tetano
Editor, Senior Moderator
Geospat Health. 2014 May;8(2):429-435.
Spatial distribution and risk factors of influenza in Jiangsu province, China, based on geographical information system.
Zhang JC, Liu WD, Liang Q, Hu JL, Norris J, Wu Y, Bao CJ, Tang FY, Huang P, Zhao Y, Yu RB, Zhou MH, Shen HB, Chen F, Peng ZH.
Abstract
Influenza poses a constant, heavy burden on society. Recent research has focused on ecological factors associated with influenza incidence and has also studied influenza with respect to its geographic spread at different scales. This research explores the temporal and spatial parameters of influenza and identifies factors influencing its transmission. A spatial autocorrelation analysis, a spatial-temporal cluster analysis and a spatial regression analysis of influenza rates, carried out in Jiangsu province from 2004 to 2011, found that influenza rates to be spatially dependent in 2004, 2005, 2006 and 2008. South-western districts consistently revealed hotspots of high-incidence influenza. The regression analysis indicates that railways, rivers and lakes are important predictive environmental variables for influenza risk. A better understanding of the epidemic pattern and ecological factors associated with pandemic influenza should benefit public health officials with respect to prevention and controlling measures during future epidemics.
PMID:
24893019
[PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/24893019
Spatial distribution and risk factors of influenza in Jiangsu province, China, based on geographical information system.
Zhang JC, Liu WD, Liang Q, Hu JL, Norris J, Wu Y, Bao CJ, Tang FY, Huang P, Zhao Y, Yu RB, Zhou MH, Shen HB, Chen F, Peng ZH.
Abstract
Influenza poses a constant, heavy burden on society. Recent research has focused on ecological factors associated with influenza incidence and has also studied influenza with respect to its geographic spread at different scales. This research explores the temporal and spatial parameters of influenza and identifies factors influencing its transmission. A spatial autocorrelation analysis, a spatial-temporal cluster analysis and a spatial regression analysis of influenza rates, carried out in Jiangsu province from 2004 to 2011, found that influenza rates to be spatially dependent in 2004, 2005, 2006 and 2008. South-western districts consistently revealed hotspots of high-incidence influenza. The regression analysis indicates that railways, rivers and lakes are important predictive environmental variables for influenza risk. A better understanding of the epidemic pattern and ecological factors associated with pandemic influenza should benefit public health officials with respect to prevention and controlling measures during future epidemics.
PMID:
24893019
[PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/24893019