دوره 7، شماره 4 - ( 4-1401 )                   جلد 7 شماره 4 صفحات 182-177 | برگشت به فهرست نسخه ها


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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
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) :182-177

URL: http://hdq.uswr.ac.ir/article-1-381-fa.html


چکیده:   (327 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 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.
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نوع مطالعه: پژوهشي | موضوع مقاله: عمومى
دریافت: 1400/5/11 | پذیرش: 1400/12/15 | انتشار: 1401/4/15

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