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
This study was carried out in Ijebu North East Local Government Area of Ogun State, Nigeria, to determine the effects of climate change on health of rural households. One hundred and twenty respondents were selected using multistage and random sampling techniques. Primary and secondary data were collected. The data collected were subjected to descriptive and inferential analyses. The results showed that 67% of respondents were males and the majority (54%) were above 40 years with a mean age of 43. The results also revealed that 60% of the respondents have been residing in the area for more than 6 years, which implied that a greater percentage of them witnessed the changes in the climatic pattern of the area. The major health problems reported included common cold, cough, malaria, and fever due to climate change. Many of the respondents patronized medicine hawkers (78%) and local chemists (46%) to treat the problematic changes in their health. Data analysis revealed that climatic change has significant effects on respondents' health status (p < 0.05). The study concluded that there have been changes in the climatic pattern in the area and the health status of the people were affected. The study recommended that health, environment, and rural development agencies should coordinate efforts to assist rural households on preventing and mitigating the effects of climate change. Sustainable use of all resources, sustainable development, preventive health methods such as clean environment and proper hygiene, and reduction in activities contributing to the increase in climate change (deforestation, bush burning, environmental, and e-pollution) were advocated.
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
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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