Dynamic Network Sampling

拟议的研究开发了新的采样策略，称为动态网络采样，这是需要的重要情况下，不能很好地服务于传统的采样和干预策略。这种情况的例子包括对难以接触到的人群进行干预，以缓解健康和社会问题的蔓延，控制动物、植物和人群中流行病的传播，可再生资源的适应性管理，以及用于检测空气传播的病原体释放的移动传感器设计，以及害虫管理。建议的抽样设计扩展了早期的设计调查，包括自适应抽样，自适应整群抽样，有针对性的随机游走网络设计和自适应网络抽样，在人口和抽样程序是完全动态的情况下。动态网络样本通过自适应地跟随网络中的链接并通过使用空间环境中的距离关系，在不断变化的感兴趣群体中传播自己。链路跟踪的选择概率自适应地依赖于样本和一步之遥节点的值、链路特征、当前样本大小和目标样本总数以及其他变量。建议的抽样策略可用于将干预措施和实验引入难以接触的人群，并对人群特征进行推断。初步研究表明，拟议的抽样战略可能比上述每一种用途的常规战略有效得多。

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.