Calibration is a technique for the adjustment of the original design weight to improve the precision of the survey. There is a dearth of information on calibration approach adapted for survey such that the survey cost is put into consideration.  This research work developed a modified calibration approach for improving survey precision by considering the cost function. Data set on vegetable and tobacco productions (metric tonnes) were considered for this study. The data were obtained from the website of Food and Agriculture Organization. Data used was stratified based on geographical location. The population under study was divided into subpopulation of units, these subpopulations were non-overlapping homogenous sub- group. Observations were drawn within each stratum by simple random sampling with optimum allocation procedure. The proposed estimator was derived and used to determine the linear weight estimator of population parameters. The statistical properties of the derived estimator was examined. Using Lagrange multiplier, Mean Square Error and Relative Efficiency was obtained. The proposed estimator is found to be efficient

The health benefits in the description and observation of quantitative contents of quality parameters present or contained in any water source cannot be underestimated as they determine selection of best choice from available water sources for different intended uses as well as resource consumption. It also helps to compare the observed quantity of the quality with the acceptable standards or limits to get desired results. Physical parameters like pH, temperature, electrical conductivity (EC) and total dissolved solids (TDS) among others are determined by present of other chemical properties like Cations (Mg2+, Ca2+, Na+, etc), Anions (Cl-, NO3-, SO42+, etc), heavy metals and other dissolved materials during the course of its formation in different proportions and amounts. This study observed EC and TDS of 20 selected boreholes as two close and correlated water quality parameters as well as two of the major water quality parameters that account for overall quality of any water source, despite their different quantitative contents and physical features, they are likely determined by the same set of cations and anions with similar constraint equations. In contrast to linear programming, multiple criteria optimization models were fitted for EC and TDS using Response Surface Methodology via desirability techniques, optimal values obtained in this case measured against several criteria are found to lie between acceptable standards limits for drinking water, other numerical values and descriptive features in the final results reflect that the response equations obtained were well fitted.

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

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Rare events population (φ) is hard-to-reach, sparsely distributed and clus-tered; an Adaptive Cluster Sampling (ACS) is the design to collect information from φ. Researchers and Policy Makers have modelled φ in ACS design with homogeneity assumptions. This study modelled φ with heterogeneity among networks and within the network units. Data from the International Institute of Tropical Agriculture on Culcasia Scandens, an understory plant and simulation were used to validate the model. Estimators for total and average number of rare events were derived and their statistical properties were examined. Bayesian Model was embedded in the designed ACS to develop the model for predicting the total number of rare events. Parameters α, β and λ were used in the model to control the expected number of grid cells with rare events, the conditional expected number of sub-network and expected number of rare events in each sub-network respectively. Markov Chain Monte-Carlo Algorithm with R and Winbugs software were used to estimated these parameters. The robustness of the model was examined and its Sensitivity Analysis was