Article ; Online: Using machine learning algorithms to predict COVID-19 vaccine uptake: A year after the introduction of COVID-19 vaccines in Ghana.
Vaccine: X
2024 Volume 18, Page(s) 100466
Abstract: The impact of vaccine hesitancy on global health is one that carries dire consequences. This was evident during the outbreak of the COVID-19 pandemic, where numerous theories and rumours emerged. To facilitate targeted actions aimed at increasing vaccine ...
Abstract | The impact of vaccine hesitancy on global health is one that carries dire consequences. This was evident during the outbreak of the COVID-19 pandemic, where numerous theories and rumours emerged. To facilitate targeted actions aimed at increasing vaccine acceptance, it is essential to identify and understand the barriers that hinder vaccine uptake, particularly regarding the COVID-19 vaccine in Ghana, one year after its introduction in the country. We conducted a cross-sectional study utilizing self-administered questionnaires to determine factors, including barriers, that predict COVID-19 vaccine uptake among clients visiting a tertiary and quaternary hospital using some machine learning algorithms. Among the findings, machine learning models were developed and compared, with the best model employed to predict and guide interventions tailored to specific populations and contexts. A random forest model was utilized for prediction, revealing that the type of facility respondents visited and the presence of underlying medical conditions were significant factors in determining an individual's likelihood of receiving the COVID-19 vaccine. The results showed that machine learning algorithms can be of great use in determining COVID-19 vaccine uptake. |
---|---|
Language | English |
Publishing date | 2024-02-27 |
Publishing country | England |
Document type | Journal Article |
ISSN | 2590-1362 |
ISSN (online) | 2590-1362 |
DOI | 10.1016/j.jvacx.2024.100466 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
More links
Kategorien
Order via subito
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.
Inter-library loan at ZB MED
Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.