The virus of COVID-19 has affected humans physically, mentally, and economically all over the world. Each country's development level, resources, and immunization to have coping capacity against this covid19 differ country-wise. Thus, understanding socioeconomic vulnerability and coping capacity among countries under different health systems can be crucial. Contrasting most articles on COVID-19, this article focuses on evaluating and estimating the COVID-19 risk and lack of coping capacity in 190 countries. This present study suggests an exponential estimator using two auxiliary attributes. Theoretically, the mean square error expressions are derived and compared with some existing estimators. These findings were supported by using the real data of INFORM COVID-19 risk. K E Y W O R D Sauxiliary attributes, COVID-19 risk, exponential estimator, immunization coverage, lack of coping capacity, mean square error INTRODUCTIONThe coronavirus, or COVID-19, is agitating panic for thus several reasons. Therefore, nobody has the immunity to fight it. Second, it is highly contagious, which means it spreads fast. Third, the scientists do not seem to be entirely sure of how it behaves since they need borderline history.Hoseinpour Dehkordi et al. 1 presented a new procedure based on a regression approach to estimate the COVID-19 case fatality rate during the pandemic. They have also compared the case history of a few Asian and European countries. Hoang 2 presented the characteristics of COVID-19 case detection in two phases to overcome the pandemic using the data set from the Ministry of Health of Vietnam. At that time, the situation in Vietnam was more volatile than expected. Overton et al. 3 provided a toolkit to analyze the outbreak and access the interventions using the statistical and mathematical model-based toolkit. They applied three different COVID-19 pandemic datasets to test the toolkit and focused on parameter estimation under known biases in the datasets. Wong et al. 4 explained that not only do different countries have different resources for the health systemto make. The immunization is strong so that people can cope with the disease, but also have a diversity of economic, political, and social features that the government's effort was unable to cope with disasters like COVID-19. In an article by Silveira et al. 5 the measures of relevance to psychological vulnerability, resilience, and social cohesion were identified for Germany in a heterogeneous Berlin population ranging from 18 to 65 years of age.Lockett et al. 6 studied that people with higher levels of posttraumatic stress symptoms and COVID-19-related functional impairment would display greater anxiety and lower coping self-efficacy after pandemic reminders. From these studies, one can conclude that the COVID-19 pandemic devastated the people economically and physically and mentally in many cases.In survey sampling, we deal with different interdisciplinary research territories such as population studies, agriculture, health issues, engineering, etc. There...

This paper presents a statistical analysis control chart for nonconforming units in quality control. In many situations the Shewhart control charts for nonconforming units may not be suitable or cannot be used. For many processes, the assumptions of binomial distribution may deviate or may provide inadequate model. In this Study we propose a new control chart, P r chart, which is based on regression estimator of proportion based on single auxiliary variable. The performance of the compared with P and Q charts with probability to signal as a performance measure. It has been observed that the proposed chart is superior to the P and Q charts. This study will help quality practitioners to choose an efficient alternative to the classical P and Q charts for monitoring nonconforming units in industrial process.

In this article, a combined general family of estimators is proposed for estimating finite population mean of a sensitive variable in stratified random sampling with non-sensitive auxiliary variable based on randomized response technique. Under stratified random sampling without replacement scheme, the expression of bias and mean square error (MSE) up to the first-order approximations are derived. Theoretical and empirical results through a simulation study show that the proposed class of estimators is more efficient than the existing estimators, i.e., usual stratified random sample mean estimator, Sousa et al (2014) ratio and regression estimator of the sensitive variable in stratified sampling.

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