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 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 June 14 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

Exploring the evidence base for national and regional policy interventions to combat resistance

The effectiveness of existing policies to control antimicrobial resistance is not yet fully understood. A strengthened evidence base is needed to inform effective policy interventions across countries with different income levels and the human health and animal sectors. We examine three policy domains—responsible use, surveillance, and infection prevention and control—and consider which will be the most effective at national and regional levels. Many complexities exist in the implementation of such policies across sectors and in varying political and regulatory environments. Therefore, we make recommendations for policy action, calling for comprehensive policy assessments, using standardised frameworks, of cost-effectiveness and generalisability. Such assessments are especially important in low-income and middle-income countries, and in the animal and environmental sectors. We also advocate a One Health approach that will enable the development of sensitive policies, accommodating the needs of each sector involved, and addressing concerns of specific countries and regions

Arabia

These authors contributed equally to this work Background: To assess the prevalence of extended spectrum beta-lactamase (ESBL) producing Escherichia coli and Klebsiella strains in nosocomial and community-acquired infections. Methodology: The study was conducted at a centralized microbiology laboratory in the Eastern Province of Saudi Arabia. Laboratory records (January 2004- December 2005) were assessed. Associated resistance to a panel of antibiotics was determined. Results: A total of 6,750 Gram-negative organisms were assessed for ESBL-phenotype. ESBL was detected in 6 % (409/6,750) of isolates, the majority of which were E. coli (83%). ESBL producers were significantly higher among isolates from in-patients 15.4 % (143/927) versus out-patients (4.5%; 266/5,823); p &lt; 0.05. Old age (older than 60 years) represented a significant risk for having an ESBL-producing pathogen. Urine was the major source of ESBL isolates in in-patients (46.1%) and out-patients (74.4%). The proportion of urinary E. coli isolates which were ESBL producers was significantly higher among in-patients (53/506; 10.4%) compared to out-patients (182/4,074; 4.4%); p &lt; 0.05. Among in-patients, 60 % of the ESBL associated infections were nosocomial. All were sensitive to imipenem but high levels of resistance to gentamicin, amikacin, amoxicillin-clavulanic acid and ciprofloxacin was shown. Conclusion: The findings document evidence of the spread of multiresistant ESBL-producers into the community. This has significan