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Identifying the spatio-temporal risk variability of avian influenza A H7N9 in China

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  • Zhang, Ping
  • Wang, Jianwen
  • Atkinson, Peter M.

Abstract

The outbreak of H7N9 epidemic in human has seasonal changes. However, up to now there is no research on the spatial-temporal variation characteristics of the relative risk of human H7N9 infection, and the covariate combination that has a greater impact on the relative risk of human H7N9 infection in different seasons. This study used China as the study area to predict the seasonal relative risk of human H7N9 infection through a Bayesian hierarchical conditional autoregressive model (BHCAR), which including five covariates (population density, number of live poultry markets, average precipitation, average temperature, and average relative humidity), seasonal random effects, and spatial random effects. Moreover, the sensitivity of the Bayesian hierarchical model (BH) to predict the seasonal relative risk of human H7N9 infection by changing the parameter settings of the BH prior distribution was analyzed. It was found that the relative risk of human H7N9 infection in spring and winter had spatial random effects, but not in summer and autumn. In spring, autumn and winter, the combination of population density and the number of live poultry markets had a significant influence on the relative risk of human H7N9 infection. In summer, however, the relative risk of human H7N9 infection was largely affected by population density, the number of live poultry markets, average precipitation and average temperature. Further, the standard deviation of the normal distribution to which the covariate coefficient in the BH was subject seemed to have an influence on the prediction and fitting effect of the seasonal relative risk of human infection with H7N9.

Suggested Citation

  • Zhang, Ping & Wang, Jianwen & Atkinson, Peter M., 2019. "Identifying the spatio-temporal risk variability of avian influenza A H7N9 in China," Ecological Modelling, Elsevier, vol. 414(C).
    • Handle: RePEc:eee:ecomod:v:414:y:2019:i:c:s0304380019303151
    DOI: 10.1016/j.ecolmodel.2019.108807
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    1. Lili Zhuang & Noel Cressie, 2014. "Bayesian hierarchical statistical SIRS models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(4), pages 601-646, November.
    2. Thomas Fung & Joanna J.J. Wang & Eugene Seneta, 2014. "The Deviance Information Criterion in Comparison of Normal Mixing Models," International Statistical Review, International Statistical Institute, vol. 82(3), pages 411-421, December.
    3. Steffen Unkel & C. Paddy Farrington & Paul H. Garthwaite & Chris Robertson & Nick Andrews, 2012. "Statistical methods for the prospective detection of infectious disease outbreaks: a review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(1), pages 49-82, January.
    4. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
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    Cited by:

    1. Yongzhu Xiong & Yunpeng Wang & Feng Chen & Mingyong Zhu, 2020. "Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China," IJERPH, MDPI, vol. 17(11), pages 1-26, May.
    2. Xu Zhao & Hengxing Xiang & Feifei Zhao, 2023. "Measurement and Spatial Differentiation of Farmers’ Livelihood Resilience Under the COVID-19 Epidemic Outbreak in Rural China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 166(2), pages 239-267, April.

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