A statistical analysis of COVID-19 using Gaussian and probabilistic model

被引：15

作者：

Nayak, Soumya Ranjan ^{[1
]}

Arora, Vaibhav ^{[1
]}

Sinha, Utkarsh ^{[1
]}

Poonia, Ramesh Chandra ^{[2
]}

机构：

[1] Amity Univ Noida, Amity Sch Engn & Technol, Noida 201301, Uttar Pradesh, India

[2] Amity Univ Jaipur, Amity Inst Informat Technol, Jaipur 303007, Rajasthan, India

来源：

JOURNAL OF INTERDISCIPLINARY MATHEMATICS | 2021年 / 24卷 / 01期

关键词：

COVID-19; Statistical analysis; Gaussian model; Epidemic control; Predictive analysis; ANOVA;

D O I：

10.1080/09720502.2020.1833442

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

SARS Cov-2, COVID-19 (Coronavirus) emerged in Wuhan in early December 2019 and then spread exponentially across the globe. Although, a series of prevention strategies (lockdown, social-distancing) have been enforced to control this pandemic. In this study, we have made statistical analysis in terms of Gaussian modeling, ANOVA test and probabilistic model. After applying ANOVA we can conclude that the recovery rate for all the countries are significantly higher than the mortality rate except for the UK where the mortality rate is significantly higher than the recovery rate. Gaussian modeling applied here was able to predict the original peak values of confirmed cases of countries. Using the probabilistic model we were able to predict that there is around 5% probability that a person in India will be tested positive for COVID-19 on 100 tests.

引用

页码：19 / 32

页数：14

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