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. 2022 Feb 26;10(3):366.
doi: 10.3390/vaccines10030366.

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  1 , Mohammad A I Al-Hatamleh  2 , Amin N Olaimat  3 , Rohimah Mohamud  2 , Mirna Fawaz  4 , Elham T Kateeb  5 , Omar K Alkhairy  6   7   8 , Reema Tayyem  9 , Mohamed Lounis  10 , Marwan Al-Raeei  11 , Rasheed K Dana  12 , Hamzeh J Al-Ameer  13 , Mutasem O Taha  14 , Khalid M Bindayna  15
Affiliations

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 et al. Vaccines (Basel). .

Abstract

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.

Keywords: SARS-CoV-2; adverse reactions; coronavirus; nCoV-2019; side effects; vaccine hesitancy; vaccine safety; vaccines.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Participants’ health status indicators and their perceptions towards COVID-19 vaccines before receiving a COVID-19 vaccine. Chart (A) represents the most common chronic diseases that were reported by participants. (B–E) show proportions of participants who are smokers, have food and/or drug allergies, had experienced COVID-19 infection, had experienced COVID-19 vaccine hesitancy and related fears, respectively. (F) shows frequencies of COVID-19 vaccines preferred by participants, while (G) shows the credible sources of information about COVID-19 vaccines among them.
Figure 2
Figure 2
Participants’ post-vaccination information. (A) Interval between receiving a COVID-19 vaccine and participating in this study (n = 10,064). (B) Time of COVID-19 vaccine breakthrough infection (n = 471). (C) Characterization of participants who experienced COVID-19 vaccine breakthrough infections based on the type of vaccine and number of doses (n = 471; 4.7%). * The perception was calculated out of the total number of participants who experienced vaccine breakthrough infection (n = 471); ** the perception was calculated out of the total number of participants who received the vaccine (Table 3).
Figure 3
Figure 3
Severity of side effects following COVID-19 vaccination.
Figure 4
Figure 4
Side effects of COVID-19 vaccines. (A), the most common post-vaccination side effects; (B), interval between receiving a COVID-19 vaccine and experiencing side effects; (C), duration of post-vaccination side effects; (D), coping responses to post-vaccination side effects.
Figure 5
Figure 5
Participants’ responses to belief-based questions after COVID-19 vaccination.
See this image and copyright information in PMC

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