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

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  • Atanu Bhattacharjee
  • Gajendra K. Vishwakarma
  • Namrata Gajare
  • Neha Singh

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. Este estudio presenta métodos de pronóstico que utilizan el análisis de series temporales para los casos confirmados, el número de muertes y casos recuperados, y el estado de vacunación individual en diferentes estados de la India. Su objetivo es pronosticar los casos confirmados y la tasa de mortalidad y desarrollar un método de inteligencia artificial y diferentes metodologías estadísticas que puedan ayudar a predecir el futuro de los casos de Covid‐19. Para el estudio se adaptaron varios métodos de pronóstico para el análisis de series temporales como ARIMA, la tendencia de Holt, el ingenuo, el suavizado exponencial simple, TBATS y MAPE. También se incluyó la tasa de fatalidades para el número de muertes y casos confirmados para los respectivos estados de la India. Este estudio incluye los valores de pronóstico para el número de casos positivos, los pacientes curados, la tasa de mortalidad y la tasa de fatalidades para los casos de Covid‐19. Entre todos los métodos de pronóstico utilizados en este estudio, el método ingenuo y el de suavización exponencial simple muestran un mayor número de casos positivos y de pacientes curados. 本研究は、インドの州における確定症例、死亡数及び回復例、および個人のワクチン接種状況に関する時系列分析を用いた予測方法を提示する。確定症例と死亡率を予測し、人工知能を用いた方法とCOVID‐19の症例の将来を予測するのに役立ついくつかの統計学的方法論を開発することを目指す。ARIMA、Holtのトレンド、単純法、単純指数平滑化法、TBATS、MAPEなどの時系列解析における各種予測法を拡張した。また、インドの各州の死亡者数と確定症例数の致死率も含んだ。本研究は、COVID‐19症例に対する、陽性症例数、治癒患者数、死亡率、および致死率に対する予測値を含む。この研究に含まれるすべての予測法の中で、単純法と単純指数平滑法は、陽性者数と治癒患者数の増加を予測した。

Suggested Citation

  • Atanu Bhattacharjee & Gajendra K. Vishwakarma & Namrata Gajare & Neha Singh, 2023. "Time Series Analysis Using Different Forecast Methods and Case Fatality Rate for Covid‐19 Pandemic," Regional Science Policy & Practice, Wiley Blackwell, vol. 15(3), pages 506-519, April.
    • Handle: RePEc:bla:rgscpp:v:15:y:2023:i:3:p:506-519
    DOI: 10.1111/rsp3.12555
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    References listed on IDEAS

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    1. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
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