These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: [Using geographic information system to study on the association between epidemic areas and main animal hosts of hemorrhagic fever with renal syndrome in China].
    Author: Fang LQ, Li CY, Yang H, Wu XM, Yang H, Chen HX, Li XW, Cao WC.
    Journal: Zhonghua Liu Xing Bing Xue Za Zhi; 2004 Nov; 25(11):929-33. PubMed ID: 15769318.
    Abstract:
    OBJECTIVE: To analyze the association between types of epidemic areas and proportion of main animal hosts of hemorrhagic fever with renal syndrome (HFRS) in China. METHODS: Data on serotype of HFRS patients from 122 regions and species as well as number of animal hosts from 41 national surveillance sites were collected and managed. The map of the types of epidemic areas from 1991-1999 in China was produced using inverse distance weighted technique in the ArcGIS 8.1 software. The map of the proportion of host animals was then overlapped on it and the surveillance sites of HFRS were clustered in terms of the proportion of host animals. RESULTS: Variance in spatial distribution of the types of epidemic areas was observed. Two epidemic area types, namely Hantaan-dominant mixed epidemic areas and Soul-dominant mixed epidemic areas seemed to have transferred from the north-east to south-west of China in turns. There were more species of HFRS animal hosts in fields than those of in residential areas. The diversity of rodent species in fields was higher than that of the residential areas which predominant harbored rodent species of 2-3 kinds. Types of surveillance sites classified by cluster analysis of proportion of main animal hosts were consistent with the serotypes of HFRS patients. CONCLUSION: Through employment of spatial analysis technique based on GIS databases, the HFRS types of main epidemic area in China could be well predicted by surveillance data.
    [Abstract] [Full Text] [Related] [New Search]