Analysis Of High Risk Areas Of Snail Distribution And Bayesian Spatial-temporal Modeling Based On Town Polygon

In spite of the great achievements in the national schistosomiasis control during the past half century, the new century will indeed be confronted by the great challenges. The number of the schistosomiasis decreased sharply, but the acute schistosomiasis increased. The drugs for the schistosomiasis were effective, but the long-term chemotherapy decreased compliance of the patients in the high risk areas. Snail habitats existed wildly, but the percent of snail control areas was relatively small. In addition, there were some new problems including global warming, floating population increase, limited financial supports and so on. The focus of schistosomiasis control was in the lake and marshland areas. In our study, modern spatial analysis technology, multilevel model and Bayesian spatial-temporal model were complemented and analyzed based on the data collection of schistosomiasis control program in Anhui province, in order to solve the key problems from the practical work and provide suggestions for the development of integrated schistosomiasis control program. This study can be referenced by the other related diseases for clustering and spatial-temporal analysis.Part I Clustering analysis of snail distribution and schistosomiasisThis part aimed to analyze the spatial clustering of schistosomiasis and snail for the high risk areas based on data collection in Anhui province and indicate the spatial-temporal association and mechanism for the development of integrated schistosomiasis control program. Two variables of the prevalence rate and the percentage of snail areas were computed according to the data collection of schstosomiasis prevalence in Anhui province from 2000 to 2008 and the spatial analysis database was constituted by the two variables matched with the spatial database of polygon. Global Autocorrelation Analysis, Local Autocorrelation Analysis and spatial scan statistics approach through moving windows were complemented to detect the clusters of schstosomiasis and snails. The results were visualized through the software of ArcGIS.For the variable of the percentage of snail areas, the spatial clustering of Global autocorrelation analysis were statistically significant from 2000 to 2008(Moran's I>0, P<0.05). And the clusters by Local autocorrelation analysis and SaTScan analysis were almost consistent. The clusters of different radius by SaTScan analysis covered most of the towns with spatial clustering of high values of Local autocorrelation analysis and it didn't appear to cluster more towns with the positive spatial clustering(T=1.85,P>0.05). The result of exploration by SaTScan suggested that the cluster area of the index mainly gathered in the band area 62 kilometers far away from both sides of the Yangtze River. The main clusters located near the downstream of Yangtze River through Anhui province before 2005. However, the clusters moved to the upstream since 2005.For the variable of prevalence rate of schistosomiasis, the results from Global Autocorrelation Analysis were not statistically significant (P>0.05) during the periods of 2000 to 2001 and 2005 to 2007. A few towns covered by small clusters were detected by Local Autocorrelation Analysis, however, the positions of the clusters were different from the result of SaTScan anslysis and distributed randomly. On the other hand, it was significant that some clusters were detected by Global Autocorrelation Analysis from 2002 to 2004 and in 2008(Moran's I>0,P<0.05), but the clustering effect was weak. The results were consistent between the Local autocorrelation analysis and SaTScan analysis. The clusters of different radiuses by SaTScan analysis covered most of the towns with spatial clustering of high values of Local Autocorrelation Analysis. According to the analysis of the three methods mentioned above, it was proved that the spatial clustering was significant to the prevalence rate of schistosomiasis from 2002 to 2004 and in 2008.Global autocorrelation analysis can identify the clustering from the global view. Local Autocorrelation Analysis can detect the spatial association and the locations with positive spatial clustering with high values. The cluster centers and radius can be identified by SaTScan analysis. The three methods can be combined and implemented gradually and the integrated results are more systematic and comprehensive. Part?Establishment and analysis of Multilevel Model of snail distributionThis part aimed to study the time trend of snail distribution based on town data collection adjusted by the confounding factors and identified the significant factors influencing the snail distribution in order to control the spatial autocorrelation by Bayesian spatial-temporal model. Growth Model of Multilevel model was used to adjust the within-group autocorrelation and study the effect of environmental factors(Normalized Difference Vegetation Index, Surface Temperature), climatic factors(Average Annual Precipitation, Annual Extreme Minimum Temperature), snail elimination and local position.The results of Empty model showed that ICC was 84.17% and the variance of the random intercept was statistically significant(t=8.19, P<0.01), so that ICC was significant and there was a great autocorrelation between the adjacent towns. It was indicated that the multilevel model was preferable to the classical statistical model for the longitudinal study.The model including the covariants with time was judged as the best model to fit our data. The variances of both the random intercept and slope were significant (?u02 =33.57,t=5.81, P<0.01;?u12=0.74, t=5.87, P<0.01). The results indicated that both the initial probability of snail and the rate of change of the probability were different and changing with the time moving for different towns. The significant covariance (?u012=-3.02,t=-4.71, P<0.01) suggested that if the initial probability of snail was great, the rate of change of snail probability would be small.The index of distance from the Yangtze River to the towns had the effect to the probability of snail for different towns (t=-8.03, P<0.01). The probability of snail would decreased with the distance increase (P<0.01). The effect of snail elimination and Land Surface Temperature were significant(P<0.05). Although the snail elimination was implemented in some towns, the probability of snail in these towns would be greater than that without snail elimination (95%CI:2.9,6.7). The probability of towns with Land Surface Temperature above 27?was smaller than that of Land Surface Temperature below 27?(95%CI:0.5,0.9).Part III Establishment and analysis of Bayesian spatial-temporal model for snail distributionThis part aimed to study the time trend and spatial clustering for quantitative measurement adjusted by the significant factors from Multilevel Model and spatial autocorrelation. Non-spatial model, separate spatial-temporal model and spatial-temporal interaction model were modeled including risk factors adjusted from Multilevel Model.According to the DIC principle, the separate spatial-temporal model was selected as the best model to fit the longitudinal data. It indicated that the random spatial effect was significant every year respectively and had no interaction as the time went by. The probability of snail decreased at town level year by year(OR=0.87,95%CI:0.84, 0.90). The probability of snail of towns with Land Surface Temperature above 27?was 0.7 time of those with the temperature below 27?(OR=0.73,95%CI:0.59,0.90). The risk of snail for different towns decreased by 20% with the distance increase away from the Yangtze River every 10 km (OR=0.83,95%CI:0.76,0.91). Though some towns implemented the snail elimination, the risk of snail was 9 times than that of the towns without snail and any control measures(OR=9.97,95%CI:7.85,12.81). In general, when the towns were over 62 km far away from the Yangtze River, the risk of this area was 0.43 times that of the towns inside this zone adjusted by other risk factors and spatial correlation(OR=0.43,95%CI:0.22,0.84). The effect of the distance from town to the Yangtze River on the probability of snail was overestimated due to the spatial correlation. The snail elimination had same effect on the probability of snail as well. More generally, the towns with high risk of snail clustered in the upstream of the Yangtze River though Anhui province, especially near the boundary of Dongzhi County and Guichi District in Chizhou City. The probability of other towns decreased with the time going.It was suggested that the surveillance and snail elimination should be reinforced greatly to the former snail habitats, especially in the zone of 62km away from the both sides of the Yangtze River and with the Land Surface Temperature below 27?. Because the environment in these areas was suitable for the survival and reproduction of snail and people had more risks than other places to schistosomiasis infection. The snail control to this zone played an important role in controlling schistosomiasis. At the same time, it was necessary to strengthen the surveillance to the areas without detection of snail, because these areas had the greater rate of change of probability than other areas with snail as the time went on.