【作者】 张志杰；

【导师】 姜庆五； 彭文祥； 周艺彪；

【作者基本信息】 复旦大学 ， 流行病与卫生统计学， 2008， 博士

【摘要】 近年来我国血吸虫病的疫情有所反弹呈漫延扩散的趋势,世界银行贷款项目以后血防投入资金的减少、广泛存在的钉螺孳生地、重复化疗导致的居民依从率下降以及化疗对控制血吸虫病传播的效果较弱等不利因素都对我国目前的血防控制策略提出了新的挑战。我们不得不在已有成绩的基础上重新思考血防控制策略的可持续性问题,利用有限的资源继续巩固血防所取得的成绩,为最终控制,甚至消灭我国的血吸虫病而努力。本课题选择安徽省池州市贵池区为研究现场,基于现代空间信息技术由浅入深、逐步准确地探测到了血吸虫病的高风险区域,确定了高危险的钉螺孳生地,并建立了具有指导性的空间分析思路与方法,为血吸虫病防治工作提供了更具有实际意义的指导作用,为空间流行病学提供了新的研究内容,课题共分为六部分。第一部分血吸虫病的空间描述性分析目的对贵池区的急血病例进行空间描述性分析,并建立相应的分析指标体系。方法通过回顾性调查方法,收集2001-2006年贵池区的急血病例资料,并进行空间定位。借鉴经典统计学中描述集中趋势和离散趋势的基本思想和犯罪学中犯罪事件的描述方法,提出了空间集中趋势和离散趋势的分析方法。选用加权均数中心和加权标准差椭圆对急血病例进行空间描述性分析,并与流行病学的常规描述结果进行比较。结果获得了空间描述性分析的常用统计指标的计算方法、应用条件及应用价值。贵池区急血病例的人口学特征在6年间变化无统计学差异,如:性别(p=0.42)、年龄(p=0.08)和职业(p=0.08);发病时间集中在7-10月份,12-3月份无病例发生。空间描述性分析发现秋浦河流域是贵池区血防控制的重点,并且发病中心有南移趋势。结论空间描述性分析与常规描述相结合能更加完整地评价血吸虫病的疫情。第二部分血吸虫病的空间分布状态研究目的探讨贵池区急血病例的空间分布状态,并建立相应的量化分析技术。方法基于病例间的空间距离提出了空间分析中疾病分布状态的量化分析指标—G、F、J和K函数,并用于探讨贵池区急血病例的空间分布状态。结果获得了4个定量统计指标的计算方法,并提出了边界效应的概念。对急血病例的分析结果显示,G和K函数始终位于空间随机分布的可信区间上方,F和J函数位于可信区间的下方。结论贵池区的急血病例在不考虑风险人群空间分布状态的情况下为聚集性的空间分布,为进一步寻找血吸虫病的高危险环境提供了初步证据。第三部分血吸虫病的空间聚集性分析目的在校正风险人群空间分布状态影响的基础上,从多个不同角度探讨贵池区急血病例的空间聚集性。方法以贵池区各村的人口数为权重,通过按容量比例概率抽样法获得与急血病例相同样本量的空间对照位置,指示风险人群的空间分布。通过Cuzick-Edwards方法、基于点过程一阶和二阶属性的方法以及空间动态窗口扫描统计量法对急血病例的空间聚集性进行全面分析,并相互比较验证。结果Cuzick-Edwards方法的分析结果表明急血病例具有显著的空间聚集性(p＜0.01);基于点过程二阶属性的分析方法显示在研究尺度小于13000m时急血病例具有空间聚集性(p＜0.05),并且聚集性先增大后减少;基于点过程一阶属性的方法和空间动态窗口扫描统计量法则探测到了两个一致的病例聚集区域,最可能聚集区的中心位置为东经117.43°,北纬30.67°,半径为5.63 km(LLR=19.56,p=0.001),位于秋浦河与长江的交叉口处;另一可能聚集区的中心位置为东经117.71°,北纬30.36°,半径为9.74 km(LLR=7.25,p=0.07),位于贵池区的东南部。结论贵池区的急血病例在考虑了风险人群异质性分布的情况下,依然具有空间聚集性,并且存在两个高风险的病例聚集区。第四部分血吸虫病高风险区域的探测第一节血吸虫病和钉螺的影响因素研究1、气温的大尺度研究目的从宏观尺度上分析气温对钉螺分布的影响,探讨敏感的气温指标。方法以北纬34°为界,选取相邻有螺和无螺省的气象站,以北编码为温度不适合钉螺生存的地区,以南编码为适合生存区,用t检验和logistic回归分析年极端低气温和年平均气温的作用,确定敏感气温指标,进而绘制相应的频率分布图,通过曲线的重叠部分确定钉螺可疑分布区的温度范围。结果共获得了37个有螺区和24个无螺区气象站的气温数据。有螺区和无螺区的年极端低气温和年平均气温的t检验结果均显示差异有统计学意义(t=-6.49,p＜0.01;t=-3.93,p＜0.01),并且无螺区的温度均低于有螺区,差值分别为6.72℃和3.02℃;logistic回归分析显示仅年极端低气温的影响有统计学意义(χ~2=15.69,p＜0.01);钉螺可疑分布区的气温范围为(-7.6℃,1.5℃)。结论年极端低气温在大尺度下是影响钉螺生存的敏感气温指标,在气温低于-7.6℃的地区钉螺不适宜生存。2、植被的小尺度研究目的研究湖沼地区植被自然状态的改变对钉螺分布的影响。方法从贵池区秋浦河沿岸随机选择一块滩地作为研究现场,设计矮草组、边界组、枯草组和对照组4组不同植被状态的研究区域,半个月后调查钉螺密度、土壤温度和土壤湿度,推断植被自然状态的改变引起钉螺分布的变化情况。结果枯草组钉螺密度(0.13只/0.11m~2)＜矮草组(32.1只/0.11m~2)＜对照组(49.07只/0.11m~2)＜边界组(53.6只/0.11m~2)。边界组与对照组的钉螺密度差异无统计学意义(p＞0.05),其余组间的差异均有统计学意义(p＜0.05)。枯草组土壤湿度大、土壤温度低与矮草组土壤湿度低、土壤温度高的环境均不利于钉螺生存。结论人为割倒植被后,钉螺由矮草组向边界组移动,钉螺分布发生相应变化,植被是钉螺分布的重要影响因素。3、钉螺密度影响因素的小尺度研究目的探讨小尺度下湖沼地区钉螺密度的影响因素。方法从贵池区秋浦河沿岸随机选择滩地作为研究现场,根据植被类型分层随机抽样,以交叉复核随机抽检法调查钉螺,同时收集螺框所在的高程、土温、气温、植被高度、土湿和植被类型6个变量,分别于2006年4月和9月各调查一次。应用广义线性模型进行模型的拟合,使用偏差量和AIC确定最佳模型结构,通过内部有效性和外部有效性来评价模型的预测效果,探讨相关因素的影响。结果建模样本量为162(框),变量间存在着复杂的相关性,钉螺数与植被高度正相关(r=0.36),与土湿负相关(r=-0.22),气温与土温正相关(r=0.59),土温与植被高度负相关(r=-0.36),土湿与土温和气温均负相关(r=-0.34和-0.12)。广义线性模型的最佳模型结构是以Gamma分布为误差分布、倒数为连接函数和均数平方为方差函数。模型拟合结果显示不同的植被对于钉螺密度的影响是不同的,植被高度对于钉螺密度是正向作用(t=-2.371,p=0.01897),土壤湿度对于钉螺密度是负向作用(t=3.124,p=0.00214),高程低的地方钉螺密度高(t=-3.202,p=0.00166),土温呈现出边界性作用(t=-1.989,p=0.04849),气温没有进入模型。结论小尺度下,土温、植被、高程和土湿是影响钉螺生存的重要因素,广义线性模型在建立钉螺密度的预测模型中具有良好地应用前景。第二节广义相加模型在血吸虫病高风险区域探测中的应用目的在考虑协变量的基础上进一步准确地探测血吸虫病的高风险区域。方法将从遥感图像提取的环境变量(归一化植被指数和地表温度)、数字高程模型提取的地形变量(高程和坡度)、数字化地图中计算的离水源距离以及病例对照的空间位置作为自变量,应用广义相加模型预测不同空间位置的血吸虫病发病风险。通过偏残差图和受试者工作特征曲线下面积(AUC)分别评价模型的拟合效果和预测能力,应用逐点P值表面法进行高风险区域的统计学检验,并与第三部分的结果进行比较。结果建立的模型具有很高的预测能力(AUC=0.911),小尺度下预测血吸虫病发病风险的重要因素依次为社会行为学因素、地理因素和环境因素。距离(χ~2=19.6879,p=0.0002)、空间位置坐标X(χ~2=11.7625,p=0.3809)、Y(χ~2=26.3038,p=0.0009)和高程(χ~2=13.4844,p=0.0037)对于小尺度下预测血吸虫病的发病风险具有统计学意义,分别是分段线性函数、二次曲线、线性函数和分段线性函数的关系。当高程＞86m时,随着高程的升高,血吸虫病的发病风险开始降低:在离危险水源1500m以内的区域具有相对稳定的高发病风险,离危险水源18000m以外的区域血吸虫病的发病风险较低。共探测到了四个有统计学意义的血吸虫病高风险区域,两个与第三部分的结果一致,但确定的聚集区域更加精确,另外两个是新发现的与高风险区域具有相似环境的适合钉螺孳生的潜在高风险区域。结论贵池区存在两个血吸虫病的高风险区域,两个潜在高风险区域。第五部分血吸虫病高风险区域内重点钉螺孳生地的确定目的确定贵池区的高风险钉螺孳生地。方法购买了两幅分别代表贵池区“枯水期”和“丰水期”的遥感图像。联合应用归一化水体指数和归一化植被指数两个指标提取钉螺孳生地,并进行现场验证,评价该方法的准确性。通过与第四部分探测到的4个血吸虫病高风险区域进行叠加分析,确定了贵池区的高风险钉螺孳生地,并通过地理信息系统提取它们的相关信息,根据经纬度坐标通过全球定位系统导航在现场寻找相应的钉螺孳生地并结合历史资料进行调查验证。结果钉螺孳生地提取方法的灵敏度和特异度分别为90%和100%,贵池区存在钉螺孳生地349个,总面积约为107 km~2,共确定了6个高风险的钉螺孳生地。结论以确定的6个高风险钉螺孳生地为中心,周围1500m的区域是贵池区血防控制的重点范围。第六部分钉螺统计分布规律的研究第一节钉螺现场调查方法的研究目的确定一种比较好的现场钉螺调查方法。方法从贵池区秋浦河沿岸的滩地中随机选择一个滩地作为研究现场,设计常规调查、个人重复调查、交叉重复调查和交叉复核随机抽检调查4种方法收集钉螺数据,从钉螺密度和漏捡率两个角度评价不同调查方法的优劣。结果个人重复调查、交叉重复调查和交叉复核随机抽检调查的钉螺密度差异无统计学差异(χ~2=3.873,p=0.144),而它们与常规调查的差异有统计学意义(U=309,p＜0.01):交叉复核随机抽检调查的漏捡率(0.57%)＜交叉重复调查(5.24%)＜个人重复调查的漏捡率(10.26%),并且差异有统计学意义(χ~2=37.44,p＜0.01)。结论交叉复核随机抽检调查法的效果最好,可用于现场的钉螺调查。第二节钉螺的统计分布规律研究目的研究湖北钉螺的统计分布规律。方法随机抽取贵池区秋浦河沿岸的4块滩地作为研究现场,2005年10月调查4块滩地的钉螺数据,2006年4月从中随机抽取2块滩地进行调查,比较同一季节不同滩地和不同季节同一滩地的钉螺结果,使用最大似然法分别拟合广义负二项分布(GNBD)和负二项分布(NBD),探讨钉螺的统计分布规律。结果不同季节、不同滩地的钉螺密度是不同的,但其分布形状均是相似的正偏态分布。GNBD能成功地拟合所有的钉螺数据,NBD则不能,并且拟合效果比GNBD差。钉螺生存环境的微小差异通过GNBD的参数可以灵敏地反映出來。结论GNBD比NBD能更好地反映钉螺分布的复杂性,具有良好地应用前景。第三节生物学行为和环境因素对钉螺分布的相对影响目的研究钉螺的生物学行为和环境因素对其分布的相对影响,正确认识钉螺的分布规律。方法在贵池区秋浦河沿岸随机选择一个滩地,确定200cm~*200cm的区域进行钉螺普查,并对其位置进行标记,然后测量每个钉螺的直角坐标。通过G和F函数初步描述钉螺的分布模式,然后应用L函数进行不同尺度分布模式的探讨,分离生物学行为和环境因素对钉螺分布的相对作用,并拟合钉螺分布的点过程模型。结果共普查了122cm~*172cm的范围,活螺数528只,该区域内钉螺的生物学行为是随机分布,环境因素使其表现为聚集性分布,其作用约为生物学行为的5.96倍。Poisson聚集性点过程能很好地拟合钉螺数据。结论不同尺度空间分布模式的探讨可以成功分离生物学行为和环境因素对钉螺分布的相对影响,环境因素对钉螺分布的影响更大。更多还原

【Abstract】 The epidemic situation of schistosomiasis japonica has rebound rapidly in China recently and showed a tendency of expanding.We cannot but reconsider the strategy of sustainable schistosomiasis control due to the decreased financial supports,extensive snail habitats, declined compliance rate due to repeated drug treatment,and bad effects for chemotherapy to interrupt schistosomiasis transmission.In this study,we selected Guichi region of Chizhou city,Anhui province as our study area.We precisely identified the high risk regions of schistosomiasis step by step and located the active transmission sites of snail habitats finally, which provided more practical directions for schistosomiasis control.At the same time,the systematical approaches for spatial analysis were established.It added new contents for spatial epidemiology.The whole study was divided into six parts.PartⅠSpatial descriptive analysis on schistosomiasis casesObjective To conduct descriptive analysis on acute schistosomiasis cases from the spatial point of view and establish the corresponding approaches.Methods Acute schistosomiasis cases were collected through retrospective survey method in Guichi region from 2001 to 2006 and their spatial positions were recorded by GPS machines.Borrowing ideas from descriptive analysis in classical statistics and methods to describe crime incidents,we put forward the approaches of describing spatial central and dispersion tendency.Weighted mean center and weighted standard deviance ellipse were used to analyze the data of schistosomiasis cases, and their results were compared with the conventional descriptive analysis to explore their merits.Results The computational methods,application conditions and potential merits on using these statistical indices to perform spatial descriptive analysis were systematically established and introduced.The demographic characteristics of these acute cases were not significantly different across the six years,such as gender(p=0.42),age(p=0.08) and occupation(p=0.08).The time of disease occurrence was mainly from July to October,while no cases existed between December and March.The results from spatial descriptive analysis showed that Qiupu River was the emphasis for schistosomiasis control in Guichi region and the spatial center of acute cases had a tendency to move southward.Conclusion Spatial descriptive analysis was valuable supplements for descriptive analysis in classical statistics, and their combination would give a more comprehensive description on acute cases in Guichi region. PartⅡStudy on the spatial pattern of schistosomiasis casesObjective To explore the spatial distribution status for acute schistosomiasis cases,and establish the corresponding techniques of quantitative analysis.Methods The theories of G,F, J and K functions were introduced on the basis of inter-case spatial distances,and they were used to analyze the data of acute schistosomiasis cases in Guichi region.In the course of analysis,the study distance was from 0 to 3000m and the interval was 50m.Results The computational methods for the four quantified indices were obtained and the concept of edge effect was simultaneously put forward.For the acute schistosomiasis cases,G and K functions were above,while F and J functions were below the simulated envelopes of spatial random distribution.They showed the same result that these cases were a clustered spatial pattern. Conclusion The spatial pattern of acute cases in Guichi region was clustered based on the assumption that the distribution of background population at risk was homogeneous.PartⅢSpatial cluster analysis on the schistosomiasis casesObjective Spatial cluster analysis was discussed from different aspects with the heterogeneous distribution of background population at risk adjusted.Methods The controls with the same sample sizes as cases were obtained by the sampling method of probability proportionate to size with the village population as the weights to represent the heterogeneous distribution of background population at risk.Cuzick-Edwards test,the methods based on first- and second- order attributes of point process,and spatial scan statistic approach through moving window were used to study the spatial clustering of acute schistosomiasis from different point of view and their results were compared with each other for a validation. Results Cuzick-Edwards test showed that the cases had a significant global clustering (p＜0.01).The result from the approaches based on the second-order attribute of point process was that the acute cases showed a significant clustering when the study scale is less than 13000m(p＜0.05),and the degree of clustering increased first and then decreased.For the results of the methods based on the first-order attribute of point process and spatial scan statistic approach,they detected the two coincident clusters.The most likely cluster was situated where the Qiupu River feeds into the Yangtze River.Its center was(117.43°longitude, 30.67°latitude) and its radius was 5.63km(LLR=19.56,p=0.001).The secondary likely cluster was located in the southeastern part of Guichi region.Its center was(117.71°longitude, 30.36°latitude) and its radius was 9.74 km(LLR=7.25,p=0.07).Conclusion The acute schistosomiasis cases in Guichi region had a significant spatial clustering when adjusting the heterogeneous distribution of background population at risk and two significant high risk clusters were detected.PartⅣIdentifying high risk regions of schistosomiasisSection one Study on the impact factors of schistosomiasis and Oncomelania hupensis1.Large-scale impacts of air temperature Objective To study the impacts of air temperature on the O.hupensis at a large scale,and explore the sensitive temperature index. Methods Weather stations in the neighboring provinces where snails exist and do not exist were selected as study objects with north latitude 340 as the boundary of snail distribution. The stations in the south and north of the boundary were coded as regions that the air temperature was and was not suitable for snails to live,respectively.The differences of yearly extreme low temperature and mean temperature in a year between snail areas and no snail areas were tested using t test to show their significance on the impacts of snail distribution, respectively.And unconditional logistic regression analysis was used to determine the sensitive index of air temperature to predict the snail distribution.Then the histogram for the sensitive temperature index was generated and the suspicious temperature range for snails to live was calculated through the overlapped parts between the histograms.Results The weather stations for snail areas and no snail areas were 37 and 24,respectively.The yearly extreme low temperature and mean temperature in a year were significantly different between snail areas and no snail areas(t=-6.49,p＜0.01;t=-3.93,p＜0.01),and they were both lower in the no snail areas and their differences were 6.72℃and 3.02℃,respectively.Logistic regression analysis showed that only the annual extreme low temperature was significant for indicating the snail distribution(χ~2=15.69,p＜0.01).The temperature range for the suspicious snail areas was(-7.6℃,1.5℃).Conclusion Annual extreme low temperature was the sensitive temperature index for the living of O.hupensis at a large scale,and the snails were not suitable to live in the places where the temperature was lower than -7.6℃.2.Small-scale impacts of vegetation Objective To study the impacts of vegetation changes on the distribution of O.hupensis in the lake and marshland regions.Methods A bottomland along the Qiupu River in Guichi region was randomly selected as the study field.Low-grass group,boundary-grass group,hay group and control group were designed to represent four different types of vegetation’s status.The snail density,soil temperature and soil moisture were surveyed half a month after the study began.The movement of snails due to vegetation changes was analyzed and inferred.Results The snail densities of the hay group,low-grass group,control group,boundary-grass group were 0.13/0.11m~2,32.1/0.11m~2,49.07/0.11m~2,and 53.6/0.11m~2,respectively.There was no significant difference for the snail density between the boundary-grass group and control group(p＞0.05),but the differences were significant among the other groups(p＜0.05).The environments of high soil moisture and low soil temperature in the hay group and low soil moisture and high soil temperature in the low-grass group were both unfavorable for the living of O.hupensis.Conclusion Vegetation was an important factor for the living of O.hupensis,and it would lead the snails to move from low-grass group to boundary-grass group if the vegetation was cut down.3.Study on the factors of affecting snail density at a small scale Objective To study the important factors of affecting snail density at a small scale.Methods Study area was randomly selected from the bottomlands along Qiupu River in Guichi region.The sampling method was stratified random sampling according to the types of vegetation;the frame size of snail survey was 0.11m~2 and snails were collected by crosscheck-random sampling inspection survey.Elevation,soil and air temperature,height of vegetation,soil humidity and types of vegetation were simultaneously collected,and the data were collected in April and September, 2006,respectively.Generalized linear models were used to develop the prediction model for snail density,and the statistics of deviance and AIC were used to determine the best model structure.Model diagnostics and model evaluation of efficiency were performed on the chosen best model.Then the impacts of different factors at a small scale were discussed following the results of the fitting model.Results The sample size was 162.There were 6 explanatory variables,including 2 categorical variables and 4 quantitative variables. Complicated relationships existed between variables,snail data was positively related with the height of vegetation(r=0.36),while negatively related with soil humidity(r=-0.22);air temperature had a strong positive relationship with soil temperature(r=0.59);soil temperature was negatively related with height of vegetation(r=-0.36);soil humidity had negative relationships with both soil and air temperature(r=-0.34 and -0.12).The best model structure was determined with gamma distribution as error distribution,inverse as link function,and mean^2 as variance function.The results showed that different vegetation had different impacts on the snail density.Height of vegetation(t=-2.371,p=0.01897) and low elevation (t=-3.202,p=0.00166) were positive impacts,while soil humidity(t=3.124,p=0.00214) was negative impacts for snail density.Soil temperature showed an ambiguous effect(t=-1.989, p=0.04849),while air temperature was not significant for predicting snail density.Conclusion Soil temperature,vegetation,elevation and soil humidity were important factors for snails at a small scale and generalized linear models were promising to establish a prediction model of snail density.Section two Detecting high-risk regions for schistosomiasis using GAMsObjective To identify high-risk regions for schistosomiasis with the impacts of covariates adjusted.Methods The environmental variables(NDVI and LST) from remote sensing images,topographical variables(elevation and slope) from digital elevation model,nearest distance from cases and controls to the rivers,and spatial positions(X/Y) were used as independent variables to predict the disease risk for schistosomiasis from the spatial point of view using generalized additive models(GAMs).The goodness of model fitting and the prediction ability were evaluated by the partial residual plots and the area under the receiver operating characteristic curve(AUC),respectively,and piecewise ’p value surface’ approach was adopted to conduct the statistical test in space.And the results were also compared with that of PartⅢto explore their differences and reasons.Results The final fitting model had a high prediction ability(AUC=0.911).Distance(χ~2=19.6879,p=0.0002),spatial positions X (χ~2=11.7625,p=0.3809),Y(χ~2=26.3038,p=0.0009) and elevation(χ~2=13.4844,p=0.0037) were significant at a small scale,and they were piecewise linear,quadratic curve,linear and piecewise linear relationships for predicting the disease risk of schistosomiasis.The disease risk began to decrease when the elevation was higher than 86m in the local area.The disease risk was constant and high when the distance was less than 1500m apart from the high risk rivers,and the risk became very low for the places where distance was larger than 18000m. The important factors for schistosomiasis risk at a small scale were social,geographical and environmental factors in sequence.Four significant high risk regions for schistosomiasis were detected.Two true high risk clusters were in agreement with the results of PartⅢ,but here they were more precise.The other two new clusters had similar environments as the two true high risk clusters and were regarded as potential high risk clusters of schistosomiasis. Conclusion There were two true and two potential high risk clusters for schistosomiasis in Guichi region.PartⅤRecognition of high risk snail habitatsObjective To locate the high risk snail habitats in Guichi region.Methods Two remote sensing images were obtained,one representing the "wet season" and the other denoting the "dry season" in Guichi region.The two indices of NDWI and NDVI were jointly applied to extract snail habitats.The accuracy of this method was evaluated through field survey.Then, the results from the previous PartⅣwere overlaid with extracted snail habitats to locate the high risk snail habitats.The detailed information for these high risk habitats were obtained by geographic information system(GIS) and field investigation was conducted to validate these analysis results following the navigation of global positioning system(GPS).Results The sensitivity and specificity of the above method to extract snail habitats were 90%and 100%, respectively.There were 349 places of snail habitats in Guichi region,and the total area was about 107km~2.Six high risk snail habitats were finally located.Conclusion Centered on the six detected high risk snail habitats,their surrounding regions of 1500m were the emphasis of schistosomiasis control in Guichi region.PartⅥStudy on the statistical distribution of Oncomelania hupensisSection one Study on the survey method to collect snails in the fieldObjective To find a better method for collecting snails in the field.Methods A bottomland was randomly selected as the study field along the Qiupu River in Guichi region.The conventional survey,individually repeated survey,crossed-repeated survey and crosscheck-random sampling inspection survey were designed to collect snails in the same area.Snail density and omission rate were used to evaluate the data quality of different survey methods.Results There were no statistical significant differences for snail density among the following three survey methods,individually repeated survey,crossed-repeated survey and crosscheck-random sampling inspection survey(χ~2=3.873,p=0.144),which were significantly different from that of conventional survey(U=309,p＜0.01).Omission rates for individually repeated survey,crossed-repeated survey and crosscheck-random sampling inspection survey were significantly different(χ~2=37.44,p＜0.01) and they were 10.26%,5.24%and 0.57%, respectively.Conclusion Crosscheck-random sampling inspection survey was the best method in the field to collect snails accurately.Section two Study on the statistical distribution of Oncomelania hupensisObjective To study the statistical distribution of O.hupensis.Methods 4 bottomlands were randomly selected as the study fields along the Qiupu River in Guichi region to collect snails in October,2005,and 2 bottomlands from the 4 selected bottomlands were randomly chosen again to survey snails in April,2006.Maximum likelihood estimation was used to fit generalized negative binomial distribution(GNBD) and negative binomial distribution(NBD). Results from different bottomlands in the same season and the same bottomlands in different seasons were compared with each other to discuss the statistical distribution of O.hupensis. Results Snail density in different seasons and different bottomlands were different,but they had the similar positive skewness distribution.GNBD was successfully fitted to all the snail data,but NBD was not.And the goodness of fitting for GNBD was better than NBD for all the collected snail data.The fitted parameters of GNBD from the same bottomlands in different seasons changed in different direction,and the parameters of GNBD from different bottomlands in the same season were not similar even if their snail density were close.Tiny changes in different snail habitats could be sensitively reflected by the parameters of GNBD. Conclusion GNBD could reflect the complexity of snail distribution than NBD very well, which was more promising for the statistical distribution of O.hupensis.Section three Relative impacts of biological behavior and environmental factors on the snail distributionObjective To study the relative impacts of biological behavior and environmental factors on the snail distribution for correctly understanding its distribution.Methods A bottomland was randomly selected along the Qiupu River in Guichi region,and the survey area of 200cm×200cm was also randomly determined.All the snails were collected,marked and measured with the corresponding Cartesian coordinates.G and F functions were first applied to conduct the exploratory data analysis on the distribution pattem of snails.Then,the transformed reduced second moment function,L-function,was used to do the multi-scale analysis and the relative impacts of biological behavior and environmental factors on the snail distribution were tried to be separated according to the results from the small and large scales,respectively. Finally,a point process model was fitted to the snail data and Monte Carlo simulation was used to test the goodness of fitting.Results An area of 122cm*172cm was surveyed finally and 528 live snails were found.The distribution pattern caused by biological behavior was random in the survey area.It was the environmental factors that led it to be an aggregated distribution and its impacts were 5.96 times as that of biological behavior.Conclusion Multi-scale analysis could be used to separate the relative impacts of biological behavior and environmental factors on the snail distribution,and the latter had larger impacts.更多还原