Dynamic Network Sampling

提出的研究开发了新的抽样策略，称为动态网络抽样，需要在重要的情况下，传统的抽样和干预策略不能很好地服务。这种情况的例子包括：在难以接触到的人口中采取干预措施，以减轻健康和社会问题的蔓延，控制流行病在动物、植物和人口中的传播，对可再生资源进行适应性管理，设计移动传感器以检测空气传播的病原体，以及虫害管理。提出的抽样设计扩展了研究者早期的设计，包括自适应抽样，自适应簇抽样，目标随机行走网络设计和自适应网抽样，以适应人口和抽样过程完全动态的情况。动态网络样本通过自适应地跟踪网络中的链接和利用空间环境中的距离关系，在不断变化的兴趣群体中传播自己。链路跟踪的选择概率自适应地取决于样本和一步节点的值、链路特征、当前样本大小和目标样本总数以及其他变量。所提出的抽样策略可用于在难以到达的种群中引入干预措施和实验，并用于对种群特征进行推断。初步研究表明，就每一种用途而言，拟议的抽样策略可能比传统策略有效得多。

The proposed research develops new sampling strategies, termed dynamic network sampling, that are needed for important situations that are not well served by conventional sampling and intervention strategies. Examples of such situations include interventions in hard-to-reach human populations to alleviate the spread of health and social problems, controlling the spread of epidemics in animal, plant, and human populations, adaptive management of renewable resources, and moving sensor designs for detection of airborne pathogen releases, and pest management. The proposed sampling designs extend earlier designs of the investigator, including adaptive sampling, adaptive cluster sampling, targeted random walk network designs and adaptive web sampling, to the cases in which the population and the sampling procedure are fully dynamic. A dynamic network sample spreads itself through the changing population of interest by adaptively following links in networks and by using distance relationships in spatial environments. The selection probabilities for link tracing depend adaptively on values of sample and one-step-away nodes, link characteristics, current sample size, and target sample totals, and other variables. The proposed sampling strategies can be used for for introducing interventions and experiments into hard-to-reach populations and for making inferences about population characteristics. Preliminary studies indicate the proposed sampling strategies are likely to be substantially more effective than conventional strategies for each of those uses.