Using an Artificial Neural Network Model to Predict the Number of COVID-19 Cases in Iran
Background: Forecasting methods are used in various fields including the health problems. This study aims to use the Artificial Neural Network (ANN) method for predicting coronavirus disease 2019 (COVID-19) cases in Iran. Materials and Methods: This is a descriptive, analytical, and comparative stud...
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| Autor*in: |
Nabi Omidi [verfasserIn] Mohammad Reza Omidi [verfasserIn] |
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E-Artikel |
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Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Health in Emergencies & Disasters Quarterly - Negah Institute for Scientific Communication, 2016, 7(2022), 4, Seite 177-182 |
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volume:7 ; year:2022 ; number:4 ; pages:177-182 |
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| 520 | |a Background: Forecasting methods are used in various fields including the health problems. This study aims to use the Artificial Neural Network (ANN) method for predicting coronavirus disease 2019 (COVID-19) cases in Iran. Materials and Methods: This is a descriptive, analytical, and comparative study to predict the time series of COVID-19 cases in Iran from May 2020 to May 2021. An ANN model was used for forecasting, which had three Input, output, and intermediate layers. The network training was conducted by the Levenberg-Marquardt algorithm. The forecasting accuracy was measured by calculating the mean absolute percentage error. Results: The mean absolute error of the designed ANN model was 6 and its accuracy was 94%. Conclusion: The ANN has high accuracy in forecasting the number of COVID-19 cases in Iran. The outputs of this model can be used as a basis for decisions in controlling the COVID-19. | ||
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(DE-627)DOAJ029195624 (DE-599)DOAJ971a5483c08649128bfda742f54bdc57 DE-627 ger DE-627 rakwb eng RC86-88.9 Nabi Omidi verfasserin aut Using an Artificial Neural Network Model to Predict the Number of COVID-19 Cases in Iran 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Forecasting methods are used in various fields including the health problems. This study aims to use the Artificial Neural Network (ANN) method for predicting coronavirus disease 2019 (COVID-19) cases in Iran. Materials and Methods: This is a descriptive, analytical, and comparative study to predict the time series of COVID-19 cases in Iran from May 2020 to May 2021. An ANN model was used for forecasting, which had three Input, output, and intermediate layers. The network training was conducted by the Levenberg-Marquardt algorithm. The forecasting accuracy was measured by calculating the mean absolute percentage error. Results: The mean absolute error of the designed ANN model was 6 and its accuracy was 94%. Conclusion: The ANN has high accuracy in forecasting the number of COVID-19 cases in Iran. The outputs of this model can be used as a basis for decisions in controlling the COVID-19. covid-19 forecasting artificial neural network time series Medical emergencies. Critical care. Intensive care. First aid Mohammad Reza Omidi verfasserin aut In Health in Emergencies & Disasters Quarterly Negah Institute for Scientific Communication, 2016 7(2022), 4, Seite 177-182 (DE-627)1760609242 23454210 nnns volume:7 year:2022 number:4 pages:177-182 https://doaj.org/article/971a5483c08649128bfda742f54bdc57 kostenfrei http://hdq.uswr.ac.ir/article-1-381-en.html kostenfrei https://doaj.org/toc/2345-4210 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA AR 7 2022 4 177-182 |
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(DE-627)DOAJ029195624 (DE-599)DOAJ971a5483c08649128bfda742f54bdc57 DE-627 ger DE-627 rakwb eng RC86-88.9 Nabi Omidi verfasserin aut Using an Artificial Neural Network Model to Predict the Number of COVID-19 Cases in Iran 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Forecasting methods are used in various fields including the health problems. This study aims to use the Artificial Neural Network (ANN) method for predicting coronavirus disease 2019 (COVID-19) cases in Iran. Materials and Methods: This is a descriptive, analytical, and comparative study to predict the time series of COVID-19 cases in Iran from May 2020 to May 2021. An ANN model was used for forecasting, which had three Input, output, and intermediate layers. The network training was conducted by the Levenberg-Marquardt algorithm. The forecasting accuracy was measured by calculating the mean absolute percentage error. Results: The mean absolute error of the designed ANN model was 6 and its accuracy was 94%. Conclusion: The ANN has high accuracy in forecasting the number of COVID-19 cases in Iran. The outputs of this model can be used as a basis for decisions in controlling the COVID-19. covid-19 forecasting artificial neural network time series Medical emergencies. Critical care. Intensive care. First aid Mohammad Reza Omidi verfasserin aut In Health in Emergencies & Disasters Quarterly Negah Institute for Scientific Communication, 2016 7(2022), 4, Seite 177-182 (DE-627)1760609242 23454210 nnns volume:7 year:2022 number:4 pages:177-182 https://doaj.org/article/971a5483c08649128bfda742f54bdc57 kostenfrei http://hdq.uswr.ac.ir/article-1-381-en.html kostenfrei https://doaj.org/toc/2345-4210 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA AR 7 2022 4 177-182 |
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(DE-627)DOAJ029195624 (DE-599)DOAJ971a5483c08649128bfda742f54bdc57 DE-627 ger DE-627 rakwb eng RC86-88.9 Nabi Omidi verfasserin aut Using an Artificial Neural Network Model to Predict the Number of COVID-19 Cases in Iran 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Forecasting methods are used in various fields including the health problems. This study aims to use the Artificial Neural Network (ANN) method for predicting coronavirus disease 2019 (COVID-19) cases in Iran. Materials and Methods: This is a descriptive, analytical, and comparative study to predict the time series of COVID-19 cases in Iran from May 2020 to May 2021. An ANN model was used for forecasting, which had three Input, output, and intermediate layers. The network training was conducted by the Levenberg-Marquardt algorithm. The forecasting accuracy was measured by calculating the mean absolute percentage error. Results: The mean absolute error of the designed ANN model was 6 and its accuracy was 94%. Conclusion: The ANN has high accuracy in forecasting the number of COVID-19 cases in Iran. The outputs of this model can be used as a basis for decisions in controlling the COVID-19. covid-19 forecasting artificial neural network time series Medical emergencies. Critical care. Intensive care. First aid Mohammad Reza Omidi verfasserin aut In Health in Emergencies & Disasters Quarterly Negah Institute for Scientific Communication, 2016 7(2022), 4, Seite 177-182 (DE-627)1760609242 23454210 nnns volume:7 year:2022 number:4 pages:177-182 https://doaj.org/article/971a5483c08649128bfda742f54bdc57 kostenfrei http://hdq.uswr.ac.ir/article-1-381-en.html kostenfrei https://doaj.org/toc/2345-4210 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA AR 7 2022 4 177-182 |
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(DE-627)DOAJ029195624 (DE-599)DOAJ971a5483c08649128bfda742f54bdc57 DE-627 ger DE-627 rakwb eng RC86-88.9 Nabi Omidi verfasserin aut Using an Artificial Neural Network Model to Predict the Number of COVID-19 Cases in Iran 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Forecasting methods are used in various fields including the health problems. This study aims to use the Artificial Neural Network (ANN) method for predicting coronavirus disease 2019 (COVID-19) cases in Iran. Materials and Methods: This is a descriptive, analytical, and comparative study to predict the time series of COVID-19 cases in Iran from May 2020 to May 2021. An ANN model was used for forecasting, which had three Input, output, and intermediate layers. The network training was conducted by the Levenberg-Marquardt algorithm. The forecasting accuracy was measured by calculating the mean absolute percentage error. Results: The mean absolute error of the designed ANN model was 6 and its accuracy was 94%. Conclusion: The ANN has high accuracy in forecasting the number of COVID-19 cases in Iran. The outputs of this model can be used as a basis for decisions in controlling the COVID-19. covid-19 forecasting artificial neural network time series Medical emergencies. Critical care. Intensive care. First aid Mohammad Reza Omidi verfasserin aut In Health in Emergencies & Disasters Quarterly Negah Institute for Scientific Communication, 2016 7(2022), 4, Seite 177-182 (DE-627)1760609242 23454210 nnns volume:7 year:2022 number:4 pages:177-182 https://doaj.org/article/971a5483c08649128bfda742f54bdc57 kostenfrei http://hdq.uswr.ac.ir/article-1-381-en.html kostenfrei https://doaj.org/toc/2345-4210 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA AR 7 2022 4 177-182 |
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Background: Forecasting methods are used in various fields including the health problems. This study aims to use the Artificial Neural Network (ANN) method for predicting coronavirus disease 2019 (COVID-19) cases in Iran. Materials and Methods: This is a descriptive, analytical, and comparative study to predict the time series of COVID-19 cases in Iran from May 2020 to May 2021. An ANN model was used for forecasting, which had three Input, output, and intermediate layers. The network training was conducted by the Levenberg-Marquardt algorithm. The forecasting accuracy was measured by calculating the mean absolute percentage error. Results: The mean absolute error of the designed ANN model was 6 and its accuracy was 94%. Conclusion: The ANN has high accuracy in forecasting the number of COVID-19 cases in Iran. The outputs of this model can be used as a basis for decisions in controlling the COVID-19. |
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Background: Forecasting methods are used in various fields including the health problems. This study aims to use the Artificial Neural Network (ANN) method for predicting coronavirus disease 2019 (COVID-19) cases in Iran. Materials and Methods: This is a descriptive, analytical, and comparative study to predict the time series of COVID-19 cases in Iran from May 2020 to May 2021. An ANN model was used for forecasting, which had three Input, output, and intermediate layers. The network training was conducted by the Levenberg-Marquardt algorithm. The forecasting accuracy was measured by calculating the mean absolute percentage error. Results: The mean absolute error of the designed ANN model was 6 and its accuracy was 94%. Conclusion: The ANN has high accuracy in forecasting the number of COVID-19 cases in Iran. The outputs of this model can be used as a basis for decisions in controlling the COVID-19. |
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Background: Forecasting methods are used in various fields including the health problems. This study aims to use the Artificial Neural Network (ANN) method for predicting coronavirus disease 2019 (COVID-19) cases in Iran. Materials and Methods: This is a descriptive, analytical, and comparative study to predict the time series of COVID-19 cases in Iran from May 2020 to May 2021. An ANN model was used for forecasting, which had three Input, output, and intermediate layers. The network training was conducted by the Levenberg-Marquardt algorithm. The forecasting accuracy was measured by calculating the mean absolute percentage error. Results: The mean absolute error of the designed ANN model was 6 and its accuracy was 94%. Conclusion: The ANN has high accuracy in forecasting the number of COVID-19 cases in Iran. The outputs of this model can be used as a basis for decisions in controlling the COVID-19. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ029195624</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502143746.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ029195624</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ971a5483c08649128bfda742f54bdc57</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC86-88.9</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Nabi Omidi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Using an Artificial Neural Network Model to Predict the Number of COVID-19 Cases in Iran</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background: Forecasting methods are used in various fields including the health problems. This study aims to use the Artificial Neural Network (ANN) method for predicting coronavirus disease 2019 (COVID-19) cases in Iran. Materials and Methods: This is a descriptive, analytical, and comparative study to predict the time series of COVID-19 cases in Iran from May 2020 to May 2021. An ANN model was used for forecasting, which had three Input, output, and intermediate layers. The network training was conducted by the Levenberg-Marquardt algorithm. The forecasting accuracy was measured by calculating the mean absolute percentage error. Results: The mean absolute error of the designed ANN model was 6 and its accuracy was 94%. Conclusion: The ANN has high accuracy in forecasting the number of COVID-19 cases in Iran. 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First aid</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mohammad Reza Omidi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Health in Emergencies & Disasters Quarterly</subfield><subfield code="d">Negah Institute for Scientific Communication, 2016</subfield><subfield code="g">7(2022), 4, Seite 177-182</subfield><subfield code="w">(DE-627)1760609242</subfield><subfield code="x">23454210</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:7</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:4</subfield><subfield code="g">pages:177-182</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/971a5483c08649128bfda742f54bdc57</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://hdq.uswr.ac.ir/article-1-381-en.html</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2345-4210</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">7</subfield><subfield code="j">2022</subfield><subfield code="e">4</subfield><subfield code="h">177-182</subfield></datafield></record></collection>
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