Research Article: Spatiotemporal Transmission Dynamics of Hemorrhagic Fever with Renal Syndrome in China, 2005–2012

Date Published: November 20, 2014

Publisher: Public Library of Science

Author(s): Wen-Yi Zhang, Li-Ya Wang, Yun-Xi Liu, Wen-Wu Yin, Wen-Biao Hu, Ricardo J. Soares. Magalhaes, Fan Ding, Hai-Long Sun, Hang Zhou, Shen-Long Li, Ubydul Haque, Shi-Lu Tong, Gregory E. Glass, Peng Bi, Archie C. A. Clements, Qi-Yong Liu, Cheng-Yi Li, Daniel G. Bausch. http://doi.org/10.1371/journal.pntd.0003344

Abstract: BackgroundHemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease caused by many serotypes of hantaviruses. In China, HFRS has been recognized as a severe public health problem with 90% of the total reported cases in the world. This study describes the spatiotemporal dynamics of HFRS cases in China and identifies the regions, time, and populations at highest risk, which could help the planning and implementation of key preventative measures.MethodsData on all reported HFRS cases at the county level from January 2005 to December 2012 were collected from Chinese Center for Disease Control and Prevention. Geographic Information System-based spatiotemporal analyses including Local Indicators of Spatial Association and Kulldorff’s space-time scan statistic were performed to detect local high-risk space-time clusters of HFRS in China. In addition, cases from high-risk and low-risk counties were compared to identify significant demographic differences.ResultsA total of 100,868 cases were reported during 2005–2012 in mainland China. There were significant variations in the spatiotemporal dynamics of HFRS. HFRS cases occurred most frequently in June, November, and December. There was a significant positive spatial autocorrelation of HFRS incidence during the study periods, with Moran’s I values ranging from 0.46 to 0.56 (P<0.05). Several distinct HFRS cluster areas were identified, mainly concentrated in northeastern, central, and eastern of China. Compared with cases from low-risk areas, a higher proportion of cases were younger, non-farmer, and floating residents in high-risk counties.ConclusionsThis study identified significant space-time clusters of HFRS in China during 2005–2012 indicating that preventative strategies for HFRS should be particularly focused on the northeastern, central, and eastern of China to achieve the most cost-effective outcomes.

Partial Text: Hemorrhagic fever with renal syndrome (HFRS) is a viral zoonosis caused by different species of hantaviruses. The disease is characterized by fever, hemorrhage, headache, back pain, abdominal pain, acute renal dysfunction and hypotension [1]. In China, HFRS is mainly caused by two types of hantaviruses: Hantaan virus (HTNV) and Seoul virus (SEOV), which have Apodemus agrarius (striped field mouse) and Rattus norvegicus (brown rat), respectively as their major rodent hosts [2], [3]. Transmission of hantaviruses from rodents to humans is believed to occur through inhalation of aerosols contaminated by virus shed in excreta, saliva and urine of infected animals [4], [5].

There have been significant changes in the spatiotemporal dynamics of HFRS throughout mainland China during the recent past (2005–2012) including the appearance of a new, major cluster of HFRS in central China. This shift became evident by applying LISA and other spatial scan statistics analysis to historical surveillance data. Identifying clusters is of practical importance by providing health authorities with a rational basis to redirect their efforts to new or more specific high risk regions for environmental management, and implementing public health interventions, such as vaccinations and health education, in high-risk populations. Our study also demonstrates that GIS-based spatiotemporal analyses serve as useful tools to analyze the changing patterns of infectious diseases that have wider application in the field of surveillance and infectious disease management [6], [10], [13], [23], [24], [26], [28]. Additionally, these methods serve as an important strategy to better identify and characterize the dynamics of environmental correlates influencing pathogen maintenance.

Source:

http://doi.org/10.1371/journal.pntd.0003344

 

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