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Environmental factor analysis of cholera in China using remote sensing and geographical information systems.
Xu, M; Cao, C X; Wang, D C; Kan, B; Xu, Y F; Ni, X L; Zhu, Z C.
Afiliação
  • Xu M; State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing,China.
  • Cao CX; State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing,China.
  • Wang DC; State Key Laboratory for Infectious Disease Prevention and Control,Institute for Infectious Disease Control and Prevention,Chinese Center for Disease Control and Prevention,Beijing,China.
  • Kan B; State Key Laboratory for Infectious Disease Prevention and Control,Institute for Infectious Disease Control and Prevention,Chinese Center for Disease Control and Prevention,Beijing,China.
  • Xu YF; State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing,China.
  • Ni XL; State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing,China.
  • Zhu ZC; State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing,China.
Epidemiol Infect ; 144(5): 940-51, 2016 Apr.
Article em En | MEDLINE | ID: mdl-26464184
ABSTRACT
Cholera is one of a number of infectious diseases that appears to be influenced by climate, geography and other natural environments. This study analysed the environmental factors of the spatial distribution of cholera in China. It shows that temperature, precipitation, elevation, and distance to the coastline have significant impact on the distribution of cholera. It also reveals the oceanic environmental factors associated with cholera in Zhejiang, which is a coastal province of China, using both remote sensing (RS) and geographical information systems (GIS). The analysis has validated the correlation between indirect satellite measurements of sea surface temperature (SST), sea surface height (SSH) and ocean chlorophyll concentration (OCC) and the local number of cholera cases based on 8-year monthly data from 2001 to 2008. The results show the number of cholera cases has been strongly affected by the variables of SST, SSH and OCC. Utilizing this information, a cholera prediction model has been established based on the oceanic and climatic environmental factors. The model indicates that RS and GIS have great potential for designing an early warning system for cholera.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Cólera / Surtos de Doenças / Sistemas de Informação Geográfica / Meio Ambiente / Tecnologia de Sensoriamento Remoto Tipo de estudo: Prognostic_studies Limite: Humans País/Região como assunto: Asia Idioma: En Revista: Epidemiol Infect Assunto da revista: DOENCAS TRANSMISSIVEIS / EPIDEMIOLOGIA Ano de publicação: 2016 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Cólera / Surtos de Doenças / Sistemas de Informação Geográfica / Meio Ambiente / Tecnologia de Sensoriamento Remoto Tipo de estudo: Prognostic_studies Limite: Humans País/Região como assunto: Asia Idioma: En Revista: Epidemiol Infect Assunto da revista: DOENCAS TRANSMISSIVEIS / EPIDEMIOLOGIA Ano de publicação: 2016 Tipo de documento: Article País de afiliação: China