Sampling and Intervention Designs

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The proposed research develops new methods for sampling and***intervention designs for hidden and hard-to-access populations. These***include ecological populations of animals and plants and human***populations that are hard to find by ordinary survey methods.***Populations of animals and fish, for example, can be highly mobile,***camouflaged, and spatially uneven in patterns that change over time.***Hard-to-access human populations include those that are hidden,***mobile, and stigmatized. Members of such populations may be reachable***only by following social network links from one member to another.******The proposed research develops new network, spatial, and temporal***sampling designs and interventions strategies for populations that***have spatial, temporal, or network structure. Network sampling is***necessary for surveys of hidden human populations, for understanding***organizational dynamics as well as for understanding online networks***and the behaviours of people who use those networks. Adaptive spatial***sampling designs are needed for unevenly distributed natural***populations. In many cases also, adaptive and network intervention***strategies are more effective than conventional strategies for***mitigating harms or bringing benefits to human populations,***organizations, and populations of plants and animals. The proposed***research continues a long-term effort of the investigator that has***resulted in effective new methods for all of these types of***situations.******In recent work I introduced simple new estimators for network sampling***of hidden and hard-to-access human populations. Empirical simulations***show the new estimators greatly improved upon the estimators currently***in widespread use. For instance, for estimating mean number of***partners in the hidden population, the new methods almost totally***eliminated the bias and reduced mean sqare error to between***one-twenty-sixt to one-eightieth that of the currently most widely***used estimator. The proposed research will extend the methods of the***new estimators and make high-performance software available under open***access.******The key technique underlying the new network sampling estimators is to***run a fast network sampling process on the sample network data. The***inclusion frequencies of the fast sampling process provide estimates***of the unequal inclusion probabilities for the real-world network***sampling design by which the sample was selected. These estimates of***the inclusion probabilities are then used in generalized unequal***probability estimators to estimate population quantities. Confidence***intervals are obtained by a simplified linearization method.******The proposed research will build on these new results to find new and***improved methods also for spatial temporal sampling and inference and***for making interventions to benefit hard-to-access, at-risk, and***under-served populations.*****