In this paper, we have conducted analysis based on data obtained from National Institute of Health (NIH) -Islamabad and produced a forecast of COVID-19 confirmed cases as well as the number of deaths and recoveries in Pakistan using the Auto-Regressive Integrated Moving Average Model (ARIMA). The fitted forecasting models revealed high exponential growth in the number of confirmed cases, deaths and recoveries in Pakistan. Based on our model prediction the number of confirmed cases will be increased by 2.7 times, 95% prediction interval for the number of cases at the end of May 2020 = (5681 to 33079). There could be up to 500 deaths, 95% prediction interval = (168 to 885) and there could be eightfold increase in the number of recoveries, 95% prediction interval = (2391 to 16126). The forecasting results of COVID-19 are alarming for May in Pakistan. The health officials and government should adopt new strategies to control the pandemic from further spread until a proper treatment or vaccine is developed.

In the article, the authors present several inequalities of the Čebyšev type for conformable k-fractional integral operators.

The Subdivision Schemes (SSs) have been the heart of Computer Aided Geometric Design (CAGD) almost from its origin, and various analyses of SSs have been conducted. SSs are commonly used in CAGD and several methods have been invented to design curves/surfaces produced by SSs to applied geometry. In this article, we consider an algorithm that generates the 5-point approximating subdivision scheme with varying arity. By applying the algorithm, we further discuss several properties: continuity, Hölder regularity, limit stencils, error bound, and shape of limit curves. The efficiency of the scheme is also depicted with assuming different values of shape parameter along with its application.

No abstract

The present outbreak of COVID-19 is a worldwide calamity for healthcare infrastructures. On a daily basis, a fresh batch of perplexing datasets on the numbers of positive and negative cases, individuals admitted to hospitals, mortality, hospital beds occupied, ventilation shortages, and so on is published. Infections have risen sharply in recent weeks, corresponding with the discovery of a new variant from South Africa (B.1.1.529 also known as Omicron). The early detection of dangerous situations and forecasting techniques is important to prevent the spread of disease and restart economic activities quickly and safely. In this paper, we used weekly mobility data to analyze the current situation in countries worldwide. A methodology for the statistical analysis of the current situation as well as for forecasting future outbreaks is presented in this paper in terms of deaths caused by COVID-19. Our method is evaluated with a multi-layer perceptron neural network (MLPNN), which is a deep learning model, to develop a predictive framework. Furthermore, the Case Fatality Ratio (CFR), Cronbach’s alpha, and other metrics were computed to analyze the performance of the forecasting. The MLPNN is shown to have the best outcomes in forecasting the statistics for infected patients and deaths in selected regions. This research also provides an in-depth analysis of the emerging COVID-19 variants, challenges, and issues that must be addressed in order to prevent future outbreaks.