The Covid-19 pandemic has emerged as one of the most disquieting worldwide public health emergencies of the 21st century and has thrown into sharp relief, among other factors, the dire need for robust forecasting techniques for disease detection, alleviation as well as prevention. Forecasting has been one of the most powerful statistical methods employed the world over in various disciplines for detecting and analyzing trends and predicting future outcomes based on which timely and mitigating actions can be undertaken. To that end, several statistical methods and machine learning techniques have been harnessed depending upon the analysis desired and the availability of data. Historically speaking, most predictions thus arrived at have been short term and country-specific in nature. In this work, multimodel machine learning technique is called EAMA for forecasting Covid-19 related parameters in the long-term both within India and on a global scale have been proposed. This proposed EAMA hybrid model is well-suited to predictions based on past and present data. For this study, two datasets from the Ministry of Health & Family Welfare of India and Worldometers, respectively, have been exploited. Using these two datasets, long-term data predictions for both India and the world have been outlined, and observed that predicted data being very similar to real-time values. The experiment also conducted for statewise predictions of India and the countrywise predictions across the world and it has been included in the Appendix.

中文翻译：

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使用监督机器学习模型预测 Covid-19 影响的方法


Covid-19 大流行已成为 21 世纪最令人不安的全球公共卫生突发事件之一，并突显了人们迫切需要用于疾病检测、缓解和预防的强大预测技术。预测一直是世界各地各个学科中使用的最强大的统计方法之一，用于检测和分析趋势并预测未来结果，在此基础上可以采取及时和缓解的行动。为此，根据所需的分析和数据的可用性，利用了多种统计方法和机器学习技术。从历史上看，由此得出的大多数预测本质上都是短期的、针对具体国家的。在这项工作中，提出了被称为 EAMA 的多模型机器学习技术，用于在印度和全球范围内长期预测 Covid-19 相关参数。这种提出的 EAMA 混合模型非常适合基于过去和当前数据的预测。本研究利用了分别来自印度卫生和家庭福利部和 Worldometers 的两个数据集。使用这两个数据集，概述了印度和世界的长期数据预测，并观察到预测数据与实时值非常相似。该实验还对印度的州级预测和世界各国的州级预测进行了实验，并已纳入附录。