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Journal of Web Engineering ; 21(5):1419-1433, 2022.
Article in English | Web of Science | ID: covidwho-1998051


To fix network congestion resulting from the increase in high volume traffic in data-intensive science and the increase in internet traffic due to COVID19, there has been a necessity of traffic engineering through traffic prediction. For this, there have been various attempts from a statistical method such as ARIMA to machine learning including LSTM and GRU. This study aimed to collect and learn KREOENT backbone and subscribers' traffic volume through diverse machine learning techniques (e.g., SVR, LSTM, GRU, etc.) and predict maximum traffic on the following day.

International Journal of Networked and Distributed Computing ; 9(1):59-74, 2021.
Article in English | Scopus | ID: covidwho-1219466


As COVID-19 enters the pandemic stage, the resulting infections, deaths and economic shocks are emerging. To minimize anxiety and uncertainty about socio-economic damage caused by the COVID-19 pandemic, it is necessary to reasonably predict the economic impact of future disease trends by scientific means. Based on previous cases of epidemic (such as influenza) and economic trends, this study has established an epidemic disease spread model and economic situation prediction model. Based on this model, the author also predict the economic impact of future COVID-19 spread. The results of this study are as follows. First, the deep learning-based economic impact prediction model, which was built based on historical infectious disease data, was verified with verification data to ensure 77% accuracy in predicting inflation rates. Second, based on the economic impact prediction model of the deep learning-based infectious disease, the author presented the COVID-19 trend and future economic impact prediction results for the next 1 year. Currently, most of the published studies on COVID-19 are on the prediction of disease spread by statistical mathematical calculations. This study is expected to be used as an empirical reference to efficient and preemptive decision making by predicting the spread of diseases and economic conditions related to COVID-19 using deep learning technology and historical infectious disease data. © 2021 The Authors. Published by Atlantis Press B.V.

J Hosp Infect ; 106(3): 570-576, 2020 Nov.
Article in English | MEDLINE | ID: covidwho-723894


BACKGROUND: Identifying the extent of environmental contamination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for infection control and prevention. The extent of environmental contamination has not been fully investigated in the context of severe coronavirus disease (COVID-19) patients. AIM: To investigate environmental SARS-CoV-2 contamination in the isolation rooms of severe COVID-19 patients requiring mechanical ventilation or high-flow oxygen therapy. METHODS: Environmental swab samples and air samples were collected from the isolation rooms of three COVID-19 patients with severe pneumonia. Patients 1 and 2 received mechanical ventilation with a closed suction system, while patient 3 received high-flow oxygen therapy and non-invasive ventilation. Real-time reverse transcription-polymerase chain reaction (rRT-PCR) was used to detect SARS-CoV-2; viral cultures were performed for samples not negative on rRT-PCR. FINDINGS: Of the 48 swab samples collected in the rooms of patients 1 and 2, only samples from the outside surfaces of the endotracheal tubes tested positive for SARS-CoV-2 by rRT-PCR. However, in patient 3's room, 13 of the 28 environmental samples (fomites, fixed structures, and ventilation exit on the ceiling) showed positive results. Air samples were negative for SARS-CoV-2. Viable viruses were identified on the surface of the endotracheal tube of patient 1 and seven sites in patient 3's room. CONCLUSION: Environmental contamination of SARS-CoV-2 may be a route of viral transmission. However, it might be minimized when patients receive mechanical ventilation with a closed suction system. These findings can provide evidence for guidelines for the safe use of personal protective equipment.

Coronavirus Infections/therapy , Decontamination/standards , Environmental Pollution/analysis , Hyperbaric Oxygenation/standards , Patients' Rooms/standards , Pneumonia, Viral/therapy , Pneumonia/therapy , Practice Guidelines as Topic , Respiration, Artificial/standards , Air Microbiology , COVID-19 , Humans , Pandemics