Molecular Biology Research Lab., Department of Zoology, Deshbandhu College, University of Delhi, New Delhi, India.
Department of Computer Science, Deshbandhu College, University of Delhi, New Delhi, India.
Front Cell Infect Microbiol. 2022 Feb 8;11:806265. doi: 10.3389/fcimb.2021.806265. eCollection 2021.
Coronavirus Disease 2019 (COVID-19) is spreading across the world, and vaccinations are running parallel. Coronavirus has mutated into a triple-mutated virus, rendering it deadlier than before. It spreads quickly from person to person by contact and nasal or pharyngeal droplets. The COVID-19 database 'Our World in Data' was analyzed from February 24, 2020, to September 26, 2021, and predictions on the COVID positives and their mortality rate were made. Factors such as Vaccine data for the First and Second Dose vaccinated individuals and COVID positives that influence the fluctuations in the COVID-19 death ratio were investigated and linear regression analysis was performed. Based on vaccination doses (partial or complete vaccinated), models are created to estimate the number of patients who die from COVID infection. The estimation of variance in the datasets was investigated using Karl Pearson's coefficient. For COVID-19 cases and vaccination doses, a quartic polynomial regression model was also created. This predictor model helps to predict the number of deaths due to COVID-19 and determine the susceptibility to COVID-19 infection based on the number of vaccine doses received. SVM was used to analyze the efficacy of models generated.
2019 年冠状病毒病（COVID-19）正在全球范围内蔓延，疫苗接种工作也在同时进行。冠状病毒已经突变成为三重突变病毒，其致命性比以前更高。它通过接触和鼻或咽飞沫在人与人之间迅速传播。从 2020 年 2 月 24 日到 2021 年 9 月 26 日，对 COVID-19 数据库“我们的世界数据”进行了分析，并对 COVID 阳性病例及其死亡率进行了预测。研究了影响 COVID-19 死亡率波动的因素，如第一和第二剂疫苗接种人群的数据和 COVID 阳性病例，并进行了线性回归分析。基于疫苗接种剂量（部分或完全接种），创建了模型来估计因 COVID 感染而死亡的患者人数。使用 Karl Pearson 系数研究了数据集的方差估计。对于 COVID-19 病例和疫苗接种剂量，还创建了一个四次多项式回归模型。该预测模型有助于预测 COVID-19 死亡人数，并根据接种疫苗的剂量来确定对 COVID-19 感染的易感性。SVM 用于分析生成模型的效果。
