Adaptive two-stage inverse sampling design to estimate density, abundance, and occupancy of rare and clustered populations

Sampling rare and clustered populations is challenging because of the effort required to find rare units. Heuristically, a practitioner would prefer to discontinue sampling in areas where rare units of interest are apparently extremely sparse or absent. We take advantage of the characteristics of inverse sampling to adaptively inform practitioners when it is efficient to move on to sample new areas. We introduce Adaptive Two-stage Inverse Sampling (ATIS), which is designed to leave a selected area after observation of an a priori number of only non-rare units and to continue sampling in the area when rare units are observed. ATIS is efficient in many cases and yields more rare units than conventional sampling for a rare and clustered population. We derive unbiased estimators of population total and variance. We also introduce an easy-to-compute estimator, which is nearly as efficient as the unbiased estimator. A simulation study on a rare plant population of buttercups (Ranunculus) shows that ATIS even with the easy-to-compute estimator is more efficient than its conventional sampling counterparts and is more efficient than Two-stage Adaptive Cluster Sampling (TACS) for small and moderate final sample sizes. Additional simulations reveal that ATIS is efficient for binary data (e.g., presence or absence) whereas TACS is inefficient for binary data. The overall results indicate that ATIS is consistently efficient compared to conventional sampling and to adaptive cluster sampling in some important cases.

INTEGRATION OF BAYESIAN MODEL AND ADAPTIVE CLUSTERED SAMPLING INTO CONTACT TRACING TO CURB THE SPREAD OF COVID -19 CASES

Covid-19 is a communicable virus that causes serious illness (Severe acutepiratory syndrome (SARS)) and middle east respiratory syndrome (MARS)). İts outbreak started in Wuhan, China on December 8, 2019. Fever, cough, tiredness are its signs and symptoms and appear between two to fourteen days after exposure. The severity of COVID-19 can include complications; pneumonia, heart problems, acute kidney injuries. Covid-19 careers should be identified in order to curb the spread of the virus within a population. In this regards, contact tracing is the current technique in use to identify and track the Covid-19 carriers. The aim is to curb the spread of the virus within the population. In order to achieve this goal effectively, appropriate technique is required in the identification of Covid-19 carriers and Modeling. It is known that Covid-19 carriers are hidden, clustered and very difficult to identify in the population. At this point, the Adaptive Cluster Sampling, which is a specialized sampling for identification of hidden and clustered event and Bayesian Model, comes to the practice. Therefore, in this study, Adaptive Cluster Sampling which is capable of tracking hidden and clustered events and Bayesian Model are integrated in contact tracing, and the application on how this technique is used is included