School of Statistics, Jiangxi University of Finance and Economics, Nanchang, China; Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan.
School of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, China; Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan.
Comput Methods Programs Biomed. 2020 Dec;197:105704. doi: 10.1016/j.cmpb.2020.105704. Epub 2020 Aug 18.
The current scenario of the Pandemic of COVID-19 demands multi-channel investigations and predictions. A variety of prediction models are available in the literature. The majority of these models are based on extrapolating by the parameters related to the diseases, which are history-oriented. Instead, the current research is designed to predict the mortality rate of COVID-19 by Regression techniques in comparison to the models followed by five countries.
The Regression method with an optimized hyper-parameter is used to develop these models under training data by Machine Learning Technique.
The validity of the proposed model is endorsed by considering the case study on the data for Pakistan. Five distinct models for mortality rate prediction are built using Confirmed cases data as a predictor variable for France, Spain, Turkey, Sweden, and Pakistan, respectively. The results evidenced that Sweden has a fewer death case over 20,000 confirmed cases without observing lockdown. Hence, by following the strategy adopted by Sweden, the chosen entity will control the death rate despite the increase of the confirmed cases.
The evaluated results notice the high mortality rate and low RMSE for Pakistan by the GPR method based Mortality model. Therefore, the morality rate based MRP model is selected for the COVID-19 death rate in Pakistan. Hence, the best-fit is the Sweden model to control the mortality rate.
当前 COVID-19 大流行的情况需要多渠道的调查和预测。文献中有多种预测模型。这些模型大多基于与疾病相关的参数进行推断，这是面向历史的。相反，当前的研究旨在通过回归技术预测 COVID-19 的死亡率，与五个国家所遵循的模型进行比较。
使用机器学习技术在训练数据下通过优化超参数的回归方法来开发这些模型。
通过考虑巴基斯坦数据的案例研究，证明了所提出模型的有效性。使用确诊病例数据作为法国、西班牙、土耳其、瑞典和巴基斯坦的预测变量，分别为死亡率预测建立了五个不同的模型。结果表明，瑞典在没有观察到封锁的情况下，在超过 20000 例确诊病例的情况下，死亡病例较少。因此，通过遵循瑞典采用的策略，所选实体将能够控制死亡率，尽管确诊病例有所增加。
评估结果注意到基于 GPR 方法的死亡率模型对巴基斯坦的高死亡率和低 RMSE。因此，选择基于死亡率的 MRP 模型来预测巴基斯坦的 COVID-19 死亡率。因此，最适合控制死亡率的是瑞典模型。
