Predictors of COVID-19 vaccination rate in USA: A machine learning approach

Osman, Syed Muhammad Ishraque; Sabit, Ahmed

doi:10.1016/j.mlwa.2022.100408

Cited by 5 publications

(3 citation statements)

References 37 publications

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“…[31] verified the impact of some variables in vaccination coverage and suggested that interventions be undertaken to improve COVID-19 vaccine acceptance and future uptake. The study conducted by [32] utilized machine learning to study the vaccination rate in the USA. The findings provide insights to increase vaccination acceptance and combat the COVID-19 pandemic.…”

Section: Covid-19 Vaccination Rates On County-levelmentioning

confidence: 99%

Analyzing County-Level COVID-19 Vaccination Rates in Texas: A New Lindley Regression Model

da Costa,

de Lima,

Cordeiro

2023

COVID

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This work aims to study the factors that explain the COVID-19 vaccination rate through a generalized odd log-logistic Lindley regression model with a shape systematic component. To accomplish this, a dataset of the vaccination rate of 254 counties in the state of Texas, US, was used, and simulations were performed to investigate the accuracy of the maximum likelihood estimators in the proposed regression model. The mathematical properties investigated provide important information about the characteristics of the distribution. Diagnostic analysis and deviance residuals are addressed to examine the fit of the model. The proposed model shows effectiveness in identifying the key variables of COVID-19 vaccination rates at the county level, which can contribute to improving vaccination campaigns. Moreover, the findings corroborate with prior studies, and the new distribution is a suitable alternative model for future works on different datasets.

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Section: Covid-19 Vaccination Rates On County-levelmentioning

confidence: 99%

Analyzing County-Level COVID-19 Vaccination Rates in Texas: A New Lindley Regression Model

da Costa,

de Lima,

Cordeiro

2023

COVID

View full text Add to dashboard Cite

show abstract

“…In Ref. [ 11 ], the authors noted that combining multiple deep learning methods and computational intelligence models demonstrates ongoing research into sophisticated technologies to gain a deeper understanding of and address the complexities of infectious diseases, particularly in COVID-19. In Ref.…”

Section: Introductionmentioning

confidence: 99%

A deep learning-based approach for predicting COVID-19 diagnosis

Munshi,

Khayyat,

Ben Slama

et al. 2024

Heliyon

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“…Oyewola et al [29] used some machine learning algorithms to determine COVID-19 vaccine acceptance in countries where residents were vaccinated. Also, Osman and Sabit [28] used some machine learning algorithms in determining vaccination rates among states in the United States of America.…”

Section: Introductionmentioning

confidence: 99%

Using machine learning algorithms to predict COVID-19 vaccine uptake: A year after the introduction of COVID-19 vaccines in Ghana

Dodoo,

Hanson-Yamoah,

Adedia

et al. 2024

Vaccine: X

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Predictors of COVID-19 vaccination rate in USA: A machine learning approach

Cited by 5 publications

References 37 publications

Analyzing County-Level COVID-19 Vaccination Rates in Texas: A New Lindley Regression Model

Analyzing County-Level COVID-19 Vaccination Rates in Texas: A New Lindley Regression Model

A deep learning-based approach for predicting COVID-19 diagnosis

Using machine learning algorithms to predict COVID-19 vaccine uptake: A year after the introduction of COVID-19 vaccines in Ghana

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