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    Modelling Spatiotemporal Patterns of Typhoid Cases Between 2005 and 2009 Using Spatial Statistics

    Access Status
    Fulltext not available
    Authors
    Dewan, Ashraf
    Corner, Robert
    Hashizume, M.
    Date
    2013
    Type
    Book Chapter
    
    Metadata
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    Citation
    Dewan, Ashraf M. and Corner, Robert J. and Hashizume, Masahiro. 2014. Modelling Spatiotemporal Patterns of Typhoid Cases Between 2005 and 2009 Using Spatial Statistics, in Dewan, A. and Corner, R. (ed), Dhaka Megacity – Geospatial Perspectives on Urbanisation, Environment and Health, pp. 345-366. America: Springer Geography.
    Source Title
    Dhaka Megacity – Geospatial Perspectives on Urbanisation, Environment and Health
    DOI
    10.1007/978-94-007-6735-5_19
    ISBN
    978-94-007-6734-8
    URI
    http://hdl.handle.net/20.500.11937/43949
    Collection
    • Curtin Research Publications
    Abstract

    The objectives of this study were to analyse spatiotemporal patterns of reported typhoid cases between 2005 and 2009 and to model the spatial relationship of demographic and socioeconomic factors associated with the occurrence of typhoid in Dhaka. The lowest level census units were used as the scale of analysis. Data in relation to typhoid was collected from 11 major hospitals by scanning individual medical reports, while demographic and socioeconomic variables were encoded in GIS from the population censuses of 2001 and 2011. Global (Moran’s /) and local models (G i *) were used to test how census districts were dispersed or clustered over space. The spatial relationships were modelled through ordinary least square (OLS) and geographically weighted regression (GWR) techniques. Spatial pattern analysis as measured by Moran’s / demonstrated that the distribution of the affected communities with typhoid was spatially autocorrelated across the study period, 2005–2009. Hotspot analysis using local G i * indicated large variation in the locations and sizes of clusters. The demographic model outperformed the socioeconomic and demographic + socioeconomic models in predicting the occurrence of typhoid in the study area. The results of this study are of great aid to identify spatial risk factors, essential to develop the control and prevention measures to specific areas.

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