Abstract
COVID-19 a novel corona virus originated from Wuhan China. It turned into a pandemic resulting in a large number of deaths and loss of livelihood. It is vital to determine the manner in which the number of cases propagates so that future pandemics can be tackled scientifically. However the pandemic can be controlled systematically using efficient health care systems. It is difficult to predict the pandemic propagation over a large period of time due to various factors. In this paper an analysis is made for short periods using statistical tools like predicting the probability curve, probability density function. Forecasting of Covid-19 cases is done using time series trend analysis and ARIMA models. The test of hypothesis for difference of means and standard deviations of the actual and forecasted values with 99% CI showed no significant difference between them.
Highlights
The pandemic of COVID-19 originated in Wuhan, China and has caused a heavy loss in lives, lockdowns and loss of livelihood etc
A probability distribution is fitted to the data, which is a best fit based on Kolmogorov Smirnov ranking test
Time series trend analysis is used to find the parameters of various models like Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) and Mean Squared Deviation (MSD) values
Summary
The pandemic of COVID-19 originated in Wuhan, China and has caused a heavy loss in lives, lockdowns and loss of livelihood etc. Data sets are available for this pandemic in the official website of Johns Hopkins University. Data set for India is considered for statistical analysis for this pandemic to predict the propagation of the disease and control the same scientifically. This must be modeled scientifically to assist policy makers and healthcare community to be prepared for future consequences to help control the problem
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