AIoT Used for COVID-19 Pandemic Prevention and Control.

The pandemic of COVID-19 is continuing to wreak havoc in 2021, with at least 170 million victims around the world. Healthcare systems are overwhelmed by the large-scale virus infection. Luckily, Internet of Things (IoT) is one of the most effective paradigms in the intelligent world, in which the technology of artificial intelligence (AI), like cloud computing and big data analysis, is playing a vital role in preventing the spread of the pandemic of COVID-19. AI and 5G technologies are advancing by leaps and bounds, further strengthening the intelligence and connectivity of IoT applications, and conventional IoT has been gradually upgraded to be more powerful AI + IoT (AIoT). For example, in terms of remote screening and diagnosis of COVID-19 patients, AI technology based on machine learning and deep learning has recently upgraded medical equipment significantly and has reshaped the workflow with minimal contact with patients, so medical specialists can make clinical decisions more efficiently, providing the best protection not only to patients but also to specialists themselves. This paper reviews the latest progress made in combating COVID-19 with both IoT and AI and also provides comprehensive details on how to combat the pandemic of COVID-19 as well as the technologies that may be applied in the future.

Relationship between Quality of Nursing Work Life and Uniformed Nurses' Attitudes and Practices Related to COVID-19 in the Philippines: A Cross-Sectional Study.

(1) Background: Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide. Uniformed nurses have played a critical role during the COVID-19 pandemic in the Philippines; however, uptake of literature is limited. This study assessed the relationship between quality of nursing work life (QNWL) and nurses' attitudes and practices during the COVID-19 pandemic. (2) Methods: A descriptive cross-sectional design was used. Participants were recruited from four government hospitals in the Manila metropolitan area of the Philippines. Participants completed three questionnaires in an online survey: a demographic questionnaire, a QNWL questionnaire, and the attitude and practices toward COVID-19 questionnaire. Descriptive statistics, an independent t-test, a one-way analysis of variance, the Pearson correlation coefficient, and hierarchical linear regression were applied for data analysis. (3) Results: The mean age of the participants was 29 years. Most of the participants were single women who were not certified in their specialties. A total of QNWL scores were high, indicating that the participants displayed favorable attitudes and practices in relation to COVID-19. A statistically significant relationship was observed between QNWL, specialty certification, and practices related to COVID-19. Practices related to COVID-19 were a significant predictor of QNWL and one of its subscales, work design. (4) Conclusion: Young adult uniformed nurses in the Philippines have assumed numerous responsibilities during the COVID-19 pandemic. Providing these frontline nurses with comprehensive specialized education and training is crucial.