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

Ma'mon M Hatmal; Mohammad A I Al-Hatamleh; Amin N Olaimat; Rohimah Mohamud; Mirna Fawaz; Elham T Kateeb; Omar K Alkhairy; Reema Tayyem; Mohamed Lounis; Marwan Al-Raeei; Rasheed K Dana; Hamzeh J Al-Ameer; Mutasem O Taha; Khalid M Bindayna

doi:10.3390/vaccines10030366

Back

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

Journal article

Open access

Peer reviewed

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

Ma'mon M Hatmal, Mohammad A I Al-Hatamleh, Amin N Olaimat, Rohimah Mohamud, Mirna Fawaz, Elham T Kateeb, Omar K Alkhairy, Reema Tayyem, Mohamed Lounis, Marwan Al-Raeei, …

Vaccines (Basel), Vol.10(3), p.366

02/26/2022

DOI: 10.3390/vaccines10030366

PMCID: PMC8955470

PMID: 35334998

Files and links (1)

url

https://doi.org/10.3390/vaccines10030366View

Published (Version of record) Open Access

Abstract

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. 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. 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 ( < 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. 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.

vaccine hesitancy

vaccine safety

SARS-CoV-2

vaccines

adverse reactions

coronavirus

side effects

nCoV-2019

Details

Title: Subtitle: 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
Creators: Ma'mon M Hatmal - Hashemite University
Mohammad A I Al-Hatamleh - Universiti Sains Malaysia
Amin N Olaimat - Hashemite University
Rohimah Mohamud - Universiti Sains Malaysia
Mirna Fawaz - Beirut Arab University
Elham T Kateeb - Al-Quds University
Omar K Alkhairy - King Abdullah International Medical Research Center
Reema Tayyem - Qatar University
Mohamed Lounis - Department of Agro-Veterinary Science, Faculty of Natural and Life Sciences, University of Ziane Achour, BP 3117, Djelfa 17000, Algeria
Marwan Al-Raeei - Damascus University
Rasheed K Dana - Mansoura University
Hamzeh J Al-Ameer - American University of Madaba
Mutasem O Taha - University of Jordan
Khalid M Bindayna - Arabian Gulf University
Resource Type: Journal article
Publication Details: Vaccines (Basel), Vol.10(3), p.366
DOI: 10.3390/vaccines10030366
PMID: 35334998
PMCID: PMC8955470
NLM abbreviation: Vaccines (Basel)
ISSN: 2076-393X
eISSN: 2076-393X
Language: English
Date published: 02/26/2022
Academic Unit: Public Policy Center (Archive)
Record Identifier: 9984283859202771

Metrics

17 Record Views

44 Times Cited - Web of Science