ABSTRACT
The coronavirus (COVID-19) as in the study of which had a starting point in China in 2019, has spread rapidly in every single country and has spread in millions of cases. The pandemic attracts lots of attentions due to major impacts not only on human health but on many other aspects including, social and political ones. This paper presents a robust data-driven machine learning analysis of COVID19 starting from data collection to the final step of knowledge extraction based on the selected research topics. The proposed approach evaluates the impact of social distancing on COVID19. Several machine learning and ensemble models have been used and compared to obtain the best accuracy. Experiments have been demonstrated on large public datasets. The motivation of this study is to propose an analytical machine learning based model to explore the social distancing aspects of COVID-19 pandemic. The proposed analytical model includes classic classifiers, distinctive ensemble methods such as bagging, feature based ensemble, voting and stacking. Also, it uses different Python libraries, Rattle, RStudio, Anaconda, and Jupyter Notebook. This study shows superior prediction performance comparing with the related approaches and the classical machine learning approaches. © 2022 IEEE.
ABSTRACT
Many healthcare workers received inadequate infection prevention and control training while facing shortages of personal protective equipment. Community exposure, however, is often overlooked when exposure assessment of infection risk is applied. The overall level of exposure of healthcare workers in Kuwait remains understudied. This study aimed at identifying and quantifying the risks of COVID-19 exposure to healthcare workers using the WHO-developed risk assessment tool before an in-house online training. A cross-sectional survey was conducted in July 2020. Healthcare workers from governmental hospitals in Kuwait were recruited by convenience due to lockdowns. The recruited individuals were offered in-house comprehensive online training. Out of 115 healthcare workers, 68.7% were female, 47.8% were aged 31-40 and 44.4% were doctors. Community exposure was identified in 53% of participants. About 80.8% were considered highly exposed to COVID-19. During interactions with patients, 95.5% wore N95 masks and 59.5% wore face shields ‘always as recommended’ and only 11.8% wore gloves ‘most of the time’. Removal of PPEs according to IPC protocol was reported by 72.2% of healthcare workers. Healthcare workers in Kuwait face an extra risk of exposure to COVID-19 from the community, not only from healthcare facilities. The causes of the suboptimal level of adherence to PPEs warrants further investigation. The COVID-19 pandemic has illuminated the significant additive biohazard risk healthcare workers face from the community. Many countries including Kuwait are following through on the International Labor Organization and the World Health Organization recommendation to establish an occupational health and safety program for healthcare workers. © 2021 Teikyo University School of Medicine. All rights reserved.