Modeling cumulative cases of Covid-19 in Yazd city using various time series techniques and machine learning and comparing their efficiency

Document Type : Industry Article

Authors

1 M.Sc. degree, Department of Industrial Engineering, Faculty of Industrial Engineering, Yazd University, Yazd, Iran

2 Associate Professor, Department of Industrial Engineering, Faculty of Industrial Engineering, Yazd University, Yazd, Iran

Abstract

Coronavirus disease 2019 or Covid-19, which is also called acute respiratory disease NCAV-2019 or commonly called corona, is a respiratory disease caused by acute respiratory syndrome coronavirus-2. Forecasting the number of new cases and deaths during todays can be a useful step in predicting the costs and facilities needed in the future. This study aims to model and predict new cases and deaths efficiently in the future. Nine popular forecasting techniques are tested on the data of Covid-19 in Yazd city as a case study. Using the evaluation criteria of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and the mean absolute percentage of error (MAPE) of the models are compared. According to the selected evaluation criteria, the results of the comprehensive analysis emphasize that the most efficient models are the ARIMA model for predicting the cumulative cases of hospitalization of Covid-19 and the Theta model for the cumulative cases of death. Also, the autoregressive neural network model has the worst performance among other models for both hospitalization and death cases.

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References

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Journal of Modeling in Engineering
Volume 21, Issue 75
December 2023
Pages 309-323

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History

  • Receive Date: 09 February 2023
  • Revise Date: 12 July 2023
  • Accept Date: 03 September 2023

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