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Int. J. Environ. Res. Public Health 2015, 12(12), 15204-15221; doi:10.3390/ijerph121214981

A Predictive Risk Model for A(H7N9) Human Infections Based on Spatial-Temporal Autocorrelation and Risk Factors: China, 2013–2014

1,2,3
,
2,3,* , 1,3
and
2,3
1
School of Tourism and Geographic Science, Yunnan Normal University, Kunming 650500, China
2
School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China
3
GIS Technology Engineering Research Centre for West-China Resources and Environment of Educational Ministry, Yunnan Normal University, Kunming 650500, China
*
Author to whom correspondence should be addressed.
Academic Editor: Peter Congdon
Received: 25 July 2015 / Revised: 11 October 2015 / Accepted: 10 November 2015 / Published: 1 December 2015
(This article belongs to the Special Issue Spatio-temporal Frameworks for Infectious Disease Epidemiology)
View Full-Text   |   Download PDF [2144 KB, uploaded 1 December 2015]   |  

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. View Full-Text
Keywords: H7N9; avian influenza; spatial-temporal autocorrelation; risk factors; logistic regression modelling H7N9; avian influenza; spatial-temporal autocorrelation; risk factors; logistic regression modelling
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Dong, W.; Yang, K.; Xu, Q.-L.; Yang, Y.-L. A Predictive Risk Model for A(H7N9) Human Infections Based on Spatial-Temporal Autocorrelation and Risk Factors: China, 2013–2014. Int. J. Environ. Res. Public Health 2015, 12, 15204-15221.

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