ArticleInternational journal of environmental research and public health2019
Spatiotemporal Variation and Hotspot Detection of the Avian Influenza A(H7N9) Virus in China, 2013⁻2017.
Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Small-scale spatiotemporal epidemiology of notifiable infectious diseases in China: a systematic review.BMC infectious diseases · 2022Pooled it
- Electronic data collection, management and analysis tools used for outbreak response in low- and middle-income countries: a systematic review and stakeholder survey.BMC public health · 2021Pooled it
- Review
- Based on the MaxEnt model the analysis of influencing factors and simulation of potential risk areas of human infection with avian influenza A (H7N9) in China.Frontiers in cellular and infection microbiology · 2024Article
- Rapid surveillance of COVID-19 by timely detection of geographically robust, alive and emerging hotspots using Particle Swarm Optimizer.Applied geography (Sevenoaks, England) · 2022Article
- Analysis of spatial-temporal distribution of notifiable respiratory infectious diseases in Shandong Province, China during 2005-2014.BMC public health · 2021Article
- Emergence of human avian influenza A(H7N9) virus infections in Wenshan City in Southwest China, 2017.BMC infectious diseases · 2020Article
- Surveillance Routing of COVID-19 Infection Spread Using an Intelligent Infectious Diseases Algorithm.IEEE access : practical innovations, open solutions · 2020Article
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4 authors.
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Abstract
This study aims to describe the spatial and temporal characteristics of human infections with H7N9 virus in China using data from 19 February 2013 to 30 September 2017 extracted from Centre for Health Protection of the Department of Health (CHP) and electronic databases managed by China's Center for Disease Control (CDC) and provincial CDCs synthetically using the Geographic Information System (GIS) software ArcMap™ 10.2 and SaTScan. Based on the multiple analyses of the A(H7N9) epidemics, there was a strong seasonal pattern in A(H7N9) virus infection, with high activity in the first quarter of the year, especially in January, February, and April, and a gradual dying out in the third quarter. Spatial distribution analysis indicated that Eastern China contained the most severely affected areas, such as Zhejiang Province, and the distribution shifted from coastline areas to more inland areas over time. In addition, the cases exhibited local spatial aggregation, with high-risk areas most found in the southeast coastal regions of China. Shanghai, Jiangsu, Zhejiang, and Guangdong were the high-risk epidemic areas, which should arouse the attention of local governments. A strong cluster from 9 April 2017 to 24 June 2017 was also identified in Northern China, and there were many secondary clusters in Eastern and Southern China, especially in Zhejiang, Fujian, Jiangsu, and Guangdong Provinces. Our results suggested that the spatial-temporal clustering of H7N9 in China is fundamentally different, and is expected to contribute to accumulating knowledge on the changing temporal patterns and spatial dissemination during the fifth epidemic and provide data to enable adequate preparation against the next epidemic.
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