Forecasting of COVID-19 Cases in India: A Predictive Study

Q4 Medicine
P. Sharma, Tanu Sharma, K. Veer
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引用次数: 0

Abstract

An outbreak of new coronavirus (COVID-19) originated by SARS-CoV has reached 212 countries throughout the world. India is the second-highest populated country, so it is critical to forecasting the confirmed cases and deaths due to pandemic. To fulfil the purpose, three machine learning models Linear Regression, Multilayer Perceptron, and Sequential Minimal Optimization Regression are used. The predictive data of three geographic regions (India, Maharashtra, and Tamil Nadu) are compared with the data considered to be adequate in practice. The analysis concluded that Sequential Minimal Optimization Regression can be adopted for possible pandemic predictions such as COVID-19.
印度新冠肺炎病例预测:一项预测研究
由SARS-CoV引起的新型冠状病毒(新冠肺炎)疫情已波及全球212个国家。印度是人口第二高的国家,因此预测疫情导致的确诊病例和死亡至关重要。为了实现这一目的,使用了三个机器学习模型线性回归、多层感知器和序列最小优化回归。将三个地理区域(印度、马哈拉施特拉邦和泰米尔纳德邦)的预测数据与实践中认为足够的数据进行了比较。分析得出结论,序列最小优化回归可用于新冠肺炎等可能的大流行预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Current Immunology Reviews
Current Immunology Reviews Medicine-Immunology and Allergy
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期刊介绍: Current Immunology Reviews publishes frontier reviews on all the latest advances in clinical immunology. The journal"s aim is to publish the highest quality review articles dedicated to clinical research in the field. The journal is essential reading for all researchers and clinicians in clinical immunology.
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