological factors, the number of live poultry markets (LPMs), population density and media coverage. The main results revealed that the impact factors of A(H7N9) prevalence are manifold, and the number of LPMs has a significantly positive effect on reported A(H7N9) cases, while the effect of weekly average temperature is significantly negative. This confirms that the interaction of multiple factors could result in a serious A(H7N9) outbreak. Therefore, public health departments adopting the corresponding management measures based on both the number of LPMs and the forecast of meteorological conditions are crucial for mitigating A(H7N9) prevalence.

Suggested Citation

  • Qinling Yan & Sanyi Tang & Zhen Jin & Yanni Xiao, 2019. "Identifying Risk Factors Of A(H7N9) Outbreak by Wavelet Analysis and Generalized Estimating Equation," IJERPH, MDPI, vol. 16(8), pages 1-13, April.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:8:p:1311-:d:222117
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    References listed on IDEAS

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    1. Min Xu & Chunxiang Cao & Qun Li & Peng Jia & Jian Zhao, 2016. "Ecological Niche Modeling of Risk Factors for H7N9 Human Infection in China," IJERPH, MDPI, vol. 13(6), pages 1-12, June.
    2. Francesca Dominici & Lianne Sheppard & Merlise Clyde, 2003. "Health Effects of Air Pollution: A Statistical Review," International Statistical Review, International Statistical Institute, vol. 71(2), pages 243-276, August.
    3. Jianfang Zhou & Dayan Wang & Rongbao Gao & Baihui Zhao & Jingdong Song & Xian Qi & Yanjun Zhang & Yonglin Shi & Lei Yang & Wenfei Zhu & Tian Bai & Kun Qin & Yu Lan & Shumei Zou & Junfeng Guo & Jie Don, 2013. "Biological features of novel avian influenza A (H7N9) virus," Nature, Nature, vol. 499(7459), pages 500-503, July.
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