A statistical analysis of COVID-19 using Gaussian and probabilistic model. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Nayak, Soumya Ranjan; Arora, Vaibhav; Sinha, Utkarsh; Poonia, Ramesh Chandra
• Is Part Of: Journal of interdisciplinary mathematics. Volume 24:Issue 1(2021)
• Page Start: 19
• Page End: 32
• Publication Date: 2021-01-02
• Subjects: 60G15; COVID-19 -- Statistical analysis -- Gaussian model -- Epidemic control -- Predictive analysis -- ANOVA; Mathematics -- Periodicals; Mathematics; Periodicals; 510.5
• DOI: 10.1080/09720502.2020.1833442
• Languages: English
• ISSNs: 0972-0502

Abstract

Abstract: 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.