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 c...
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| Format: | Article |
| Language: | fas |
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Semnan University
2023-12-01
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| Series: | مجله مدل سازی در مهندسی |
| Subjects: | |
| Online Access: | https://modelling.semnan.ac.ir/article_8349_d41d8cd98f00b204e9800998ecf8427e.pdf |
| _version_ | 1797296444758032384 |
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| author | Mohammadhossein Karimizarchi Davood Shishebori |
| author_facet | Mohammadhossein Karimizarchi Davood Shishebori |
| author_sort | Mohammadhossein Karimizarchi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T22:04:49Z |
| format | Article |
| id | doaj.art-0078887157f247459b89b62f96b23b70 |
| institution | Directory Open Access Journal |
| issn | 2008-4854 2783-2538 |
| language | fas |
| last_indexed | 2024-03-07T22:04:49Z |
| publishDate | 2023-12-01 |
| publisher | Semnan University |
| record_format | Article |
| series | مجله مدل سازی در مهندسی |
| spelling | doaj.art-0078887157f247459b89b62f96b23b702024-02-23T19:11:16ZfasSemnan Universityمجله مدل سازی در مهندسی2008-48542783-25382023-12-01217510.22075/jme.2023.29849.24048349Modeling cumulative cases of Covid-19 in Yazd city using various time series techniques and machine learning and comparing their efficiencyMohammadhossein Karimizarchi0Davood Shishebori1Department of Industrial Engineering, Yazd University, Yazd, IranyazdCoronavirus 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.https://modelling.semnan.ac.ir/article_8349_d41d8cd98f00b204e9800998ecf8427e.pdfcovid-19time seriesforecastingstatistical modeling |
| spellingShingle | Mohammadhossein Karimizarchi Davood Shishebori Modeling cumulative cases of Covid-19 in Yazd city using various time series techniques and machine learning and comparing their efficiency مجله مدل سازی در مهندسی covid-19 time series forecasting statistical modeling |
| title | Modeling cumulative cases of Covid-19 in Yazd city using various time series techniques and machine learning and comparing their efficiency |
| title_full | Modeling cumulative cases of Covid-19 in Yazd city using various time series techniques and machine learning and comparing their efficiency |
| title_fullStr | Modeling cumulative cases of Covid-19 in Yazd city using various time series techniques and machine learning and comparing their efficiency |
| title_full_unstemmed | Modeling cumulative cases of Covid-19 in Yazd city using various time series techniques and machine learning and comparing their efficiency |
| title_short | Modeling cumulative cases of Covid-19 in Yazd city using various time series techniques and machine learning and comparing their efficiency |
| title_sort | modeling cumulative cases of covid 19 in yazd city using various time series techniques and machine learning and comparing their efficiency |
| topic | covid-19 time series forecasting statistical modeling |
| url | https://modelling.semnan.ac.ir/article_8349_d41d8cd98f00b204e9800998ecf8427e.pdf |
| work_keys_str_mv | AT mohammadhosseinkarimizarchi modelingcumulativecasesofcovid19inyazdcityusingvarioustimeseriestechniquesandmachinelearningandcomparingtheirefficiency AT davoodshishebori modelingcumulativecasesofcovid19inyazdcityusingvarioustimeseriestechniquesandmachinelearningandcomparingtheirefficiency |