Volume 7, Issue 4 (Summer 2022)
Health in Emergencies and Disasters Quarterly 2022, 7(4): 177-182 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Omidi N, Omidi M R. Using an Artificial Neural Network Model to Predict the Number of COVID-19 Cases in Iran. Health in Emergencies and Disasters Quarterly 2022; 7 (4) :177-182
URL: http://hdq.uswr.ac.ir/article-1-381-en.html
URL: http://hdq.uswr.ac.ir/article-1-381-en.html
1- Department of Management, Payam Noor University, Tehran, Iran. , mromid_91@yahoo.com
2- Department of Industrial Engineering, Payam Noor University, Tehran, Iran.
2- Department of Industrial Engineering, Payam Noor University, Tehran, Iran.
Abstract: (1858 Views)
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.
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.
Type of Study: Research |
Subject:
General
Received: 2021/08/2 | Accepted: 2022/03/6 | Published: 2022/07/6
Received: 2021/08/2 | Accepted: 2022/03/6 | Published: 2022/07/6
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
Copyright © The Author(s);
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC-By-NC), which permits use, distribution, and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Contact Information
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC-By-NC), which permits use, distribution, and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Contact Information
