The World Health Organization has declared COVID-19 as a global pandemic in early 2020. A comprehensive understanding of the epidemiological characteristics of this virus is crucial to limit its spreading. Therefore, this research applies artificial intelligence-based models to predict the prevalence of the COVID-19 outbreak in Egypt. These models are long short-term memory network (LSTM), convolutional neural network, and multilayer perceptron neural network. They are trained and validated using the dataset records from 14 February 2020 to 15 August 2020. The results of the models are evaluated using the determination coefficient and root mean square error. The LSTM model exhibits the best performance in forecasting the cumulative infections for one week and one month ahead. Finally, the LSTM model with the optimal parameter values is applied to forecast the spread of this epidemic for one month ahead using the data from 14 February 2020 to 30 June 2021. The total size of infections, recoveries, and deaths is estimated to be 285,939, 234,747, and 17,251 cases on 31 July 2021. This study could assist the decision-makers in developing and monitoring policies to confront this disease.

世界卫生组织已于 2020 年初宣布 COVID-19 为全球大流行病。全面了解该病毒的流行病学特征对于限制其传播至关重要。因此，这项研究应用基于人工智能的模型来预测埃及 COVID-19 疫情的流行情况。这些模型是长短期记忆网络（LSTM）、卷积神经网络和多层感知器神经网络。使用2020年2月14日至2020年8月15日的数据集记录对它们进行训练和验证。使用判定系数和均方根误差来评估模型的结果。LSTM 模型在预测未来一周和一个月的累积感染情况方面表现出最佳性能。最后，使用具有最佳参数值的LSTM模型，利用2020年2月14日至2021年6月30日的数据来预测本次疫情未来一个月的传播情况。感染、康复和死亡的总规模估计为285,939截至 2021 年 7 月 31 日，分别有 234,747 例和 17,251 例病例。这项研究可以帮助决策者制定和监测应对这种疾病的政策。