Shawaqfah Moayyad, Almomani Fares
Department of Civil Engineering, Faculty of Engineering, Al al-Bayt University, Mafraq 25113, Jordan.
Department of Chemical Engineering, College of Engineering, Qatar University, P.O. Box 2713, Doha, Qatar.
Results Phys. 2021 Aug;27:104484. doi: 10.1016/j.rinp.2021.104484. Epub 2021 Jun 21.
The present study illustrates the outbreak prediction and analysis on the growth and expansion of the COVID-19 pandemic using artificial neural network (ANN). The first wave of the pandemic outbreak of the novel Coronavirus (SARS-CoV-2) began in September 2019 and continued to March 2020. As declared by the World Health Organization (WHO), this virus affected populations all over the globe, and its accelerated spread is a universal concern. An ANN architecture was developed to predict the serious pandemic outbreak impact in Qatar, Spain, and Italy. Official statistical data gathered from each country until July 6th was used to validate and test the prediction model. The model sensitivity was analyzed using the root mean square error (RMSE), the mean absolute percentage error and the regression coefficient index R, which yielded highly accurate values of the predicted correlation for the infected and dead cases of 0.99 for the dates considered. The verified and validated growth model of COVID-19 for these countries showed the effects of the measures taken by the government and medical sectors to alleviate the pandemic effect and the effort to decrease the spread of the virus in order to reduce the death rate. The differences in the spread rate were related to different exogenous factors (such as social, political, and health factors, among others) that are difficult to measure. The simple and well-structured ANN model can be adapted to different propagation dynamics and could be useful for health managers and decision-makers to better control and prevent the occurrence of a pandemic.
本研究阐述了利用人工神经网络（ANN）对新冠疫情的增长和蔓延进行爆发预测及分析。新型冠状病毒（SARS-CoV-2）大流行的第一波爆发始于2019年9月，并持续至2020年3月。正如世界卫生组织（WHO）所宣布的，这种病毒影响了全球各地的人群，其加速传播是一个普遍关切的问题。开发了一种人工神经网络架构，以预测卡塔尔、西班牙和意大利严重的疫情爆发影响。使用截至7月6日从每个国家收集的官方统计数据来验证和测试预测模型。使用均方根误差（RMSE）、平均绝对百分比误差和回归系数指数R对模型敏感性进行分析，在所考虑的日期中，感染病例和死亡病例的预测相关性得出了高度准确的值，为0.99。针对这些国家的经核实和验证的新冠疫情增长模型显示了政府和医疗部门为减轻疫情影响所采取措施的效果，以及为降低病毒传播以降低死亡率所做的努力。传播率的差异与不同的外部因素（如社会、政治和健康因素等）有关，这些因素难以衡量。简单且结构良好的人工神经网络模型可以适应不同的传播动态，对卫生管理人员和决策者更好地控制和预防大流行的发生可能会有所帮助。
