Background At the end of December 2019, the world in general and Wuhan, the industrial hub of China, in particular, experienced the COVID-19 pandemic. Massive increment of cases and deaths occurred in China and 209 countries in Europe, America, Australia, Asia, and Pakistan. Pakistan was first hit by COVID-19 when a case was reported in Karachi on 26 February 2020. Several methods were presented to model the death rate due to the COVID-19 pandemic and to forecast the pinnacle of reported deaths. Still, these methods were not used in identifying the first day when Pakistan enters or exits the early exponential growth phase. Methods New approaches are needed that display the death patterns and signal an alarming situation so that corrective actions can be taken before the condition worsens. To meet this purpose, secondary data on daily reported deaths due to the COVID-19 pandemic have been considered, and the c and exponentially weighted moving average (EWMA) control charts are used to monitor variations in deaths and to identify the growth phases such as pre-growth, growth, and post-growth phases in Pakistan due to the COVID-19 pandemic. Results The-chart shows that Pakistan switches from the pre-growth to the growth phase on 31 March 2020. The EWMA chart demonstrates that Pakistan remains in the growth-phase from 31 March 2020 to 17 August 2020, with some indications signaling a decrease in deaths. It is found that Pakistan moved to a post-growth phase for a brief period from 27 July 2020 to 28 July 2020. The country encounters a re-growth phase right after this short-term post-growth phase, but the number of deaths is decreasing in that Pakistan may approach the post-growth phase shortly. Conclusion This novel amalgamation of control charts illustrates a systematic implementation of the charts to government leaders and forefront medical teams to facilitate the rapid detection of daily reported deaths due to COVID-19. Besides government and public health officials, it is also the public’s responsibility to follow the enforced standard operating procedures as a temporary remedy of this pandemic in ensuring public safety while awaiting a suitable vaccine to be discovered.

Extreme behavior (Performance) of students is inclined by number of factor which must be painted for important policy implications. This study states that the CGPA is the most important system to deduct student performance. Data on CGPA has been collected from B.A/B.Sc (Hons.) of 32 private and public universities of Lahore. Generally, researchers investigate an average performance of the students with classical methods of simple linear regression. This approach does not give complete picture of different variables influencing student performance from corner to corner. Quantile regression introduces information across the whole distribution of the student’s achievements. Study furnishes that students performance strongly affected by father’s education. Student’s gender, passion for fashion, and mother’s job are significant factors. Class participation is found as a magical variable that has positive impact on student performance at all quantiles. The quantile estimate of student performance shows that effect of the urban-rural difference is significant factor. The study clearly shows for high performance students, factors like mother occupation, father education, gender and area become insignificant at high quantiles. The results highlight that quantile regression model is a useful technique for examine information than ordinary least squares. It also depicts that ordinary least squares underestimated and overestimated the Quantile regression at different quantiles.