Comparison of Models in Predicting Cumulative Cases of Hospitalization and Death of Covid-19 (Case Study: Bahabad city

Subject Areas : -Health economy

mohammad hossein karimizarchi ¹ (M.Sc. degree, Department of Industrial Engineering, Yazd University, Yazd, Iran)
Davood Shishebori ² (Associate Professor, Department of Industrial Engineering, Yazd University, Yazd, Iran)

Received: 2023-03-01 Accepted : 2023-06-26 Published : 2023-09-23

Keywords: Time series, Statistical modeling, Covid-19, Forecasting, pandemic,

Abstract :

Introduction: Coronavirus disease 2019 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 modeling, comparing the performance of models, and predict new cases and deaths efficiently in the future. Methods: In this article nine popular forecasting techniques are tested on the data of Covid-19 in Bahabad 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.  Results: The results of the analysis showed that the best model according to the evaluation criteria for forecasting cumulative cases of hospitalization of Covid-19 is the cubic spline smoothing model, and cumulative cases of death, KNN regression model. Also, autoregressive neural network and theta models for hospitalization cases, and for death cases, autoregressive neural network model has the worst performance among other models. Conclusion: This study can provide a proper understanding of the spread of covid-19 disease in this region so that by taking precautionary measures and formulating appropriate policies, this epidemic can be effectively overcome. Also, unlike other studies, this study uses 9 different techniques and their comparison, which in turn increases the confidence factor in decision making. Also, an important point is that the data should be updated in real time.

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