Modeling cumulative cases of Covid-19 in Yazd city using various time series techniques and machine learning and comparing their efficiency
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.
-
Analysis of operational risks of electricity distribution network based on the fuzzy cognitive map approach
Elham Fallah Baghemoortini, , Majid Alimohammadi Ardakani *
Journal of Energy Management and Technology, Autumn 2024 -
A novel approach to multi-objective portfolio selection: modeling emerging financial markets using satisfaction functions and fuzzy values
Milad Saleki, Mohamad Saber Falah Nejad *, , Mohammad Aref Dehghani Tafti
Journal of Decisions and Operations Research, -
Comparison of Models in Predicting Cumulative Cases of Hospitalization and Death of Covid-19 (Case Study: Bahabad city
MohammadHossein Karimizarchi, *
Journal of Health Management, -
Forecasting the Spread of COVID-19 Using Time Series in Mehriz City, Iran
MohammadHossein Karimizarchi, *
Iranian Journal of Health Insurance,