Coronavirus (COVID-19) epidemic affects public health infrastructure across the world. The outbreak is considered as third major Coronavirus epidemic after SARS (Severe Acute Respiratory Syndrome) in the year 2002-2003 and MERS (Middle East Respiratory Syndrome) in 2015 since past 2 decades. It has been observed that the nature of growth of coronavirus is exponential. It has been tough to control and analyze the situation with limited human resource and treatment process must be carried for the large number of patients within an appropriate time. So, it has become obligatory to work on an automated model, grounded on computing approach, for curative measure. This paper concludes a Time Series Forecasting model and analyze the COVID-19 epidemic occurrence to check whether these numbers are going to be increased or decreased in near future. Statistical pattern analysis and data visualization is performed with widely accepted time series approaches as Auto-Regressive Integrated Moving Average (ARIMA) and its constituents Moving Average (MA) and Auto Regressive (AR). Finally, time-dependent parameters can enlighten the trends of the outbreak COVID-19 in India.
Risk of type 1 diabetes at 3 years is high for initially multiple and single Ab+ IT and multiple Ab+ NT. Genetic predisposition, age, and male sex are significant risk factors for development of Ab+ in twins.
Lottery scheduling is one of the useful techniques for managing the process queue by the scheduler. The significant feature it has the random selection of jobs in a probability manner so that various existing probability models could be used to derive interesting results. One of possible applications incorporated herewith by using probability based sampling models to estimate total time required to process all the jobs in a ready queue. A new scheduling scheme is designed named as Group Lottery Scheduling (GLS) and using this the total possible ready queue processing time is predicted in multi-processor environment. There are two variants involved in GLS as Type-I allocation and Type-II allocation of jobs to the multi-processors whose variabilities are compared. A numerical example is incorporated to support the theoretical findings.
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