Anticipating the quantity of new associated or affirmed cases with novel coronavirus ailment 2019 (COVID-19) is critical in the counteraction and control of the COVID-19 flare-up. The new associated cases with COVID-19 information were gathered from 20 January 2020 to 21 July 2020. We filtered out the countries which are converging and used those for training the network. We utilized the SARIMAX, Linear regression model to anticipate new suspected COVID-19 cases for the countries which did not converge yet. We predict the curve of non-converged countries with the help of proposed Statistical SARIMAX model (SSM). We present new information investigation-based forecast results that can assist governments with planning their future activities and help clinical administrations to be more ready for what's to come. Our framework can foresee peak corona cases with an R-Squared value of 0.986 utilizing linear regression and fall of this pandemic at various levels for countries like India, US, and Brazil. We found that considering more countries for training degrades the prediction process as constraints vary from nation to nation. Thus, we expect that the outcomes referenced in this work will help individuals to better understand the possibilities of this pandemic.
翻译:预测2019年新冠状病毒疾病(COVID-19)的新病例的数量,对于应对和控制COVID-19信号(COVID-19)至关重要。2020年1月20日至2020年7月21日收集了COVID-19信息的新病例。我们过滤了那些正在汇集并利用它们来培训网络的国家。我们利用SARIMAX(线性回归模型)来预测尚未聚集的国家新的疑似COVID-19病例的数量。我们利用拟议的SASIMAX(SM)模型(SSSM)来预测非趋同国家的曲线。我们提出了新的基于信息的调查预测结果,可以帮助各国政府规划其未来活动,并帮助临床管理部门为未来行动做好准备。我们的框架可以预测R-Squaled值为0.986的顶峰冠状病例,用于印度、美国和巴西等国的线性回归和这种流行病在各个层次的下降。我们发现,考虑更多的培训国家会降低预测过程,因为各国所受的限制各不相同。因此,我们期望在这项工作中能够了解这一结果。