Monitoring schistosomiasis risk in East China over space and time using a Bayesian hierarchical modeling approach.

ABSTRACT: Schistosomiasis remains a major public health problem and causes substantial economic impact in east China, particularly along the Yangtze River Basin. Disease forecasting and surveillance can assist in the development and implementation of more effective intervention measures to control disease. In this study, we applied a Bayesian hierarchical spatio-temporal model to describe trends in schistosomiasis risk in Anhui Province, China, using annual parasitological and environmental data for the period 1997-2010. A computationally efficient approach-Integrated Nested Laplace Approximation-was used for model inference. A zero-inflated, negative binomial model best described the spatio-temporal dynamics of schistosomiasis risk. It predicted that the disease risk would generally be low and stab

SUBMITTER: Hu Y 

PROVIDER: S-EPMC4823756 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature