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Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors

Background: The unprecedented global spread of coronavirus disease 2019 (COVID-19) has imposed huge challenges on the healthcare facilities, and impacted every aspect of life. This has led to the development of several vaccines against COVID-19 within one year. This study aimed to assess the attitud...

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Autores principales: Hatmal, Ma’mon M., Al-Hatamleh, Mohammad A. I., Olaimat, Amin N., Mohamud, Rohimah, Fawaz, Mirna, Kateeb, Elham T., Alkhairy, Omar K., Tayyem, Reema, Lounis, Mohamed, Al-Raeei, Marwan, Dana, Rasheed K., Al-Ameer, Hamzeh J., Taha, Mutasem O., Bindayna, Khalid M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8955470/
https://www.ncbi.nlm.nih.gov/pubmed/35334998
http://dx.doi.org/10.3390/vaccines10030366
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author Hatmal, Ma’mon M.
Al-Hatamleh, Mohammad A. I.
Olaimat, Amin N.
Mohamud, Rohimah
Fawaz, Mirna
Kateeb, Elham T.
Alkhairy, Omar K.
Tayyem, Reema
Lounis, Mohamed
Al-Raeei, Marwan
Dana, Rasheed K.
Al-Ameer, Hamzeh J.
Taha, Mutasem O.
Bindayna, Khalid M.
author_facet Hatmal, Ma’mon M.
Al-Hatamleh, Mohammad A. I.
Olaimat, Amin N.
Mohamud, Rohimah
Fawaz, Mirna
Kateeb, Elham T.
Alkhairy, Omar K.
Tayyem, Reema
Lounis, Mohamed
Al-Raeei, Marwan
Dana, Rasheed K.
Al-Ameer, Hamzeh J.
Taha, Mutasem O.
Bindayna, Khalid M.
author_sort Hatmal, Ma’mon M.
collection PubMed
description Background: The unprecedented global spread of coronavirus disease 2019 (COVID-19) has imposed huge challenges on the healthcare facilities, and impacted every aspect of life. This has led to the development of several vaccines against COVID-19 within one year. This study aimed to assess the attitudes and the side effects among Arab communities after receiving a COVID-19 vaccine and use of machine learning (ML) tools to predict post-vaccination side effects based on predisposing factors. Methods: An online-based multinational survey was carried out via social media platforms from 14 June to 31 August 2021, targeting individuals who received at least one dose of a COVID-19 vaccine from 22 Arab countries. Descriptive statistics, correlation, and chi-square tests were used to analyze the data. Moreover, extensive ML tools were utilized to predict 30 post vaccination adverse effects and their severity based on 15 predisposing factors. The importance of distinct predisposing factors in predicting particular side effects was determined using global feature importance employing gradient boost as AutoML. Results: A total of 10,064 participants from 19 Arab countries were included in this study. Around 56% were female and 59% were aged from 20 to 39 years old. A high rate of vaccine hesitancy (51%) was reported among participants. Almost 88% of the participants were vaccinated with one of three COVID-19 vaccines, including Pfizer-BioNTech (52.8%), AstraZeneca (20.7%), and Sinopharm (14.2%). About 72% of participants experienced post-vaccination side effects. This study reports statistically significant associations (p < 0.01) between various predisposing factors and post-vaccinations side effects. In terms of predicting post-vaccination side effects, gradient boost, random forest, and XGBoost outperformed other ML methods. The most important predisposing factors for predicting certain side effects (i.e., tiredness, fever, headache, injection site pain and swelling, myalgia, and sleepiness and laziness) were revealed to be the number of doses, gender, type of vaccine, age, and hesitancy to receive a COVID-19 vaccine. Conclusions: The reported side effects following COVID-19 vaccination among Arab populations are usually non-life-threatening; flu-like symptoms and injection site pain. Certain predisposing factors have greater weight and importance as input data in predicting post-vaccination side effects. Based on the most significant input data, ML can also be used to predict these side effects; people with certain predicted side effects may require additional medical attention, or possibly hospitalization.
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spelling pubmed-89554702022-03-26 Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors Hatmal, Ma’mon M. Al-Hatamleh, Mohammad A. I. Olaimat, Amin N. Mohamud, Rohimah Fawaz, Mirna Kateeb, Elham T. Alkhairy, Omar K. Tayyem, Reema Lounis, Mohamed Al-Raeei, Marwan Dana, Rasheed K. Al-Ameer, Hamzeh J. Taha, Mutasem O. Bindayna, Khalid M. Vaccines (Basel) Article Background: The unprecedented global spread of coronavirus disease 2019 (COVID-19) has imposed huge challenges on the healthcare facilities, and impacted every aspect of life. This has led to the development of several vaccines against COVID-19 within one year. This study aimed to assess the attitudes and the side effects among Arab communities after receiving a COVID-19 vaccine and use of machine learning (ML) tools to predict post-vaccination side effects based on predisposing factors. Methods: An online-based multinational survey was carried out via social media platforms from 14 June to 31 August 2021, targeting individuals who received at least one dose of a COVID-19 vaccine from 22 Arab countries. Descriptive statistics, correlation, and chi-square tests were used to analyze the data. Moreover, extensive ML tools were utilized to predict 30 post vaccination adverse effects and their severity based on 15 predisposing factors. The importance of distinct predisposing factors in predicting particular side effects was determined using global feature importance employing gradient boost as AutoML. Results: A total of 10,064 participants from 19 Arab countries were included in this study. Around 56% were female and 59% were aged from 20 to 39 years old. A high rate of vaccine hesitancy (51%) was reported among participants. Almost 88% of the participants were vaccinated with one of three COVID-19 vaccines, including Pfizer-BioNTech (52.8%), AstraZeneca (20.7%), and Sinopharm (14.2%). About 72% of participants experienced post-vaccination side effects. This study reports statistically significant associations (p < 0.01) between various predisposing factors and post-vaccinations side effects. In terms of predicting post-vaccination side effects, gradient boost, random forest, and XGBoost outperformed other ML methods. The most important predisposing factors for predicting certain side effects (i.e., tiredness, fever, headache, injection site pain and swelling, myalgia, and sleepiness and laziness) were revealed to be the number of doses, gender, type of vaccine, age, and hesitancy to receive a COVID-19 vaccine. Conclusions: The reported side effects following COVID-19 vaccination among Arab populations are usually non-life-threatening; flu-like symptoms and injection site pain. Certain predisposing factors have greater weight and importance as input data in predicting post-vaccination side effects. Based on the most significant input data, ML can also be used to predict these side effects; people with certain predicted side effects may require additional medical attention, or possibly hospitalization. MDPI 2022-02-26 /pmc/articles/PMC8955470/ /pubmed/35334998 http://dx.doi.org/10.3390/vaccines10030366 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hatmal, Ma’mon M.
Al-Hatamleh, Mohammad A. I.
Olaimat, Amin N.
Mohamud, Rohimah
Fawaz, Mirna
Kateeb, Elham T.
Alkhairy, Omar K.
Tayyem, Reema
Lounis, Mohamed
Al-Raeei, Marwan
Dana, Rasheed K.
Al-Ameer, Hamzeh J.
Taha, Mutasem O.
Bindayna, Khalid M.
Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors
title Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors
title_full Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors
title_fullStr Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors
title_full_unstemmed Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors
title_short Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors
title_sort reported adverse effects and attitudes among arab populations following covid-19 vaccination: a large-scale multinational study implementing machine learning tools in predicting post-vaccination adverse effects based on predisposing factors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8955470/
https://www.ncbi.nlm.nih.gov/pubmed/35334998
http://dx.doi.org/10.3390/vaccines10030366
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