Using machine learning algorithms to predict COVID-19 vaccine uptake : A year after the introduction of COVID-19 vaccines in Ghana
©2024TheAuthors.PublishedbyElsevierLtd..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:18 |
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| Enthalten in: |
Vaccine: X - 18(2024) vom: 19. März, Seite 100466 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Dodoo, Cornelius C [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 vaccine |
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| Anmerkungen: |
Date Revised 07.03.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.jvacx.2024.100466 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369344820 |
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| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a Vaccine hesitancy | |
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| 700 | 1 | |a Adedia, David |e verfasserin |4 aut | |
| 700 | 1 | |a Erzuah, Irene |e verfasserin |4 aut | |
| 700 | 1 | |a Yamoah, Peter |e verfasserin |4 aut | |
| 700 | 1 | |a Brobbey, Fareeda |e verfasserin |4 aut | |
| 700 | 1 | |a Cobbold, Constance |e verfasserin |4 aut | |
| 700 | 1 | |a Mensah, Josephine |e verfasserin |4 aut | |
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