Optimization Method for Forecasting Confirmed Cases of COVID-19 in China
Optimization Method for Forecasting Confirmed Cases of COVID-19 in China
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DOI：
10.3390/jcm9030674
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发表时间：
2020-03-01
影响因子：
3.9
通讯作者：
Abd El Aziz, Mohamed
中科院分区：
文献类型：
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作者：
Al-qaness, Mohammed A. A.;Ewees, Ahmed A.;Abd El Aziz, Mohamed
2019年12月，一种名为COVID-19的新型冠状病毒在中国武汉被发现，并已蔓延到中国的不同城市以及其他24个国家。确诊病例每天都在增加，2020年2月8日达到34，598例。在目前的研究中，我们提出了一个新的预测模型，以估计和预测未来十天的COVID-19确诊病例数量，基于中国先前记录的确诊病例。该模型是一种改进的自适应神经模糊推理系统（ANFIS），使用了一种增强的花授粉算法（FPA），通过使用salp群算法（SSA）。一般来说，SSA用于改进FPA以避免其缺点（即，陷入局部最优解）。提出的模型，称为FPASSA-ANFIS的主要思想是通过使用FPASSA确定ANFIS的参数来提高ANFIS的性能。FPASSA-ANFIS模型使用世界卫生组织（WHO）的COVID-19爆发官方数据进行评估，以预测未来十天的确诊病例。此外，FPASSA-ANFIS模型相比，现有的几个模型，它表现出更好的性能方面的平均绝对百分比误差（MAPE），均方根相对误差（RMSRE），均方根相对误差（RMSRE），决定系数（R2），和计算时间。此外，我们使用两个国家（即美国和中国）每周流感确诊病例的两个不同数据集对所提出的模型进行了测试。结果也显示了良好的表现。
In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. The number of confirmed cases is increasing daily and reached 34,598 on 8 February 2020. In the current study, we present a new forecasting model to estimate and forecast the number of confirmed cases of COVID-19 in the upcoming ten days based on the previously confirmed cases recorded in China. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using an enhanced flower pollination algorithm (FPA) by using the salp swarm algorithm (SSA). In general, SSA is employed to improve FPA to avoid its drawbacks (i.e., getting trapped at the local optima). The main idea of the proposed model, called FPASSA-ANFIS, is to improve the performance of ANFIS by determining the parameters of ANFIS using FPASSA. The FPASSA-ANFIS model is evaluated using the World Health Organization (WHO) official data of the outbreak of the COVID-19 to forecast the confirmed cases of the upcoming ten days. More so, the FPASSA-ANFIS model is compared to several existing models, and it showed better performance in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), Root Mean Squared Relative Error (RMSRE), coefficient of determination (R2), and computing time. Furthermore, we tested the proposed model using two different datasets of weekly influenza confirmed cases in two countries, namely the USA and China. The outcomes also showed good performances.