Abstract
This study investigated the spatial distribution, spatial autocorrelation, temporal cluster, spatial-temporal autocorrelation and probable risk factors of H7N9 outbreaks in humans from March 2013 to December 2014 in China. The results showed that the epidemic spread with significant spatial-temporal autocorrelation. In order to describe the spatial-temporal autocorrelation of H7N9, an improved model was developed by introducing a spatial-temporal factor in this paper. Logistic regression analyses were utilized to investigate the risk factors associated with their distribution, and nine risk factors were significantly associated with the occurrence of A(H7N9) human infections: the spatial-temporal factor φ (OR = 2546669.382, p < 0.001), migration route (OR = 0.993, p < 0.01), river (OR = 0.861, p < 0.001), lake(OR = 0.992, p < 0.001), road (OR = 0.906, p < 0.001), railway (OR = 0.980, p < 0.001), temperature (OR = 1.170, p < 0.01), precipitation (OR = 0.615, p < 0.001) and relative humidity (OR = 1.337, p < 0.001). The improved model obtained a better prediction performance and a higher fitting accuracy than the traditional model: in the improved model 90.1% (91/101) of the cases during February 2014 occurred in the high risk areas (the predictive risk > 0.70) of the predictive risk map, whereas 44.6% (45/101) of which overlaid on the high risk areas (the predictive risk > 0.70) for the traditional model, and the fitting accuracy of the improved model was 91.6% which was superior to the traditional model (86.1%). The predictive risk map generated based on the improved model revealed that the east and southeast of China were the high risk areas of A(H7N9) human infections in February 2014. These results provided baseline data for the control and prevention of future human infections.
Highlights
Following the human infections of H5N1, H9N2, H7N7, H7N2, H7N3 avian flu virus, in March2013, the emergence of the novel avian-origin influenza A(H7N9) virus attracted worldwide concern about the possibility of a new influenza pandemic on human society [1,2,3]
An improved model was proposed by introducing the spatial-temporal factor φ in traditional logistic model, we included φ as an additional variable along with other risk factors used in traditional logistic regression analysis
In the improved multivariate logistic regression model, we noted that the difference of the spatial-temporal factor φ was obviously statistically significant, and φ was a very significant risk factor for human influenza A(H7N9) cases in China (OR = 2546669.382, p < 0.001), this showed the existence of spatial-temporal autocorrelation in A(H7N9) human infections
Summary
Following the human infections of H5N1, H9N2, H7N7, H7N2, H7N3 avian flu virus, in March. 2013, the emergence of the novel avian-origin influenza A(H7N9) virus attracted worldwide concern about the possibility of a new influenza pandemic on human society [1,2,3]. Human infections with severe respiratory illness and mortality were confirmed from March 2013 to December 2014 in China [4]. The H7N9 epidemic essentially became a public health threat in. Gene sequence studies have demonstrated that the novel avian-origin. Res. Public Health 2015, 12, 15204–15221; doi:10.3390/ijerph121214981 www.mdpi.com/journal/ijerph
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