Background: The outbreak of COVID-19 is rapidly spreading around the world and became a pandemic disease For help to better planning of interventions, this study was conducted to forecast the number of daily new infected cases with COVID-19 for next thirty days in Iran Methods: The information of observed Iranian new cases from 19th Feb to 30th Mar 2020 was used to predict the number of patients until 29th Apr Artificial Neural Networks (ANN) and Auto-Regressive Integrated Mov-ing Average (ARIMA) models were applied for prediction The data was prepared from daily reports of Iran Ministry of Health and open datasets provided by the JOHN Hopkins To compare models, dataset was sepa-rated into train and test sets Mean Squared Error (MSE) and Mean Absolute Error (MAE) was the comparison criteria Results: Both algorithms forecasted an exponential increase in number of newly infected patients If the spreading pattern continues the same as before, the number of daily new cases would be 7872 and 9558 by 29th Apr, respectively by ANN and ARIMA While Model comparison confirmed that ARIMA prediction was more accurate than ANN Conclusion: COVID-19 is contagious disease, and has infected many people in Iran Our results are an alarm for health policy planners and decision-makers, to make timely decisions, control the disease and provide the equipment needed © 2020, Iranian Journal of Public Health All rights reserved

中文翻译：

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伊朗 COVID-19 感染患者呈指数增长趋势：神经网络和 ARIMA 预测模型的比较


背景：COVID-19 的爆发正在世界范围内迅速蔓延，并成为一种流行病 为了帮助更好地规划干预措施，进行这项研究是为了预测未来 30 天内伊朗每日新增的 COVID-19 感染病例数方法：利用2020年2月19日至3月30日观察到的伊朗新增病例信息来预测截至4月29日的患者人数，采用人工神经网络（ANN）和自回归综合移动平均（ARIMA）模型进行预测数据是根据伊朗卫生部的每日报告和约翰·霍普金斯大学提供的开放数据集准备的。为了比较模型，数据集分为训练集和测试集均方误差（MSE）和平均绝对误差（MAE）进行比较结果：两种算法均预测新感染患者数量呈指数增长。如果传播模式继续与以前相同，则 ANN 和 ARIMA 到 4 月 29 日每日新增病例数将分别为 7872 例和 9558 例，而模型比较证实： ARIMA 预测比 ANN 更准确 结论：COVID-19 是一种传染病，已感染伊朗许多人 我们的结果为卫生政策规划者和决策者敲响了警钟，要求他们及时做出决策，控制疾病并提供所需的设备© 2020，伊朗公共卫生杂志版权所有