tetano
Editor, Senior Moderator
Front Cell Infect Microbiol
. 2022 Feb 8;11:806265.
doi: 10.3389/fcimb.2021.806265. eCollection 2021.
Analyzing the Effect of Vaccination Over COVID Cases and Deaths in Asian Countries Using Machine Learning Models
Vanshika Rustagi[SUP] 1 [/SUP], Monika Bajaj[SUP] 2 [/SUP], Tanvi[SUP] 3 [/SUP], Priya Singh[SUP] 1 [/SUP], Rajiv Aggarwal[SUP] 4 [/SUP], Mohamed F AlAjmi[SUP] 5 [/SUP], Afzal Hussain[SUP] 5 [/SUP], Md Imtaiyaz Hassan[SUP] 6 [/SUP], Archana Singh[SUP] 7 [/SUP], Indrakant K Singh[SUP] 1 3 [/SUP]
Affiliations
Abstract
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.
Keywords: COVID-19; OLS regression; Support Vector Machine (SVM); linear regression; machine learning; polynomial distribution.
. 2022 Feb 8;11:806265.
doi: 10.3389/fcimb.2021.806265. eCollection 2021.
Analyzing the Effect of Vaccination Over COVID Cases and Deaths in Asian Countries Using Machine Learning Models
Vanshika Rustagi[SUP] 1 [/SUP], Monika Bajaj[SUP] 2 [/SUP], Tanvi[SUP] 3 [/SUP], Priya Singh[SUP] 1 [/SUP], Rajiv Aggarwal[SUP] 4 [/SUP], Mohamed F AlAjmi[SUP] 5 [/SUP], Afzal Hussain[SUP] 5 [/SUP], Md Imtaiyaz Hassan[SUP] 6 [/SUP], Archana Singh[SUP] 7 [/SUP], Indrakant K Singh[SUP] 1 3 [/SUP]
Affiliations
- PMID: 35223534
- PMCID: PMC8877421
- DOI: 10.3389/fcimb.2021.806265
Abstract
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.
Keywords: COVID-19; OLS regression; Support Vector Machine (SVM); linear regression; machine learning; polynomial distribution.