Awareness of the epidemiological investigation tasks of the nurse in charge of COVID-19 epidemiological investigations

Purpose: This qualitative study was to understand the awareness of epidemiological investigation tasks for nurses who were in charge of coronavirus disease 2019 (COVID-19) epidemiological investigations. Methods: Before data collection, written consent was obtained from 13 participants, and the data were then collected from September 1 to December 31, 2021. Individual interviews were conducted and recorded by video interview using Zoom, and data were transcribed verbatim. Four themes were derived by using the qualitative thematic analysis method. Results: The participants perceived that epidemiological investigations were burdensome but that the field work was important, and that expertise and collaboration were required. The participants started work without preparation due to the explosive increase in the number of confirmed COVID-19 cases, and they recognized work conflicts, unstable employment, and exhaustion as obstacles to their work performance. On the other hand, the participants took pride in contributing to the national epidemiological investigation and control and felt a sense of responsibility as nursing professionals. Finally, participants mentioned that the training of infectious disease practitioner was important for work improvement. Conclusion: Further research is needed on the development of standardized manuals for the training of nursing personnel as infectious disease specialists through the job analysis of epidemiological investigators. Copyright ©2022 The Korean Academic Society of Nursing Education.

A Novel Approach to Detect and Monitor COVID-19 Infection Using Transfer Learning Concept in AI

COVID-19 is an ongoing pandemic that is continuing to spread after recording one hundred million cases, causing millions of casualties, overwhelming health care systems of many countries, and threatening the whole world. Monitoring and assessing the severity of COVID-19 through artificial intelligence would be a practical support for medical practitioners reviving patients and offloading the burden from medical system. Previous works exploited deep learning, for this purpose, which produces inexplainable diagnosis results and lacks medical evidence. Integrating clinical symptom into diagnosis with deep learning will support generating results more compelled and validated. In this study, we focus on verifying the effectiveness of applying the human lung lesion, specifically Ground Glass Opacity and Consolidation, caused by typical pneumonia for COVID-19 detection or severity assessment on chest X-ray image with deep learning technology. We have conducted multiple experiments with state-of-art machine learning architectures (MobileNetV2, ResNet, Faster R-CNN) on many datasets to establish the conclusion. The experiment result demonstrates that lung lesion is useful when incorporating with deep learning solutions for monitoring COVID-19 progression and will provide solid pathway to develop an improved model and support better research in the future. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.