• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.93-102
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• 2021

The world is facing an unprecedented economic, social and political crisis with the spread of COVID-19. The Corona Virus (COVID-19) and its global spread have resulted in declaring a pandemic by the World Health Organization. The deadly pandemic of 21st century has spread its wings across the globe with an exponential increase in the number of cases in many countries. The developing and underdeveloped countries are struggling hard to counter the rapidly growing and widespread challenge of COVID-19 because it has greatly influenced the global economies whereby the underdeveloped countries are more affected by its devastating impacts, especially the life of the low-income population. Information and Communication Technology (ICT) were particularly useful in spreading key emergency information and helping to maintain extensive social distancing. Updated information and testing results were published on national and local government websites. Mobile devices were used to support early testing and contact tracing. The government provided free smartphone apps that flagged infection hotspots with text alerts on testing and local cases. The purpose of this research work is to provide an in depth overview of emerging technologies and recent ICT developments to combat COVID-19 Pandemic. Finally, the author highlights open challenges in order to give future research directions.

• Asia Pacific Journal of Corpus Research
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• v.1 no.1
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• pp.1-30
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• 2020

The present study examines language used to talk about the global coronavirus pandemic during a three-month period of movement control order in Malaysia. More specifically, a corpus of online letters to the editor of a local popular national newspaper was collected during the time in which the official quarantine instruction was initiated, resulting in a total of 303 online letters written by Malaysians that were analyzed through use of corpus linguistics techniques. For this purpose, the latest version of #LancsBox 5.0 (Brezina et al., 2020) is used to analyze patterns of language surrounding the portrayal of Covid-19 and further visualizing them by use of collocation networks. Findings present 25 statistically significant collocates that share an interesting relationship in revealing what the letters are about and thus, reflecting how Malaysians perceive and receive news about the pandemic during this time. Recurring topics and expressions include describing the virus in terms of metaphorical use of language (Covid-19 does not discriminate), preparing for an economic fallout (Prihatin Economic Stimulus Package), and preference to associate Covid-19 as a pandemic (impacts of the Covid19 pandemic) rather than an outbreak (first/second/third wave of the outbreak). Implications of the study resonates with findings from Azizan et al. (2020) where constructions of positive discourse among Malaysian writers may reflect the culture and society that make up the nation.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.225-233
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• 2022

The recent outbreak of corona virus (COVID-19) infectious disease had made its forecasting critical cornerstones in most scientific studies. This study adopts a machine learning based time series model - Auto Regressive Integrated Moving Average (ARIMA) model to forecast COVID-19 confirmed cases in Botswana over 60 days period. Findings of the study show that COVID-19 confirmed cases in Botswana are steadily rising in a steep upward trend with random fluctuations. This trend can also be described effectively using an additive model when scrutinized in Seasonal Trend Decomposition method by Loess. In selecting the best fit ARIMA model, a Grid Search Algorithm was developed with python language and was used to optimize an Akaike Information Criterion (AIC) metric. The best fit ARIMA model was determined at ARIMA (5, 1, 1), which depicted the least AIC score of 3885.091. Results of the study proved that ARIMA model can be useful in generating reliable and volatile forecasts that can used to guide on understanding of the future spread of infectious diseases or pandemics. Most significantly, findings of the study are expected to raise social awareness to disease monitoring institutions and government regulatory bodies where it can be used to support strategic health decisions and initiate policy improvement for better management of the COVID-19 pandemic.

• IMMUNE NETWORK
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• v.22 no.3
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• pp.23.1-23.12
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• 2022

Natural infection with severe acute respiratory syndrome-coronavirus-2 or vaccination induces virus-specific immunity protecting hosts from infection and severe disease. While the infection-preventing immunity gradually declines, the severity-reducing immunity is relatively well preserved. Here, based on the different longevity of these distinct immunities, we develop a mathematical model to estimate courses of endemic transition of coronavirus disease 2019 (COVID-19). Our analysis demonstrates that high viral transmission unexpectedly reduces the rates of progression to severe COVID-19 during the course of endemic transition despite increased numbers of infection cases. Our study also shows that high viral transmission amongst populations with high vaccination coverages paradoxically accelerates the endemic transition of COVID-19 with reduced numbers of severe cases. These results provide critical insights for driving public health policies in the era of 'living with COVID-19.'

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.30 no.3
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• pp.256-269
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• 2020

Objectives: Everyone has been affected in some way by the COVID-19 pandemic, while some workers face threats to their health due to their jobs as well as worries about spreading the virus to intimate family members. With these concerns in mind, this study aims to identify occupations more vulnerable to COVID-19 and suggests the necessity of providing proper control measures against the risks in a timely manner in Korea. Methods: Daily briefing reports by the Korea Centers for Disease Control and Prevention (KCDC) were compiled (n=120) between January 20 and May 31, 2020. A total of 11,486 confirmed cases were included, which were sorted by occasion, area, and occupation. Among them, 2,411 cases were classified with specific occasions and/or areas of infection, while only 544 cases were determined with identifiable occupations. Guidelines for biological hazard management and COVID-19 guidelines for workplaces from domestic and international bodies were enlisted and compared. Results: It is unsurprising to find that healthcare professionals are experiencing the most immediate threat from COVID-19. In addition, service workers with face-to-face practices or indirect contact are also facing high risks. Religion facilities and eating places (dining, drinking bar, café, etc.,) follow. Guidelines and manuals for biological hazards are still lacking in Korea compared to the US and EU. Workplace manuals for managing COVID-19 are neither as comprehensive as the approaches of NIOSH's hierarchy of controls nor inclusive enough for minimizing secondary or tertiary suffering. Conclusions: The COVID-19 crisis is still ongoing and there is no doubt there will be more such events in the future. This analysis suggests that occupational health professionals, amid a pandemic including COVID-19, are urged to anticipate emerging risks related to all sorts of occupations, identify vulnerable workers and working environments, and plan and take actions to protect workers' health.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.251-258
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• 2021

In 2019, a novel coronavirus (COVID-19) outbreak started in China and spread all over the world. The countries went into lockdown and closed their borders to minimize the spread of the virus. Shortage of testing kits and trained clinicians, motivate researchers and computer scientists to look for ways to automatically diagnose the COVID-19 patient using X-ray and ease the burden on the healthcare system. In recent years, multiple frameworks are presented but most of them are trained on a very small dataset which makes clinicians adamant to use it. In this paper, we have presented a lightweight deep learning base automatic COVID-19 detection system. We trained our model on more than 22,000 dataset X-ray samples. The proposed model achieved an overall accuracy of 96.88% with a sensitivity of 91.55%.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.365-373
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• 2021

Covid-19 posed a serious threat to public health worldwide, especially in the absence of vaccines or medicines. The only viable strategies to combat a virus with a high infection rate were to apply lock-down strategies, transport ban, social and physical distancing. In this work, we provide a domain-specific component model for crisis management. The model allows for building a plan for managing Covid-19 crisis and use the plan as a template to generate a system specific for managing that crisis. The crisis component model is derived from X-MAN II, a generic component model that we have developed for the aircraft industry

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.107-115
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• 2023

This study analyzes the content of YouTube Vlog videos created by patients of Coronavirus disease 2019 ("COVID-19") in South Korea and viewer comments on those videos. As this new infectious disease started to sweep the world in late 2019 and early 2020, the public started facing fear and uncertainty stemming from the lack of sufficient and accurate information about the virus. At the same time, as COVID-19 patients in South Korea were treated in isolation to prevent the spread of the virus, the patients themselves were experiencing anxiety and exclusion from the society. During this period, there was an increase in YouTube Vlog videos created by the patients in which they shared their experiences going through the treatment and recovery processes. To understand how these YouTube Vlog videos were being used by the patients to connect with the society and seek support in a state of isolation and anxiety, this study conducted a qualitative multi-case analysis of three sample YouTube Vlog video channels to analyze their content, as well as a lexicon-based sentiment analysis of viewer comments to understand the experiences and reactions of viewers. The patients' YouTube Vlog videos showed that they shared similar stages of progress, despite each emphasizing a different main theme. Overall, the tone of the viewer comments became increasingly positive over time, although with some variance among different patient cases and stages. The results confirmed that Vlogs of patients played a significant role in reducing the uncertainty around COVID-19 and strengthening social support for the patients. The findings of this study can improve an understanding of the psychological and behavioral aspects of patient experience in isolated treatment and the impact of shared communication among members of society in times of crisis.

• Journal of Digital Convergence
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• v.18 no.6
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• pp.479-493
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• 2020

Outbreak of COVID-19 originated from China resulted significantly high casualties and social and economic damages. Currently the major countries see importance of accurate prediction of originating trend to prevent the spread of infectious disease and AI is actively utilized when establishing the system. Therefore this study has comprehended the status of utilizing the AI in overseas and made comparison and analysis with domestic status. It derived the necessity to establish national control tower based on One Health to respond to infectious disease to effectively utilize AI and suggested to establish higher organization, Medical Big Data Governance, to respond to the infectious disease. It is necessary to conduct further study to utilize the results and suggestions derived from this study into the policy and if the suggestions are reflected to improve institutional imperfection, it will be positively used for prevention of the spreading infectious disease and utilizing medical Big Data.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.539-545
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• 2020

This study was designed to find out the efficacy of college students' lecture participation in online real-time lectures conducted at university due to the Corona virus events. Participants in this study are 701 students attending the W University in the J region and participating in real-time online lectures. The tools of this study consisted of four factors: interaction, learning improvement, adaptation, and accessibility. The results of this study are as follows: First, accessibility is the highest efficacy among college students' real-time online lectures participation and interaction is the lowest. Second, the efficacy of real-time online lecture participation of university students differed according to gender, grade, major, and access devices. This study is meaningful in that it confirmed the applicability of real-time online teaching method due to corona virus and confirmed the applicability of real-time online teaching as a method to prepare for the arrival of full-scale distance education in the post-Corona era. Lastly, it was suggested that the online class management method and support for each college major reflect the changes of the times.