No abstract

The COVID-19 pandemic needs immediate solution before inflicting more devastation. So far, China has successfully controlled transmission of COVID-19 through implementing stringent preventive measures. In this study, we analyze the effectiveness of preventive measures taken in thirteen regions of China based on the feedback provided by 1135 international students studying in China. The study uses factor analysis combined with varimax rotation of variables. It was found that awareness raising and dispersing actionable knowledge regarding trust and adapting measures remained significantly important. Therefore, recognition of information gaps, improvements in the level of alertness, and development of preventive measures in each sector are imperative. The findings of this study revealed that trust, students' health, waste disposal, and the efforts of the Chinese government/ international institute of education to prevent this pandemic were significantly and positively associated with preventive measures. The results showed that prior knowledge, global pandemics, and food and grocery purchases were firmly related to the preventive measures of COVID-19. Moreover, anxiety, transportation, and economic status were negatively related to the preventive measures. During this epidemic situation, international students suffered various types of mental stresses and anxiety, especially living in most affected regions of China. The study adopted a mixed (qualitative and quantitative) approach where the findings can act as a set of guidelines for governmental authorities in formulating, assisting in the preparation, instructing, and guiding policies to prevent and control the epidemic COVID-19 at national, local, and divisional levels.

Malaria is second most life threatening disease in the world. It shows highest morbidity rate among serious illness including tuberculosis etc. Pakistan is at high risk of this disease giving very rise high frequency of Malaria victims in rural areas of Federally Administered Tribal Areas (FATA) of Pakistan. Due to its severe epidemics in specific regions they are termed as malarious areas of a country. Objectives: Main purpose of the study was to find out prevalence (p-value) of malaria in local community of FR Bannu region visited to basic health clinics. Study Design: In current survey based epidemiological descriptive study, we analyzed valuable data of malaria epidemic and its prevalence in selected areas of FATA (FR Bannu) region in Khyber Pakhtunkhwa via questionnaire & personal interaction. Study Period: The study was conducted in the months of June 2014 to August 2014. Material & Methods: Followed questionnaire against gender, age, seasonal, area & specie wise protocol survey. Results: Results showed (P > 0.05) ranging variables including high number of plasmodium vivax strain over plasmodium falciparum, gender comparison was dominant by male against females, age wise effect of pathogenic strain upon infants and old aged peoples, seasonal occurrence and its prevalence was less in cold months and in start of summer season where the data of rural areas was at its peak risk. Conclusion: It is concluded that pregnant ladies and infants are at high risk in such areas, so more care and control programs for malaria eradication are needed in selected areas of Pakistan.

Control charts (CCs) are one of the main tools in Statistical Process Control that have been widely adopted in manufacturing sectors as an effective strategy for malfunction detection throughout the previous decades. Measurement errors (M.E's) are involved in the quality characteristic of interest, which can effect the CC's performance. The authors explored the impact of a linear model with additive covariate M.E on the multivariate cumulative sum (CUSUM) CC for a specific kind of data known as compositional data (CoDa). The average run length (ARL) is used to assess the performance of the proposed chart. The results indicate that M.E's significantly affects the multivariate CUSUM-CoDa CCs. The authors have used the Markov chain method to study the impact of different involved parameters using six different cases for the variance-covariance matrix (VCM) (i.e., uncorrelated with equal variances, uncorrelated with unequal variances, positively correlated with equal variances, positively correlated with unequal variances, negatively correlated with equal variances and negatively correlated with unequal variances). The authors concluded that the error VCM has a negative impact on the performance of the multivariate CUSUM-CoDa CC, as the ARL increases with an increase in the value of the error VCM. The subgroup size m and powering operator b positively impact the proposed CC, as the ARL decreases with an increase in m or b. The number of variables p also has a negative impact on the performance of the proposed CC, as the values of ARL increase with an increase in p. For the implementation of the proposal, two illustrated examples have been reported for multivariate CUSUM-CoDa CCs in the presence of M.E's. One deals with the manufacturing process of uncoated aspirin tablets, and the other is based on monitoring the machines involved in the muesli manufacturing process.

This article uses the classic multivariate cumulative sum (MCUSUM$\mathrm{MCUSUM}$) chart scheme proposed by Crossier (1988) to present a new modified MCUSUM$\mathrm{MCUSUM}$ chart for compositional data (CoDa$\mathrm{CoDa}$). For this purpose, the data are first transformed using isometric log‐ratio (ilr$\operatorname{ilr}$) coordinates representation to eliminate the constant sum constraint of CoDa$\mathrm{CoDa}$. The MCUSUM$\mathrm{MCUSUM}$‐CoDa$\mathrm{CoDa}$ control chart has been defined along with the performance measures of the proposed chart using the average run length (ARL$\mathrm{ARL}$). Besides, the Markov chain method has been used to study the ARL$\mathrm{ARL}$ performance of the proposed chart. Assuming that the ilr$\operatorname{ilr}$ transformed data are normally distributed, the proposed MCUSUM$\mathrm{MCUSUM}$‐CoDa$\mathrm{CoDa}$ charts have been compared with existing competitors such as T2$T^2$‐CoDa$\mathrm{CoDa}$ and MEWMA$\mathrm{MEWMA}$‐CoDa$\mathrm{CoDa}$ charts. The comparison shows that the proposed chart has better performance than the T2$T^2$‐CoDa$\mathrm{CoDa}$ control charts, while the performance of the proposed chart is comparable with the MEWMA$\mathrm{MEWMA}$‐CoDa$\mathrm{CoDa}$ chart. The effect of the estimated mean vector and variance‐co‐variance matrix on run‐length characteristics of the proposed MCUSUM$\mathrm{MCUSUM}$‐CoDa$\mathrm{CoDa}$ control chart has also been studied in this paper. For the ARL$\mathrm{ARL}$ performance of MCUSUM$\mathrm{MCUSUM}$‐CoDa$\mathrm{CoDa}$ with estimated parameters Monte Carlo simulation has been adopted. The effect of the number of variables p$p$, sample size n$n$, and subgroup size m$m$ has also been studied on the data's upper control limit (UCL$\mathrm{UCL}$) and ARL$\mathrm{ARL}$. In the end, two illustrative examples of the particle size distribution of plants and production of muesli are provided to represent the practical implementation of the MCUSUM$\mathrm{MCUSUM}$‐CoDa$\mathrm{CoDa}$ chart.