Volume 7, Issue 4 (Summer 2022)                   Health in Emergencies and Disasters Quarterly 2022, 7(4): 177-182 | Back to browse issues page


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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
1- Department of Management, Payam Noor University, Tehran, Iran. , mromid_91@yahoo.com
2- Department of Industrial Engineering, Payam Noor University, Tehran, Iran.
Abstract:   (199 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 forecast​ing, 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.
Full-Text [PDF 656 kb]   (78 Downloads)    
Type of Study: Research | Subject: General
Received: 2021/08/2 | Accepted: 2022/03/6 | Published: 2022/07/6

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