ArticleFrontiers in cellular and infection microbiology2021
Analyzing the Effect of Vaccination Over COVID Cases and Deaths in Asian Countries Using Machine Learning Models.
Article in Frontiers in cellular and infection microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Klebsiella pneumoniae in the global AMR: resistance mechanisms and genomic adaptation.World journal of microbiology & biotechnology · 2026Review
- Exploring the correlates of COVID-19 vaccination inequity: a global analysis using machine learning from a health economic lens.Frontiers in health services · 2026Article
- Machine learning insights on the effectiveness of non-pharmaceutical interventions against COVID-19 in Nigeria.International health · 2025Article
- A protocol for modeling the factors influencing the deployment of the COVID-19 vaccine across African countries.PloS one · 2024Article
- Outcome prediction in hospitalized COVID-19 patients: Comparison of the performance of five severity scores.Frontiers in medicine · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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