[Feasibility of the spatiotemporal filtering model for analyzing the spatiotemporal distribution of reported schistosomiasis cases].

Objective: To investigate the feasibility of the spatiotemporal filtering model in analysis of reported schistosomiasis cases, so as to provide insights into analysis of complicated data pertaining to schistosomiasis control.

Methods: Demographic and epidemiological data of reported schistosomiasis cases in Anhui Province from 1997 to 2010 were collected from Anhui Provincial Center for Disease Control and Prevention, and the annual prevalence of Schistosoma japonicum human infections was calculated. The meteorological data were captured from meteorological stations in counties (cities, districts) of Anhui Province where schistosomiasis cases were reported from 1997 to 2010 at the National Meteorological Information Center, including monthly average air temperature and precipitation. Meteorological data were interpolated using the inverse-distance weighting method, and the annual average air temperature and annual precipitation were calculated in each county (city, district). The centroid of the county (city, district) where schistosomiasis cases were reported was extracted using the software ArcGIS 10.0, and the Euclidean distance from each centroid to the Yangtze River was calculated as the distance between that county (city, district) and the Yangtze River. The global Moran's I of the prevalence of S. japonicum human infections in Anhui Province for each year from 1997 to 2010 were calculated to analyze the spatial autocorrelation. A spatial weight matrix was constructed using Rook adjacency, and a first-order temporal weight matrix was built to quantify the relationship between disease changes over time. Subsequently, a spatiotemporal structure matrix was constructed. A negative binomial model was built based on the spatiotemporal structure matrix and data pertaining to reported schistosomiasis cases, and a linear model was created between the residual of the model and candidate set feature vectors to determine the optimal subset composition of the spatiotemporal filter through stepwise regression. Then, a spatio-temporal filtering model was constructed using the negative binomial model. Negative binomial models, Bayesian spatial models, and Bayesian spatiotemporal models were constructed and compared with the spatiotemporal filtering model to validate the performance of the spatiotemporal filtering model, and cross-validation was conducted for each model. The goodness of fit was evaluated using the deviance information criterion (DIC) and Watanabe-Akaike information criterion (WAIC), and the effectiveness of model validation was assessed using mean squared error (MSE), while the accuracy of assessment results was assessed using coefficients and their 95% confidence intervals (CI), and the computational efficiency was assessed based on the running time of the model. The four feature vectors with the largest Moran's I values were selected to identify regions with autocorrelation through their schematic diagrams to investigate the differences in spatiotemporal patterns of specific regions.

Results: Of all models created, the spatiotemporal filtering model exhibited the highest goodness of fit (DIC = 3 240.70, WAIC = 3 257.80), the best model validation effectiveness (MSE = 42 617.52), and the runtime was 3.18 s, exhibiting the optimal performance. Across all modeling results, the distance from the Yangtze River showed a negative correlation with the number of reported schistosomiasis cases (coefficient values = -4.93 to -3.78, none of the 95% CIs included 0), and annual average air temperature or average precipitation posed no significant effects on numbers of reported schistosomiasis cases (both of the 95% CIs included 0). Schematic diagrams of feature vectors showed that the transmission of schistosomiasis might be associated with water systems in Anhui Province, and localized clustering patterns were primarily concentrated in the northern and western parts of schistosomiasis-endemic areas in the province.

Conclusions: The spatiotemporal filtering model is an effective spatiotemporal analysis characterized by simple modeling, user-friendly operation, accurate results and good flexibility, which may serve as an efficient alternative to conventional complex spatiotemporal models for data analysis in schistosomiasis researches.