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

Atanu Bhattacharjee, Gajendra K. Vishwakarma (Lead / Corresponding author), Namrata Gajare, Neha Singh

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)506-519
Number of pages14
JournalRegional Science Policy and Practice
Volume15
Issue number3
Early online date28 May 2022
DOIs
Publication statusPublished - 27 Apr 2023

Keywords

  • case fatality rateARIMA
  • forecasting
  • Holt
  • MAPE
  • simple exponential smoothing
  • TBATS
  • time series analysis

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Development
  • Management, Monitoring, Policy and Law

Fingerprint

Dive into the research topics of 'Time Series Analysis Using Different Forecast Methods and Case Fatality Rate for Covid-19 Pandemic'. Together they form a unique fingerprint.

Cite this