Research on Spatial Distribution of Oncomelania Hupensis and the Snail Control Method in Mountainous Regions
|Course||Epidemiology and Biostatistics,|
|Keywords||Schistosomiasis japonica Hupensis Mountainous Geographic Information Systems Spatial distribution Spatial statistics Kriging method Remote sensing Spatial autoregressive Niclosamide Molluscicidal|
Sichuan hills endemic areas. Snails is the only intermediate host of Schistosoma japonicum, its geographical distribution affects the spatial distribution of schistosomiasis. Correct analysis of the the snail distribution of space characterized further by analysis of the environmental factors that affect the snail to predict snail distribution has a very important significance for controlling snails. Eradication of snails is an important part of the schistosomiasis control strategies the mountain snail complex environment, it is necessary to explore the more targeted and better methods of snail control. The research through the collection of snail, digitized topographic maps and remote sensing data, extract relevant environmental factors, Pugh County, Sichuan Province endemic areas snail distribution spatial database, based on the spatial characteristics of spatial analysis technology snail distribution its relationship with environmental factors, and to explore the characteristics of mountain water seepage grass the niclosamide heap deposited snail control methods to provide a scientific basis to predict the the snail distribution of control snails and schistosomiasis prevention, mainly including the following three aspects: The first part of the alpine endemic areas snails purpose of the spatial characteristics of study analysis of the spatial characteristics of the mountain snail distribution, predicted the entire Pugh County schistosomiasis endemic areas snail distribution, control snails and schistosomiasis prevention and treatment to provide a scientific basis for the mountain. Pugh County, Sichuan Province snail distribution of geographic information systems (Geographic Information System, GIS), nearest neighbor analysis, the distribution of spatial autocorrelation analysis and spatial scan statistic mountain snails spatial autocorrelation and aggregation, with the variogram quantitative description the snail distribution of spatial variability, using ordinary kriging method snail distribution forecast map. Results Pugh County schistosomiasis endemic areas of snail habitats spatial location distribution aggregated distribution mode. 28 Pugh County snail distribution in the presence of spatial autocorrelation, the screw box appears the rate of global Moran's I index was 0.095 (P lt; 0.05) General G index was 0.067 (P = 0.405), and local spatial autocorrelation analysis the snail habitats LISA value was statistically significant (P lt; 0.05), these environments can be divided into high - high and low - low, low - high and high - low the four spatial association mode. The spatial scan statistic method of detection of 24 snail habitats enclave enclave of 14 high-value, 10 low-value accumulation area (P lt; 0.05), overlay analysis found local spatial autocorrelation results more consistent . The snail distribution of spatial variability meet the spherical model, spatial autocorrelation changes with distance, change away as 0.2542. Established using ordinary Kriging the snail distribution of prediction model, cross-validation results show that the model unbiasedness and optimality are better. Conclusion Pugh mountain snail distribution in the presence of space autocorrelation snail habitats in space was a certain degree of aggregation that the existence of the spatial distribution of heterogeneity, spatial variability meet the spherical model, Kriging method can be used to predict the mountain snail distribution . Snail distribution in the second part of the alpine endemic areas with environmental factors of space snail distribution and environmental factors regression model to analyze the relationship between spatial autocorrelation regression analysis purposes, in order to accurately predict the snails distribution. Collected Pugh County, Sichuan Province of snail status information and remote sensing data, extract relevant environmental factors, respectively, with the correlation analysis, linear regression and spatial autoregressive research snail distribution and environmental factors. Results Descriptive analysis found the snail habitats environmental indicators are in the smaller range of specific fluctuations. Spearman rank correlation analysis show snail distribution and normalized vegetation index (Normalized Difference Vegetation the Index, NDVI), humidity index (Moisture Index, MI) was significant with are related (P lt; 0.05), with the surface temperature (Land Surface Temperature, LST ), the elevation was negatively correlated, but no statistical significance (P gt; 0.05). Spatial dependence tests found snail distribution and NDVI, MI, LST. Elevation of environmental factors such as multiple linear regression model residuals with spatial autocorrelation (Moran's 1 = 0.242, P lt; 0.05) is not suitable for linear regression analysis . Lagrange multiplier test statistic prompted to select the spatial lag model (spatial lag model, SLM) analysis. SLM model spatial autocorrelation coefficient p = 0.249 (P lt; 0.05), the goodness of fit of the model to better R2 from 0.368 to 0.674 linear regression model. The SLM model results indicate that Snail density the NDVI, MI was positively correlated, and the regression coefficient is statistically significant (P lt; 0.05). Conclusions space autoregressive models can better reflect the relationship between snail density and environmental factors the snail distribution to accurately predict, is a powerful tool for processing space. The third part alpine endemic areas niclosamidum heaps deposited molluscicidal research purpose content analysis of of niclosamide heaps deposited molluscacide law in the soil, and to explore its trend over time its laboratory molluscicidal effect reasonable amount of medication. Method to select the six test points in Toz Township,, Puig County, in Sichuan Province, the implementation of niclosamide stack deposited molluscicidal the pesticide dose 16,8,4,2,1 g/m2 and 0 g/m2 (control group). Spraying day and 5 months after application, soil samples were collected by ultrasound extraction, centrifugation, concentrated later determined by high performance liquid chromatography which niclosamide content. Laboratory molluscicidal 3 days and 7 days (3d, 7d) snails mortality trials were calculated. Results of each dose group topsoil and deep soil niclosamide content differences were not statistically significant (P gt; 0.05). And above 4 g/m2 dose group after application can detect five soil niclosamide. With niclosamide reduced content in the soil, after application of 0, 5 groups of surface and deep soil samples 3d, 7d snails mortality showed a downward trend (P lt; 0.05). 5 months after application of 4 g/m2 group of the surface soil samples 3d, 7d snails mortality were 5.33%, 9.33%, higher than that of the control group (P lt; 0.05). The conclusion heap deposited molluscicidal law is a more effective method of molluscicidal recommended field use reasonable dose 4g/m2.