Yipeng Zhang, Lawrence Liu, Yuanyi Ding, Xin Chen, Tonmoy Monsoor, Atsuro Daida, Shingo Oana, Shaun Hussain, Raman Sankar, Aria Fallah, Cesar Santana-Gomez, Jerome Engel, Richard J Staba, William Speier, Jianguo Zhang, Hiroki Nariai, Vwani Roychowdhury
PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application
Journal of neural engineering
study aimed analyze coronavirus disease 2019 covid19 infodemic phenomenon medical field providing essential data help healthcare professionals understand study utilized hybrid model concept analysis theoretical phase first phase literature review conducted using sciencedirect pubmed cinahl proquest scopus web science dbpia riss kiss semistructured interviews involving eight physicians six nurses used fieldwork phase second phase final analysis phase third phase results preceding phases combined based findings phases covid19 infodemic defined phenomenon information flood reproduction dissemination asymmetry occurred pandemic social networks among public lacking essential knowledge infectious disease associated negative positive effects findings help ministry health welfare healthcare professionals understand phenomenon infodemic prepare necessary strategies education programs public therefore provision basic data important developing influential roles healthcare professionals infectious disease outbreaks