Department of Environmental Health, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, USA.
J R Soc Interface. 2010 Aug 6;7(49):1181-93. doi: 10.1098/rsif.2009.0523. Epub 2010 Feb 17.
The sustained transmission and spread of environmentally mediated infectious diseases is governed in part by the dispersal of parasites, disease vectors and intermediate hosts between sites of transmission. Functional geospatial models can be used to quantify and predict the degree to which environmental features facilitate or limit connectivity between target populations, yet typical models are limited in their geographical and analytical approach, providing simplistic, global measures of connectivity and lacking methods to assess the epidemiological implications of fine-scale heterogeneous landscapes. Here, functional spatial models are applied to problems of surveillance and control of the parasitic blood fluke Schistosoma japonicum and its intermediate snail host Oncomelania haupensis in western China. We advance functional connectivity methods by providing an analytical framework to (i) identify nodes of transmission where the degree of connectedness to other villages, and thus the potential for disease spread, is higher than is estimated using Euclidean distance alone and (ii) (re)organize transmission sites into disease surveillance units based on second-order relationships among nodes using non-Euclidean distance measures, termed effective geographical distance (EGD). Functional environmental models are parametrized using ecological information on the target organisms, and pair-wise distributions of inter-node EGD are estimated. A Monte Carlo rank product analysis is presented to identify nearby nodes under alternative distance models. Nodes are then iteratively embedded into EGD space and clustered using a k-means algorithm to group villages into ecologically meaningful surveillance groups. A consensus clustering approach is taken to derive the most stable cluster structure. The results indicate that novel relationships between nodes are revealed when non-Euclidean, ecologically determined distance measures are used to quantify connectivity in heterogeneous landscapes. These connections are not evident when analysing nodes in Euclidean space, and thus surveillance and control activities planned using Euclidean distance measures may be suboptimal. The methods developed here provide a quantitative framework for assessing the effectiveness of ecologically grounded surveillance systems and of control and prevention strategies for environmentally mediated diseases.
寄生虫、疾病媒介和中间宿主在传播地点之间的扩散，部分决定了环境介导的传染病的持续传播和扩散。功能地理空间模型可用于量化和预测环境特征促进或限制目标种群之间连通性的程度，但典型模型在其地理和分析方法上存在局限性，提供了连通性的简单、全局度量，并且缺乏评估细粒度异质景观的流行病学影响的方法。在这里，功能空间模型被应用于中国西部寄生血吸虫日本血吸虫及其中间宿主钉螺的监测和控制问题。我们通过提供分析框架来推进功能连通性方法，该框架用于：(i) 识别传播节点，其中与其他村庄的连通程度（因此疾病传播的潜力）高于仅使用欧几里得距离估计的程度；(ii) (重新)根据节点之间的二阶关系，使用非欧几里得距离度量（称为有效地理距离（EGD），将传播地点组织为疾病监测单元。功能环境模型使用目标生物的生态信息进行参数化，并估计节点之间的 EGD 分布。提出了一种蒙特卡罗秩乘积分析来识别替代距离模型下的邻近节点。然后，节点被迭代地嵌入到 EGD 空间中，并使用 k-均值算法进行聚类，将村庄分组为具有生态意义的监测组。采用共识聚类方法得出最稳定的聚类结构。结果表明，当使用非欧几里得、生态确定的距离度量来量化异质景观中的连通性时，会揭示节点之间的新关系。当在欧几里得空间中分析节点时，这些连接并不明显，因此使用欧几里得距离度量规划的监测和控制活动可能不是最佳的。这里开发的方法为评估基于生态的监测系统以及环境介导疾病的控制和预防策略的有效性提供了定量框架。
