Time Series Analysis Using Different Forecast Methods and Case Fatality Rate for Covid-19 Pandemic

Abstract

This study presents forecasting methods using time series analysis for confirmed cases, the number of deaths and recovery cases, and individual vaccination status in different states of India. It aims to forecast the confirmed cases and mortality rate and develop an artificial intelligence method and different statistical methodologies that can help predict the future of Covid-19 cases. Various forecasting methods in time series analysis such as ARIMA, Holt's trend, naive, simple exponential smoothing, TBATS, and MAPE are extended for the study. It also involved the case fatality rate for the number of deaths and confirmed cases for respective states in India. This study includes the forecast values for the number of positive cases, cured patients, mortality rate, and case fatality rate for Covid-19 cases. Among all forecast methods involved in this study, the naive and simple exponential smoothing method shows an increased number of positive instances and cured patients.

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DOI

https://doi.org/10.1111/rsp3.12555

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https://discovery.dundee.ac.uk/en/publications/256264dd-1737-4ade-a9fc-3178b8a3974b