Xian-Hong Wang

A strategy to control transmission of Schistosoma japonicum in China.

BACKGROUND Schistosoma japonicum causes an infection involving humans, livestock, and snails and is a significant cause of morbidity in China. METHODS We evaluated a comprehensive control strategy in two intervention villages and two control villages along Poyang Lake in the southeastern province of Jiangxi, where annual synchronous chemotherapy is routinely used. New interventions, implemented from 2005 through 2007, included removing cattle from snail-infested grasslands, providing farmers with mechanized farm equipment, improving sanitation by supplying tap water and building lavatories and latrines, providing boats with fecal-matter containers, and implementing an intensive health-education program. During the intervention period, we observed changes in S. japonicum infection in humans, measured the rate of infection in snails, and tested the infectivity of lake water in mice. RESULTS After three transmission seasons, the rate of infection in humans decreased to less than 1.0% in the intervention villages, from 11.3% to 0.7% in one village and from 4.0% to 0.9% in the other (P<0.001 for both comparisons). The rate of infection in humans in control villages fluctuated but remained at baseline levels. In intervention villages, the percentage of sampling sites with infected snails decreased from 2.2% to 0.1% in one grassland area and from 0.3% to no infection in the other (P<0.001 for both comparisons). The rate of infection in mice after exposure to lake water decreased from 79% to no infection (P<0.001). CONCLUSIONS A comprehensive control strategy based on interventions to reduce the rate of transmission of S. japonicum infection from cattle and humans to snails was highly effective. These interventions have been adopted as the national strategy to control schistosomiasis in China.

Epidemiology of Schistosomiasis in the People’s Republic of China, 2004

Although the number of human infections decreased, human prevalence increased from 4.9% in 1995 to 5.1% in 2004.

Bayesian spatio-temporal modeling of Schistosoma japonicum prevalence data in the absence of a diagnostic 'gold' standard.

Background Spatial modeling is increasingly utilized to elucidate relationships between demographic, environmental, and socioeconomic factors, and infectious disease prevalence data. However, there is a paucity of studies focusing on spatio-temporal modeling that take into account the uncertainty of diagnostic techniques. Methodology/Principal Findings We obtained Schistosoma japonicum prevalence data, based on a standardized indirect hemagglutination assay (IHA), from annual reports from 114 schistosome-endemic villages in Dangtu County, southeastern part of the Peoples Republic of China, for the period 1995 to 2004. Environmental data were extracted from satellite images. Socioeconomic data were available from village registries. We used Bayesian spatio-temporal models, accounting for the sensitivity and specificity of the IHA test via an equation derived from the law of total probability, to relate the observed with the ‘true’ prevalence. The risk of S. japonicum was positively associated with the mean land surface temperature, and negatively correlated with the mean normalized difference vegetation index and distance to the nearest water body. There was no significant association between S. japonicum and socioeconomic status of the villages surveyed. The spatial correlation structures of the observed S. japonicum seroprevalence and the estimated infection prevalence differed from one year to another. Variance estimates based on a model adjusted for the diagnostic error were larger than unadjusted models. The generated prediction map for 2005 showed that most of the former and current infections occur in close proximity to the Yangtze River. Conclusion/Significance 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.

An integrated approach to identify distribution of Oncomelania hupensis, the intermediate host of Schistosoma japonicum, in a mountainous region in China.

The aim of this study is to better understand ecological variability related to the distribution of Oncomelania hupensis, the snail intermediate host of Schistosoma japonicum, and predict the spatial distribution of O. hupensis at the local scale in order to develop a more effective control strategy for schistosomiasis in the hilly and mountainous regions of China. A two-pronged approach was applied in this study consisting of a landscape pattern analysis complemented with Bayesian spatial modelling. The parasitological data were collected by cross-sectional surveys carried out in 11 villages in 2006 and mapped based on global positioning system (GPS) coordinates. Environmental surrogates and landscape metrics were derived from remotely-sensed images and land-cover/land-use classification data. Bayesian non-spatial and spatial models were applied to investigate the variation of snail density in relation to environmental surrogates and landscape metrics at the local scale. A Bayesian spatial model, validated by the deviance information criterion (DIC), was found to be the best-fitting model. The mean shape index (MSI) and Shannons evenness indexes (SEI) were significantly associated with snail density. These findings suggest that decreasing the heterogeneity of the landscape can reduce snail density. A prediction maps were generated by the Bayesian model together with environmental surrogates and landscape metrics. In conclusion, the risk areas of snail distribution at the local scale can be identified using an integrated approach with landscape pattern analysis supported by remote sensing and GIS technologies, as well as Bayesian modelling.