Rights statement: This is the author’s version of a work that was accepted for publication in Ecological Modelling. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Ecological Modelling, 414, 2019 DOI: 10.1016/j.ecolmodel.2019.108807
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Identifying the spatio-temporal risk variability of avian influenza A H7N9 in China
AU - Zhang, P.
AU - Wang, J.
AU - Atkinson, P.M.
N1 - This is the author’s version of a work that was accepted for publication in Ecological Modelling. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Ecological Modelling, 414, 2019 DOI: 10.1016/j.ecolmodel.2019.108807
PY - 2019/12/15
Y1 - 2019/12/15
N2 - 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.
AB - 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.
KW - BHCAR
KW - Human infection with H7N9
KW - Spatial-temporal risk variation
KW - Sensitivity analysis
U2 - 10.1016/j.ecolmodel.2019.108807
DO - 10.1016/j.ecolmodel.2019.108807
M3 - Journal article
VL - 414
JO - Ecological Modelling
JF - Ecological Modelling
SN - 0304-3800
M1 - 108807
ER -