Abstract
The novel coronavirus disease (COVID - 19) now has a large global impact. The relationship between variables such as confirmed number of positive cases and number of deceased people is investigated in this paper. The purpose of a time-varying correlational study is to determine how one variable influences the other. The most vulnerable states on the variables studied were identified using statistical process control methods. To discover the association shift between the variables, the Exponential Moving Average Method covariance was performed. Time series models such as Autoregressive Integrated Moving Average (ARIMA) and GARCH models are examined in an attempt to estimate on future proven cases models. The performance measures of Mean Absolute per Error and Akaike Information Criteria are used to compare the predicted time series models.
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