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    Geostatistical mapping of the seasonal spread of under-reported dengue cases in Bangladesh

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    Author
    Sharmin, S; Glass, K; Viennet, E; Harley, D
    Date
    2018-11-01
    Source Title
    PLoS Neglected Tropical Diseases
    Publisher
    PUBLIC LIBRARY SCIENCE
    University of Melbourne Author/s
    Sharmin, Sifat
    Affiliation
    Medicine and Radiology
    Metadata
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    Document Type
    Journal Article
    Citations
    Sharmin, S., Glass, K., Viennet, E. & Harley, D. (2018). Geostatistical mapping of the seasonal spread of under-reported dengue cases in Bangladesh. PLOS NEGLECTED TROPICAL DISEASES, 12 (11), https://doi.org/10.1371/journal.pntd.0006947.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/253272
    DOI
    10.1371/journal.pntd.0006947
    Abstract
    Geographical mapping of dengue in resource-limited settings is crucial for targeting control interventions but is challenging due to the problem of zero-inflation because many cases are not reported. We developed a negative binomial generalised linear mixed effect model accounting for zero-inflation, spatial, and temporal random effects to investigate the spatial variation in monthly dengue cases in Bangladesh. The model was fitted to the district-level (64 districts) monthly reported dengue cases aggregated over the period 2000 to 2009 and Bayesian inference was performed using the integrated nested Laplace approximation. We found that mean monthly temperature and its interaction with mean monthly diurnal temperature range, lagged by two months were significantly associated with dengue incidence. Mean monthly rainfall at two months lag was positively associated with dengue incidence. Densely populated districts and districts bordering India or Myanmar had higher incidence than others. The model estimated that 92% of the annual dengue cases occurred between August and September. Cases were identified across the country with 94% in the capital Dhaka (located almost in the middle of the country). Less than half of the affected districts reported cases as observed from the surveillance data. The proportion reported varied by month with a higher proportion reported in high-incidence districts, but dropped towards the end of high transmission season.

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