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1.
Korea Observer ; 53(1):105-133, 2022.
Article in English | Web of Science | ID: covidwho-1856657

ABSTRACT

This study examines the relationship between the changes in the number of the COVID-19 confirmed cases and presidential approval ratings by using cases of infectious disease prevention management in Korea. Linking the pandemic quarantine with presidential approval, we apply a time-series spillover analysis to the daily poll data on Korean presidential approval ratings. The findings demonstrate that Moon's presidential approval ratings tend to rise with the health crisis caused by the spread of the COVID-19, because the public evaluating the countermeasures against the COVID-19 of Moon's administration are likely to support Moon when the issue of the COVID-19 is salient. We also find that changes in presidential approval ratings due to the fluctuations in the number of confirmed cases are not consistent but differentiated depending on the period, because of the fatigue of people with the pandemic.

2.
12th International Conference on ICT Convergence (ICTC) - Beyond the Pandemic Era with ICT Convergence Innovation ; : 1750-1753, 2021.
Article in English | Web of Science | ID: covidwho-1853460

ABSTRACT

The severe acute respiratory syndrome virus (SARS-CoV-2), known as COVID-19, has brought untold hardship and deaths all over the world. Individuals affected by COVID-19 often experience respiratory difficulties along with fever, cough, and other symptoms. Social distancing and self-quarantine are strongly suggested by researchers to avoid the exponential spread of the disease. The ultra-wideband (UWB) sensor has recently offered remote monitoring and capturing respiratory signs by ensuring privacy. In this work, a UWB sensor is employed to observe the movement and respiration of a home-quarantined person for fourteen days. After collecting the information in real-time, a deep learning (DL) approach, the long-term short memory (LSTM) framework is further applied to detect the breathing and movement patterns. The experimental result depicts that the framework accomplished 99.93% accuracy with 2 misclassification costs. The proposed application shows promising possibilities into the Internet of medical things (IoMT), smart home health care support system (ShHeS), and practical use in COVID-19 pandemic emergency.

3.
12th International Conference on ICT Convergence (ICTC) - Beyond the Pandemic Era with ICT Convergence Innovation ; : 440-445, 2021.
Article in English | Web of Science | ID: covidwho-1853455

ABSTRACT

As most countries relax restrictions on lockdown and social activities returns due to massive response to COVID-19 vaccination, there is need to put in place a universally acceptable technological innovation that can checkmate and enforce compliance to avoid resurgence of another deadly wave as witnessed previously. Combining vaccination effort with disruptive technology for compliance enforcement is an unarguable panacea. This paper presents an IT-convergence solution that fuses disruptive technologies to distinguish between vaccinated and non-vaccinated individuals in real-time and initiate strict and appropriate compliance directives and consequent denial of access to certain places. The proposed design is a fusion of facial recognition, mask wearing detection technology using Yolov5 deep learning model, network-based vaccination record management application, biometric feature-based vaccination status validation, and compliance enforcement in real-time. The system achieved 99.5% accurate detection and 100% real-time authentication with less computational complexities. This innovation guarantees intuitive monitoring of vaccination progress and curtailment of COVID-19 spread through compliance enforcement.

4.
Journal of Breast Imaging ; : 10, 2022.
Article in English | Web of Science | ID: covidwho-1853109

ABSTRACT

Objective Evaluate women's anxiety and experience undergoing screening mammography during the COVID-19 pandemic. Methods An IRB-approved anonymous survey was administered to women receiving screening mammography across six sites in the U.S. and Singapore from October 7, 2020, to March 11, 2021. Using a 1-5 Likert scale, women rated their pre- and post-visit anxiety regarding having their mammogram during the COVID-19 pandemic, importance of observed COVID-19 precautions, and personal risk factors for breast cancer and severe COVID-19 illness. Post-visit change in anxiety was evaluated. Multivariable logistic regression was used to test associations of pre-visit anxiety with breast cancer and COVID-19 risk factors. Results In total, 1086 women completed the survey. Of these, 59% (630/1061) had >1 breast cancer risk factor;27% (282/1060) had >1 COVID-19 risk factors. Forty-two percent (445/1065) experienced pre-visit anxiety. Pre-visit anxiety was independently associated with risk factors for severe COVID-19 (OR for >2 vs 0 risk factors: 2.04, 95% confidence interval [CI]: 1.11-3.76) and breast cancer (OR for >2 vs 0 risk factors: 1.71, 95% CI: 1.17-2.50), after adjusting for age and site. Twenty-six percent (272/1065) of women reported post-visit anxiety, an absolute 16% decrease from pre-visit anxiety (95% CI: 14%-19%, P < 0.001). Provider masking (941/1075, 88%) and physical distancing (861/1085, 79%) were rated as the most important precautions. Conclusion Pre-visit anxiety was associated with COVID-19 or breast cancer risk factors and declined significantly after screening mammography. Provider masking and physical distancing were rated the most important precautions implemented by imaging clinics.

5.
12th International Conference on Information and Communication Technology Convergence, ICTC 2021 ; 2021-October:1750-1753, 2021.
Article in English | Scopus | ID: covidwho-1642553

ABSTRACT

The severe acute respiratory syndrome virus (SARS-CoV-2), known as COVID-19, has brought untold hardship and deaths all over the world. Individuals affected by COVID-19 often experience respiratory difficulties along with fever, cough, and other symptoms. Social distancing and self-quarantine are strongly suggested by researchers to avoid the exponential spread of the disease. The ultra-wideband (UWB) sensor has recently offered remote monitoring and capturing respiratory signs by ensuring privacy. In this work, a UWB sensor is employed to observe the movement and respiration of a home-quarantined person for fourteen days. After collecting the information in realtime, a deep learning (DL) approach, the long-term short memory (LSTM) framework is further applied to detect the breathing and movement patterns. The experimental result depicts that the framework accomplished 99.93% accuracy with 2 misclassification costs. The proposed application shows promising possibilities into the Internet of medical things (IoMT), smart home health care support system (ShHeS), and practical use in COVID-19 pandemic emergency. © 2021 IEEE.

6.
12th International Conference on Information and Communication Technology Convergence, ICTC 2021 ; 2021-October:440-445, 2021.
Article in English | Scopus | ID: covidwho-1642549

ABSTRACT

As most countries relax restrictions on lockdown and social activities returns due to massive response to COVID-19 vaccination, there is need to put in place a universally acceptable technological innovation that can checkmate and enforce compliance to avoid resurgence of another deadly wave as witnessed previously. Combining vaccination effort with disruptive technology for compliance enforcement is an unarguable panacea. This paper presents an IT-convergence solution that fuses disruptive technologies to distinguish between vaccinated and non-vaccinated individuals in real-time and initiate strict and appropriate compliance directives and consequent denial of access to certain places. The proposed design is a fusion of facial recognition, mask wearing detection technology using Yolov5 deep learning model, network-based vaccination record management application, biometric feature-based vaccination status validation, and compliance enforcement in real-time. The system achieved 99.5% accurate detection and 100% real-time authentication with less computational complexities. This innovation guarantees intuitive monitoring of vaccination progress and curtailment of COVID-19 spread through compliance enforcement. © 2021 IEEE.

7.
Pediatric Diabetes ; 22(SUPPL 30):33, 2021.
Article in English | EMBASE | ID: covidwho-1571042

ABSTRACT

Introduction: An increase in newly diagnosed type 1 diabetes (T1D) has been posited during the COVID-19 pandemic, but data have been conflicting. Objectives: We aimed to determine trends in newly diagnosed T1D and severity of presentation at diagnosis for pediatric and adolescent patients during COVID-19 year (2020) as compared to the previous year (2019) in a multi-center data analysis across the United States. Methods: This retrospective multi-center study included data from seven large U.S. clinical centers recruited from the T1D Exchange Quality Improvement Collaborative (T1DX-QI). Data on diagnosis, diabetic ketoacidosis (DKA), and clinical characteristics were collected from January 1 to December 31, 2020, compared to the prior year. Chi-square tests were used to compare differences in patient characteristics during the pandemic compared to the pre-pandemic comparison group. Results: Across seven member sites, there were 1399 newly diagnosed patients with T1D in 2020, compared to 1277 in 2019 (p=0.007). Of the newly diagnosed patients, a greater number, presented in DKA in 2020 compared to 2019 (599/1399 (42.8%) v. 493/1277 (38.6%), p<0.001), and a higher proportion of these patients presented with severe DKA (p=0.01) as characterized by a pH<7.1 or bicarbonate of <5mmol/L. The mean age at diagnosis was not different, but there were fewer females (p=0.004), and fewer NH White youth diagnosed in 2020 (p<0.001). Newly diagnosed T1D patients in 2020 were less likely to have private insurance (p=0.001). Monthly data trends demonstrated a higher number of new diagnoses of T1D over the spring and summer months (April to September) of 2020 compared to 2019 (p=0.007). Conclusions: We found an increase in newly diagnosed T1D and a greater proportion of newly diagnosed T1D patients presenting in DKA at diagnosis during the COVID-19 pandemic compared to the prior year. Future longitudinal studies are needed to confirm these findings with population level data and determine the long-term impact of COVID-19 on diabetes trends.

8.
11th International Conference on Information and Communication Technology Convergence (ICTC) - Data, Network, and AI in the age of Untact (ICTC) ; : 403-406, 2020.
Article in English | Web of Science | ID: covidwho-1431564

ABSTRACT

This paper reviews the provisions of 3rd Generation Partnership Project (3GPP) Release 16 as it affects Industrial Internet of Things and mission critical communications with a view to see the implementation possibilities and challenges. The goal is to give a useful insight into the expectations of beyond 5G or 6G system with respect to IIoT. 3GPP completed the Release 16 version of its specifications in July 2020 a little behind the target of March 2020. The reality is that the growth of the Internet-of Things (IoT) has resulted in an explosion in the use of IoT devices leading to the prediction that IoT devices will exceed 25 billion in 2020 and may exceed 75 billion in 2025. This explosion has placed the direction of Release 16 on new features for eURLLC (enhanced Ultra-Reliable Low Latency Communication) and Industrial Internet of Things, including Time Sensitive Networks, enhanced location services and support for non public networks. The paper adopted short review of major works available on ieee Xplore between 1993-2020. The result of the research showed that the following are open research issues: Security for industrial IoT beyond 5G, Supporting technologies for industrial IoT beyond 5G, and the need for more testbed research for industrial IoT beyond 5G.

9.
Journal of the American Geriatrics Society ; 69(SUPPL 1):S300-S301, 2021.
Article in English | EMBASE | ID: covidwho-1214845

ABSTRACT

Background: The COVID-19 pandemic continues to have detrimental effects on older adults' mental health while rapidly transforming access to medical care. Our objective was to determine if anxiety surrounding the pandemic may be associated with increased avoidance of in-person medical visits, even when visits were perceived as needed. Methods: We conducted phone-based surveys from April 7th, 2020 to October 27th, 2020 with 155 older adults age ≥60 from two community sites and an academic geriatrics outpatient clinical practice. Healthcare system access was assessed with questions on telehealth use, the number of delayed in-person medical appointments or procedures, and avoidance due to fear of the coronavirus of in-person medical visits participants perceived as 'needed'. Anxiety was measured with the Generalized Anxiety Disorder 2-item (GAD-2) scale. We used bivariate statistics to determine the association between the rate of healthcare utilization and anxiety. We also assessed freetext comments for expression of anxiety or concerns about access to medical care. Results: Participants were on average 75 years old (SD=10), 50% of whom had hearing or vision impairment, 26% had difficulty bathing, 64% lived alone, and 92% had accessed telehealth clinical services. Approximately 52% reported worries about delayed medical care and 41% reported avoiding an in-person medical visit that they felt was needed due to fear of COVID-19;55% of avoided visits were routine or preventive, 43% specialist appointments, and 8% urgent care/ED visits. Individuals screening positive for anxiety were more likely to avoid in-person medical visits (60% versus 47%, p=0.006). Open-ended responses revealed worries about delays in routine medical care (e.g. injections, vaccines, dental visits), fears that non-COVID health needs were deemed non-essential, and difficulty accessing the health system due to restrictions. Conclusion: Older adults who screened positive for anxiety were more likely to avoid needed in-person medical visits, and had a broad range of visit types they felt were needed. Clinicians should work with older patients, particularly those who are anxious, to provide safe and accessible options for seeking in-person care and reassure patients that non-COVID related health concerns are still a priority.

10.
Int. Conf. ICT Convergence ; 2020-October:403-406, 2020.
Article in English | Scopus | ID: covidwho-1026975

ABSTRACT

This paper reviews the provisions of 3rd Generation Partnership Project (3GPP) Release 16 as it affects Industrial Internet of Things and mission critical communications with a view to see the implementation possibilities and challenges. The goal is to give a useful insight into the expectations of beyond 5G or 6G system with respect to IIoT. 3GPP completed the Release 16 version of its specifications in July 2020 a little behind the target of March 2020. The reality is that the growth of the Internet-of Things (IoT) has resulted in an explosion in the use of IoT devices leading to the prediction that IoT devices will exceed 25 billion in 2020 and may exceed 75 billion in 2025. This explosion has placed the direction of Release 16 on new features for eURLLC (enhanced Ultra-Reliable Low Latency Communication) and Industrial Internet of Things, including Time Sensitive Networks, enhanced location services and support for non public networks. The paper adopted short review of major works available on ieee Xplore between 1993-2020. The result of the research showed that the following are open research issues: Security for industrial IoT beyond 5G, Supporting technologies for industrial IoT beyond 5G, and the need for more testbed research for industrial IoT beyond 5G. © 2020 IEEE.

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