Ahmad Alshehri

Despite the effectiveness of current vaccines in reducing the spread and severity of SARS-CoV-2 infections, many people, including migrants, refugees, and foreign workers, are hesitant to be vaccinated. This systematic review and meta-analysis (SRMA) was conducted to determine the pooled prevalence estimate of the acceptance and hesitancy rates of the COVID-19 vaccine among these populations. A comprehensive search of the peer-reviewed literature indexed in PubMed, Scopus, Science Direct, and Web of Science databases was conducted. Initially, 797 potential records were identified, of which 19 articles met the inclusion criteria. A meta-analysis of proportions using data from 14 studies revealed that the overall acceptance rate of COVID vaccination among 29,152 subjects was 56.7% (95% CI: 44.9–68.5%), while the prevalence of vaccine hesitancy among 26,154 migrants reported in 12 studies was estimated to be 31.7% (95% CI: 44.9–68.5%). The acceptance rate for the COVID-19 vaccination first declined from 77.3% in 2020 to 52.9% in 2021 and then slightly increased to 56.1% in 2022. The most frequent factors influencing vaccine hesitancy were worries about vaccine efficacy and safety. Intensive vaccination campaigns should be implemented to raise vaccination awareness among migrants, which will increase the acceptance rate for the COVID-19 vaccine and result in herd immunity.

SARS-CoV-2 is a recently discovered virus that poses an urgent threat to global health. The disease caused by this virus is termed COVID-19. Death tolls in different countries remain to rise, leading to continuous social distancing and lockdowns. Patients of different ages are susceptible to severe disease, in particular those who have been admitted to an ICU. Machine learning (ML) predictive models based on medical data patterns are an emerging topic in areas such as the prediction of liver diseases. Prediction models that combine several variables or features to estimate the risk of people being infected or experiencing a poor outcome from infection could assist medical staff in the treatment of patients, especially those that develop organ failure such as that of the liver. In this paper, we propose a model called the detecting model for liver damage (DMLD) that predicts the risk of liver damage in COVID-19 ICU patients. The DMLD model applies machine learning algorithms in order to assess the risk of liver failure based on patient data. To assess the DMLD model, collected data were preprocessed and used as input for several classifiers. SVM, decision tree (DT), Naïve Bayes (NB), KNN, and ANN classifiers were tested for performance. SVM and DT performed the best in terms of predicting illness severity based on laboratory testing.