دوره 7، شماره 4 - ( 4-1401 )
جلد 7 شماره 4 صفحات 182-177 |
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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-fa.html
URL: http://hdq.uswr.ac.ir/article-1-381-fa.html
Using an Artificial Neural Network Model to Predict the Number of COVID-19 Cases in Iran. فصلنامه سلامت در حوادث و بلایا. 1401; 7 (4) :177-182
چکیده: (1261 مشاهده)
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.
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