Bayesian Spatio-Temporal Modeling of Schistosoma japonicum Prevalence Data in the Absence of a Diagnostic ‘Gold’ Standard
PLoS neglected tropical diseasesPublished 10 June 2008Open access
Xian-Hong Wang, Xiao‐Nong Zhou, Penelope Vounatsou, Chen Zhao, Jürg Utzinger, Kun Yang
Citations61
SJR quartileQ1
SJR score1.37
SNIP1.44
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TL;DR
Bayesian spatial-temporal modeling incorporating diagnostic uncertainty is a suitable approach for risk mapping S. japonicum prevalence data from annual reports from 114 schistosome-endemic villages in Dangtu County, southeastern part of the People's Republic of China, for the period 1995 to 2004.
Abstract
Bayesian spatial-temporal modeling incorporating diagnostic uncertainty is a suitable approach for risk mapping S. japonicum prevalence data. The Yangtze River and its tributaries govern schistosomiasis transmission in Dangtu County, but spatial correlation needs to be taken into consideration when making risk prediction at small scales.
Keywords
Immunology and MicrobiologyMedicineNursing
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