Abstract: 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. Conclusion　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.

Key words: Schistosomiasis, Spatiotemporal analysis, Spatiotemporal filtering model, Anhui Province

摘要： 目的　探索时空滤波模型用于血吸虫病报告病例数据分析的可行性，从而为血吸虫病防治复杂数据分析提供参考。方法　于安徽省疾病预防控制中心收集1997—2010年安徽省血吸虫病报告病例数据，计算各年度人群血吸虫感染率。自国家气象信息中心获取1997—2010年安徽省血吸虫病报告病例所在县（市、区）气象站月平均气温和降水量等气象数据，采用反距离加权法对气象数据进行插值，计算各县（市、区）年均气温和年均降水量。通过ArcGIS 10.0软件提取血吸虫病报告病例所在县（市、区）质心，计算各质心到长江的欧氏距离并将其作为该县（市、区）距长江距离。计算1997—2010年各年份安徽省人群血吸虫感染率全局莫兰指数（global Moran’s I）以分析其空间自相关性。采用Rook邻接构建空间权重矩阵，构建一阶时间权重矩阵以量化疾病随时间变化的关系，随后构造时空结构矩阵。基于时空结构矩阵与血吸虫病报告病例数据构建负二项模型，以模型残差与候选集特征向量建立线性模型，通过逐步回归法进行筛选，确定最优子集构成时空滤波器后，采用负二项模型构建时空滤波模型。构建负二项模型、贝叶斯空间模型和贝叶斯时空模型，并与时空滤波模型进行比较以验证时空滤波模型性能，并对各模型进行交叉验证。通过偏差信息准则（deviance information criterion，DIC）、渡边⁃赤池信息准则（Watanabe⁃Akaike information criterion，WAIC）评估拟合优度，通过均方误差（mean squared error，MSE）评估模型验证效果，通过系数及其95%置信区间（confidence interval，CI）评估结果准确性，通过模型运行时间评估计算效率。选取Moran’s I值较大的4个特征向量，通过其示意图识别具有自相关性的区域，揭示特定区域在时空模式中的差异。结果　建立的各模型中，时空滤波模型拟合优度最高（DIC = 3 240.70，WAIC = 3 257.80）、模型验证效果最好（MSE = 42 617.52）、运行时间为3.18 s，表现最佳。各模型建模结果中，距长江距离与血吸虫病病例数均呈负相关（系数值= -4.93 ~ -3.78，95% CI均不包含0）；平均气温和平均降水量对血吸虫病病例数的影响均无统计学意义（95% CI均包含0）。特征向量示意图提示，安徽省血吸虫病传播可能与水系有关，局部性聚集模式主要集中在该省血吸虫病流行区北部和西部。结论　时空滤波模型是一种有效的时空分析方法，其建模思路简单、使用方便、结果准确、灵活性较高，可作为传统复杂时空模型在血吸虫病研究中的一种有效替代方法。

关键词: 血吸虫病, 时空分析, 时空滤波模型, 安徽省