Side Effects and Perceptions Following COVID-19 Vaccination in Jordan: A Randomized, Cross-Sectional Study Implementing Machine Learning for Predicting Severity of Side Effects.
Side Effects and Perceptions Following COVID-19 Vaccination in Jordan: A Randomized, Cross-Sectional Study Implementing Machine Learning for Predicting Severity of Side Effects.
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DOI：
10.3390/vaccines9060556
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发表时间：
2021-05-26
期刊：
影响因子：
7.8
通讯作者：
Mohamud R
中科院分区：
文献类型：
--
作者：
Hatmal MM;Al-Hatamleh MAI;Olaimat AN;Hatmal M;Alhaj-Qasem DM;Olaimat TM;Mohamud R
背景：自从 2019 年冠状病毒病 (COVID-19) 被宣布为大流行病以来，毫无疑问，疫苗接种是应对该疾病的理想方案。一年之内，一些新冠肺炎 (COVID-19) 疫苗已被开发出来并获得授权。这种无与伦比的疫苗开发举措给这些疫苗的功效和安全性带来了许多不确定性。本研究旨在评估约旦接种 COVID-19 疫苗后的副作用和看法。方法：通过针对接种过 COVID-19 疫苗的约旦居民进行在线调查，开展了一项横断面研究。对数据进行统计分析，并使用某些机器学习 (ML) 工具（包括多层感知器 (MLP)、eXtreme 梯度提升 (XGBoost)、随机森林 (RF) 和 K-star）来预测副作用的严重程度。结果：共有 2213 名参与者在接受国药控股、阿斯利康、辉瑞 BioNTech 和其他疫苗后参与研究（分别为 38.2%、31%、27.3% 和 3.5%）。一般来说，大多数疫苗接种后副作用是常见且不危及生命的（例如疲劳、发冷、头晕、发烧、头痛、关节痛和肌痛）。只有 10% 的参与者出现了严重的副作用； 39% 和 21% 的参与者分别出现中度和轻度副作用。尽管这些疫苗在副作用的存在和严重程度方面存在很大差异，但统计分析表明这些疫苗可能提供相同的针对 COVID-19 感染的保护。最后，约52.9%的参与者在接种疫苗前对疫苗犹豫和焦虑；而在接种疫苗后，95.5%的参与者建议其他人接种疫苗，80%的人感到更加放心，67%的人认为从长远来看，COVID-19疫苗是安全的。此外，根据疫苗类型、人口统计数据和副作用，RF、XGBoost 和 MLP 给出了很高的准确度（分别为 0.80、0.79 和 0.70）和 Cohen 的 kappa 值（分别为 0.71、0.70 和 0.56）。结论：本研究证实，授权的 COVID-19 疫苗是安全的，接种疫苗让人们更放心。大多数疫苗接种后的副作用都是轻度到中度，这表明身体的免疫系统正在建立保护。 ML 还可用于根据输入数据预测副作用的严重程度；预测的严重病例可能需要更多的医疗护理甚至住院治疗。
Background: Since the coronavirus disease 2019 (COVID-19) was declared a pandemic, there was no doubt that vaccination is the ideal protocol to tackle it. Within a year, a few COVID-19 vaccines have been developed and authorized. This unparalleled initiative in developing vaccines created many uncertainties looming around the efficacy and safety of these vaccines. This study aimed to assess the side effects and perceptions following COVID-19 vaccination in Jordan. Methods: A cross-sectional study was conducted by distributing an online survey targeted toward Jordan inhabitants who received any COVID-19 vaccines. Data were statistically analyzed and certain machine learning (ML) tools, including multilayer perceptron (MLP), eXtreme gradient boosting (XGBoost), random forest (RF), and K-star were used to predict the severity of side effects. Results: A total of 2213 participants were involved in the study after receiving Sinopharm, AstraZeneca, Pfizer-BioNTech, and other vaccines (38.2%, 31%, 27.3%, and 3.5%, respectively). Generally, most of the post-vaccination side effects were common and non-life-threatening (e.g., fatigue, chills, dizziness, fever, headache, joint pain, and myalgia). Only 10% of participants suffered from severe side effects; while 39% and 21% of participants had moderate and mild side effects, respectively. Despite the substantial variations between these vaccines in the presence and severity of side effects, the statistical analysis indicated that these vaccines might provide the same protection against COVID-19 infection. Finally, around 52.9% of participants suffered before vaccination from vaccine hesitancy and anxiety; while after vaccination, 95.5% of participants have advised others to get vaccinated, 80% felt more reassured, and 67% believed that COVID-19 vaccines are safe in the long term. Furthermore, based on the type of vaccine, demographic data, and side effects, the RF, XGBoost, and MLP gave both high accuracies (0.80, 0.79, and 0.70, respectively) and Cohen’s kappa values (0.71, 0.70, and 0.56, respectively). Conclusions: The present study confirmed that the authorized COVID-19 vaccines are safe and getting vaccinated makes people more reassured. Most of the post-vaccination side effects are mild to moderate, which are signs that body’s immune system is building protection. ML can also be used to predict the severity of side effects based on the input data; predicted severe cases may require more medical attention or even hospitalization.
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DOI：
10.3390/molecules21080983
发表时间：
2016-07-28
期刊：
Molecules (Basel, Switzerland)
影响因子：
--
作者：
Babajide Mustapha I;Saeed F
通讯作者：
Saeed F
影响因子：
2.2
作者：
Abu-Farha RK;Alzoubi KH;Khabour OF
通讯作者：
Khabour OF
DOI：
10.1007/s42399-020-00521-8
发表时间：
2020
期刊：
SN comprehensive clinical medicine
影响因子：
--
作者：
Bhattacharjee S;Banerjee M
通讯作者：
Banerjee M
影响因子：
8.1
作者：
Lopez, Yury O. Nunez;Retnakaran, Ravi;Seyhan, Attila A.
通讯作者：
Seyhan, Attila A.
COVID-19-19感染和纳米医学在开发疫苗和治疗剂中的应用：基于聚合物体的概述和未来观点。
影响因子：
5
作者：
Al-Hatamleh MAI;Hatmal MM;Alshaer W;Rahman ENSEA;Mohd-Zahid MH;Alhaj-Qasem DM;Yean CY;Alias IZ;Jaafar J;Ferji K;Six JL;Uskoković V;Yabu H;Mohamud R
通讯作者：
Mohamud R