Cluster of Human Infections with Avian Influenza A (H7N9) Cases: A Temporal and Spatial Analysis

Yi Zhang, Zhixiong Shen, Chunna Ma, Chengsheng Jiang, Cindy Feng, Nivedita Shankar, Peng Yang, Wenjie Sun and Quanyi Wang
Additional contact information
Yi Zhang: Beijing Center for Disease Prevention and Control (CDC), Beijing 100013, China
Zhixiong Shen: Department of Earth and Environmental Sciences, Tulane University, New Orleans, LA 70118, USA
Chunna Ma: Beijing Center for Disease Prevention and Control (CDC), Beijing 100013, China
Chengsheng Jiang: Maryland Institute for Applied Environmental Health, School of Public Health in University of Maryland, College Park, MD 20742, USA
Cindy Feng: School of Public Health & The Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK S7N 5E5, Canada
Nivedita Shankar: Saw Swee Hock School of Public Health, National University of Singapore, Singapore
Peng Yang: Beijing Center for Disease Prevention and Control (CDC), Beijing 100013, China
Wenjie Sun: School of Food Science, Guangdong Pharmaceutical University, Zhongshan 528458, China
Quanyi Wang: Beijing Center for Disease Prevention and Control (CDC), Beijing 100013, China

IJERPH, 2015, vol. 12, issue 1, 1-13

Abstract: Objectives : This study aims to describe the spatial and temporal characteristics of human infections with H7N9 virus in China using data from February 2013 to March 2014 from the websites of every province’s Population and Family Planning Commission. Methods : A human infection with H7N9 virus dataset was summarized by county to analyze its spatial clustering, and by date of illness onset to analyze its space-time clustering using the ESRI ® Geographic Information System (GIS) software ArcMap™ 10.1 and SatScan. Results : Based on active surveillance data, the distribution map of H7N9 cases shows that compared to the rest of China, the areas from near the Yangtze River delta (YRD) to farther south around the Pearl River delta (PRD) had the highest densities of H7N9 cases. The case data shows a strong space-time clustering in the areas on and near the YRD from 26 March to 18 April 2013 and a weak space-time clustering only in the areas on and near the PRD between 3 and 4 February 2014. However, for the rest of the study period, H7N9 cases were spatial-temporally randomly distributed. Conclusions : Our results suggested that the spatial-temporal clustering of H7N9 in China between 2013 and 2014 is fundamentally different.

Keywords: H7N9; influenza A; GIS; SatScan; space-time; clustering (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2015
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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