Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 49
Filter
1.
Heliyon ; : e10575, 2022.
Article in English | ScienceDirect | ID: covidwho-2031296

ABSTRACT

Individuals' COVID-19 vaccination behaviors were examined when the government introduced a new vaccine into the immunization program. The purpose of this study is to thoroughly examine the effects of COVID-19 risk perception (CR), COVID-19 vaccination perception (VC), and Social Media (SO) on COVID-19 vaccine hesitancy (HE) in Vietnam. Three hundred fifty samples were collected regarding a reluctance to vaccinate against COVID-19 from 6/2021 to 7/2021. This is when immunizations are administered and injected in Vietnam;hence, hesitation regarding injection is rather prevalent. The multivariate regression analysis is conducted on a dataset of 350 Vietnamese respondents using the Partial Least Squares-Structural Equation Modeling (PLS-SEM) approach. According to the findings, the Perception Vaccine functions as a link between CV and HE. CR has a beneficial effect on both HE and VC, whereas VC has a negative impact on HE. Simultaneously, the study illustrates the detrimental effect of SO on immunity by comparing it to the influence of social media. The study's findings demonstrate the critical role of protection motivational theory (PMT) and information theory in promoting vaccination efforts in Vietnam. The study's findings indicate that PMT and information theory promote immunization initiatives in Vietnam.

2.
Journal of Gastroenterology and Hepatology ; 37:89-90, 2022.
Article in English | Web of Science | ID: covidwho-2030795
3.
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Acl 2022), Vol 1: (Long Papers) ; : 3108-3127, 2022.
Article in English | Web of Science | ID: covidwho-2030731

ABSTRACT

Even to a simple and short news headline, readers react in a multitude of ways: cognitively (e.g. inferring the writer's intent), emotionally (e.g. feeling distrust), and behaviorally (e.g. sharing the news with their friends). Such reactions are instantaneous and yet complex, as they rely on factors that go beyond interpreting factual content of news. We propose Misinfo Reaction Frames (MRF), a pragmatic formalism for modeling how readers might react to a news headline. In contrast to categorical schema, our free-text dimensions provide a more nuanced way of understanding intent beyond being benign or malicious. We also introduce a Misinfo Reaction Frames corpus, a crowdsourced dataset of reactions to over 25k news headlines focusing on global crises: the Covid-19 pandemic, climate change, and cancer. Empirical results confirm that it is indeed possible for neural models to predict the prominent patterns of readers' reactions to previously unseen news headlines. Additionally, our user study shows that displaying machine-generated MRF implications alongside news headlines to readers can increase their trust in real news while decreasing their trust in misinformation. Our work demonstrates the feasibility and importance of pragmatic inferences on news headlines to help enhance AI-guided misinformation detection and mitigation.

4.
Cureus ; 14(7):e27353, 2022.
Article in English | MEDLINE | ID: covidwho-2025379

ABSTRACT

Multisystem inflammatory syndrome in adults (MIS-A) is a systemic inflammatory syndrome that presents with a heterogeneous collection of signs and symptoms in adults. Here we present a case of a 38-year-old male who met the case definition of the MIS-A four weeks after a mild, symptomatic case of coronavirus disease 2019 (COVID-19) despite receiving casirivimab-imdevimab (REGEN-COV). Given the presence of signs and symptoms consistent with MIS-A, the patient was started on intravenous immune globulin (IVIG) and IV methylprednisolone. He promptly demonstrated clinical improvement over the next several days.

5.
Jama ; 26:26, 2022.
Article in English | MEDLINE | ID: covidwho-2013218

ABSTRACT

Importance: Despite the expansion of SARS-CoV-2 testing, available tests have not received Emergency Use Authorization for performance with self-collected anterior nares (nasal) swabs from children younger than 14 years because the effect of pediatric self-swabbing on SARS-CoV-2 test sensitivity is unknown. Objective: To characterize the ability of school-aged children to self-collect nasal swabs for SARS-CoV-2 testing compared with collection by health care workers. Design, Setting, and Participants: Cross-sectional study of 197 symptomatic children and adolescents aged 4 to 14 years old. Individuals were recruited based on results of testing in the Children's Healthcare of Atlanta system from July to August 2021. Exposures: Children and adolescents were given instructional material consisting of a short instructional video and a handout with written and visual steps for self-swab collection. Participants first provided a self-collected nasal swab. Health care workers then collected a second specimen. Main Outcomes and Measures: The primary outcome was SARS-CoV-2 detection and relative quantitation by cycle threshold (Ct) in self- vs health care worker-collected nasal swabs when tested with a real-time reverse transcriptase-polymerase chain reaction test with Emergency Use Authorization. Results: Among the study participants, 108 of 194 (55.7%) were male and the median age was 9 years (IQR, 6-11). Of the 196 participants, 87 (44.4%) tested positive for SARS-CoV-2 and 105 (53.6%) tested negative by both self- and health care worker-collected swabs. Two children tested positive by self- or health care worker-collected swab alone;1 child had an invalid health care worker swab. Compared with health care worker-collected swabs, self-collected swabs had 97.8% (95% CI, 94.7%-100.0%) and 98.1% (95% CI, 95.6%-100.0%) positive and negative percent agreement, respectively, and SARS-CoV-2 Ct values did not differ significantly between groups (mean [SD] Ct, self-swab: 26.7 [5.4] vs health care worker swab: 26.3 [6.0];P = .65). Conclusions and Relevance: After hearing and seeing simple instructional materials, children and adolescents aged 4 to 14 years self-collected nasal swabs that closely agreed on SARS-CoV-2 detection with swabs collected by health care workers.

6.
PloS one ; 17(8):e0272546, 2022.
Article in English | MEDLINE | ID: covidwho-2009688

ABSTRACT

OBJECTIVES: The coronavirus disease 2019 pandemic has affected countries around the world since 2020, and an increasing number of people are being infected. The purpose of this research was to use big data and artificial intelligence technology to find key factors associated with the coronavirus disease 2019 infection. The results can be used as a reference for disease prevention in practice. METHODS: This study obtained data from the "Imperial College London YouGov Covid-19 Behaviour Tracker Open Data Hub", covering a total of 291,780 questionnaire results from 28 countries (April 1~August 31, 2020). Data included basic characteristics, lifestyle habits, disease history, and symptoms of each subject. Four types of machine learning classification models were used, including logistic regression, random forest, support vector machine, and artificial neural network, to build prediction modules. The performance of each module is presented as the area under the receiver operating characteristics curve. Then, this study further processed important factors selected by each module to obtain an overall ranking of determinants. RESULTS: This study found that the area under the receiver operating characteristics curve of the prediction modules established by the four machine learning methods were all >0.95, and the RF had the highest performance (area under the receiver operating characteristics curve is 0.988). Top ten factors associated with the coronavirus disease 2019 infection were identified in order of importance: whether the family had been tested, having no symptoms, loss of smell, loss of taste, a history of epilepsy, acquired immune deficiency syndrome, cystic fibrosis, sleeping alone, country, and the number of times leaving home in a day. CONCLUSIONS: This study used big data from 28 countries and artificial intelligence methods to determine the predictors of the coronavirus disease 2019 infection. The findings provide important insights for the coronavirus disease 2019 infection prevention strategies.

7.
Maritime Business Review ; 2022.
Article in English | Scopus | ID: covidwho-1948704

ABSTRACT

Purpose: This study aims to identify the characteristics of the maritime shipping network in Northeast Asia as well as compare the level of port connectivity among these container ports in the region. In addition, this study analyses the change in role and position of 20 ports in the region by clustering these ports based on connectivity index and container throughput and route index. Design/methodology/approach: This study employs Social Network Analysis (SNA) to delineate the international connectivity of major container ports in Northeast Asia. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to identify each port's connectivity index and container throughput index, and the resulting indexes are employed as the basis to cluster 20 major ports by fuzzy C-mean (FCM). Findings: The results revealed that Northeast Asia is a highly connected maritime shipping network with the domination of Shanghai, Shenzhen, Hong Kong and Busan. Furthermore, both container throughput and connectivity in almost all container ports in the region have decreased significantly due to the coronavirus disease 2019 (COVID-19) pandemic. The rapid growth of Shenzhen and Ningbo has allowed them to join Cluster 1 with Shanghai while maintaining high connectivity, yet decreasing container throughput has pushed Busan down to Cluster 2. Originality/value: The originality of this study is to combine indexes of SNA into connectivity index reflecting characteristics of the maritime shipping network in Northeast Asia and categorize 20 major ports by FCM. © 2022, Pacific Star Group Education Foundation.

8.
Journal for Educators Teachers and Trainers ; 13(2):12, 2022.
Article in English | Web of Science | ID: covidwho-1897377

ABSTRACT

The stressful relationship between children and parents is the pain both go through when they find themselves unable to cope as a parent or a child. In order to find out the status, causes, and impacts of the COVID-19 pandemic and suggest some solutions to reduce stress between parents and children, we surveyed the impact of the COVID-19 pandemic on stressful relationships between parents and children at high school age in Da Nang city. The findings of a survey conducted on 550 randomly selected parents and 550 high school students using the Perceived Stress Scale reveal a high rate of tension between parents and their children, particularly up to 51.1% and 38.5%, respectively. In reality, many factors are affecting the stressful relationship between parents and their children at this age, in which psychological fear about health;social distancing policy;closed schools;students staying at home 24 hours a day and learning online;the disruption in children's daily routine;excessive use of electronic devices are major causes of stress in the relationship between parents and their children. From this practice, our research team has proposed such solutions as participating in creative activities and consulting the handbook instructing parents' behaviour rules toward children and vice versa, designing extra-curricular activities, and organizing training courses on life values for both parents and children to increase happiness and reduce stress in the parent-child relationship.

9.
Computers, Materials and Continua ; 72(3):5059-5078, 2022.
Article in English | Scopus | ID: covidwho-1836524

ABSTRACT

The main objective of this study is to comprehensively investigate individuals' vaccination intention against COVID-19 during the second wave of COVID-19 spread in Vietnam using a novel hybrid approach. First, the Decision-Making Trial and Evaluation Laboratory based on Grey Theory (DEMATEL-G) was employed to explore the critical factors of vaccination intention among individuals. Second, Partial Least Squares-Structural Equation Modeling (PLS-SEM) was applied to test the hypotheses of individual behavioral intention to get the vaccine to prevent the outbreak of COVID- 19. A panel of 661 valid respondents was collected from June 2021 to July 2021, and confidentiality was maintained for all data obtained. The results identified that perception of COVID-19 vaccination and trust in vaccination strategy directly associated with individuals' COVID-19 immunization. Hence, the perceived severity of COVID-19 has an indirect impact onCOVID- 19 vaccination intentions via the perception of the COVID-19 vaccine. These findings indicated that the government's information about vaccines is necessary for the new phase of vaccination intervention strategies in Vietnam. Therefore, the study suggests that the government needs to give complete information about the role of vaccines prioritizes transparency in official information about COVID-19 vaccines to allay concerns about side effects, allowing for the most appropriate policy formulation and implementation to encourage public vaccination. Future studies can apply PLS-SEM and other MCDMmodels with the fuzzy, hesitant numbers to re-evaluate the feasibility, validity and reliability of this research's proposed model. © 2022 Tech Science Press. All rights reserved.

10.
Green Energy and Technology ; : 187-203, 2022.
Article in English | Scopus | ID: covidwho-1826224

ABSTRACT

Recently, the Covid-19 pandemic has become very complicated and seriously affecting the economy as well as society in every countries in the world. In this chapter, we explore the solution of Computer Vision for handling the Covid-19 pandemic situation. The given scenarios based on deep learning techniques are used to monitor the traffic of people and vehicles through the checkpoints to control the in-out movement in significant areas. In addition, we also need to pay attention to complying with the regulations on wearing masks and ensuring a safe social distance in public places. From there, the proposed system will effectively support organizations to deal with the Covid-19 pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

11.
Clinical Cancer Research ; 27(6 SUPPL 1), 2021.
Article in English | EMBASE | ID: covidwho-1816895

ABSTRACT

Background: Cancer therapy may put patients at risk of mortality from COVID-19. The impact of abbreviated treatment courses on outcomes in the setting of COVID-19 is unknown. We incorporated COVID-19-associated risks in re-analysis of practice-defining randomized trials in oncology that compared different radiation therapy (RT) regimens. Methods: We extracted individual patient level data (IPLD) from published survival curves from randomized trials in rectal cancer (Dutch TME, TROG 01.04), early stage breast cancer (CALGB 9343, OCOG hypofractionation trial, FAST-Forward, NSABP B-39), and localized prostate cancer (CHHiP, HYPO-RT-PC). Trials were simulated with incorporation of varying risk of SARS-CoV-2 infection and mortality associated with receipt of therapy. Results: IPLD from 14,170 patients were re-analyzed. In scenarios with low COVID-19-associated risks (0.5% infection risk per fraction [IRF], 5% case fatality rate [CFR]), fractionation did not significantly affect outcomes. In locally advanced rectal cancer, short-course RT appeared preferable to long-course chemoradiation (TROG 01.04) or RT omission (Dutch TME) in most settings. While moderate hypofractionation in early stage breast cancer (OCOG hypofractionation trial) and prostate cancer (CHHiP) was not associated with survival benefits in the setting of COVID-19, more aggressive hypofractionation (FAST-Forward, HYPO-RT-PC) and accelerated partial breast irradiation (NSABP B-39) were associated with improved survival in higher risk scenarios (≥5% IRF;≥ 20% CFR). In settings where RT can be omitted, such as favorable early stage breast cancer in the elderly (CALGB 9343), RT was associated with worse survival in higher risk pandemic scenarios (≥5% IRF, ≥ 20% CFR). Conclusions: Our framework, which can be adapted to dynamic changes in COVID-19 risk, provides a flexible, quantitative approach to assess the impact of treatment recommendations across oncology. The magnitude of potential benefit from abbreviated RT courses depends on the degree of hypofractionation and local COVID-19-associated risk. Abbreviated RT courses should be prioritized when possible and are increasingly beneficial in higher risk pandemic settings. With increased understanding and precautions against COVID-19 that can minimize risks for patients, our results support the continued use of evidence-based treatments for cancer patients in the COVID-19 era.

12.
Epidemics ; 39: 100569, 2022 06.
Article in English | MEDLINE | ID: covidwho-1804061

ABSTRACT

The effort for combating the COVID-19 pandemic around the world has resulted in a huge amount of data, e.g., from testing, contact tracing, modelling, treatment, vaccine trials, and more. In addition to numerous challenges in epidemiology, healthcare, biosciences, and social sciences, there has been an urgent need to develop and provide visualisation and visual analytics (VIS) capacities to support emergency responses under difficult operational conditions. In this paper, we report the experience of a group of VIS volunteers who have been working in a large research and development consortium and providing VIS support to various observational, analytical, model-developmental, and disseminative tasks. In particular, we describe our approaches to the challenges that we have encountered in requirements analysis, data acquisition, visual design, software design, system development, team organisation, and resource planning. By reflecting on our experience, we propose a set of recommendations as the first step towards a methodology for developing and providing rapid VIS capacities to support emergency responses.


Subject(s)
COVID-19 , COVID-19/epidemiology , Contact Tracing , Humans , Pandemics
13.
International Journal of Qualitative Methods ; 21:13, 2022.
Article in English | Web of Science | ID: covidwho-1799145

ABSTRACT

Doing remote fieldwork is a 'new normal' in the COVID-19 pandemic era. It is challenging, but not impossible. With planning and preparation, comprehensive training and ongoing support for Research Assistants (RAs), researchers can overcome the challenges of remote fieldwork. In this article, we reflect on the experience of employing local RAs to support doctoral research involving in-depth household interviews and focus group discussions with ethnic minority people in upland Vietnam. The challenge of adapting to this 'new normal' provided us with an opportunity for a critical appraisal of the researcher-RA relationship. The approach to remote fieldwork we developed centres on frequent communication, feedback and building trusting team dynamics. We argue that this approach can overcome some of the power hierarchies between global north researchers and local RAs, and therefore, should not simply be seen as a temporary or inferior 'Plan B' for researchers, but should be embraced as a way of reimagining knowledge production. We discuss lessons learned in how to carry out remote fieldwork, present practical strategies and recommendations, and consider the strengths of this approach for knowledge production and the empowerment of researchers in the global south.

14.
Journal of Asian Finance Economics and Business ; 9(4):367-380, 2022.
Article in English | Web of Science | ID: covidwho-1798662

ABSTRACT

Using an extended unified theory of acceptance and use of technology, the goal of this paper is to investigate the antecedents of behavioral intention towards mobile money, as well as the mediating effect of trust between behavioral intention and financial inclusion in Vietnam during the COVID-19 period (UTAUT2). The data for this study was obtained via an online self-administered questionnaire, which was then analyzed using the SmartPLS 3.3.3 program. For the purpose of determining the relevance and performance of the exogenous constructs, an importance-performance matrix analysis was performed. The findings of this study suggest that knowledge, structural assurance, habit, and performance expectancy are the most important factors influencing users' behavioral intention to use mobile money. In the case of mobile money adoption, the behavioral intention has a significant influence, and trust does not mediate between behavioral intention and financial inclusion. For the first time in Vietnam, the extended UTAUT2 is being used to investigate mobile money usage and adoption patterns. The current study, however, focuses on users' financial inclusion goals rather than their intended behavior.

15.
IEEE Transactions on Services Computing ; 2022.
Article in English | Scopus | ID: covidwho-1788797

ABSTRACT

We present the design and development of a data visualization service (RAMPVIS) in response to the urgent need to support epidemiological modeling workflows during the COVID-19 pandemic. Facing a set of demanding requirements and several practical challenges, our small team of volunteers had to rely on existing knowledge and components of services computing, while thinking on our feet in configuring services composition and adopting suitable approaches to services engineering. Through developing the RAMPVIS service, we have gained useful experience of ensuring conformation to services computing standards, enabling rapid development and early deployment, and facilitating effective and efficient maintenance and operation with limited resources. This experience can be valuable to the ongoing effort for combating the COVID-19 pandemic, and provides a blueprint for visualization service development when future needs for visual analytics arise during emergency response. IEEE

16.
6th IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2021 ; : 117-118, 2021.
Article in English | Scopus | ID: covidwho-1759015

ABSTRACT

This work introduces a low-latency, searchable web tool for biologist and healthcare researchers to quickly explore a large number of host-pathogen interactions (HPI) reported in scientific publication. Our database contains 23,581 generic HPI and 257 COVID-19 related HPI extracted from 32 million PubMed s. The data was automatically collected by running our high-precision biomedical text mining system, which consumes much less effort than manual curation while still provides reliable output. Web URL: philm2web.live © 2021 IEEE.

17.
Biophysical Journal ; 121(3):538A-538A, 2022.
Article in English | Web of Science | ID: covidwho-1755846
18.
Open Forum Infectious Diseases ; 8(SUPPL 1):S267, 2021.
Article in English | EMBASE | ID: covidwho-1746669

ABSTRACT

Background. The data on CAPA in the U.S. are limited to date and clinical characteristics unique to this phenomenon have not been widely reported. Methods. This retrospective observational study was conducted at multiple VA hospitals across southern California and Arizona. CAPA cases were identified in inpatients with laboratory-confirmed COVID-19 based on microbiologic or serologic evidence of aspergillosis and pulmonary abnormalities on imaging, and were classified according to ECMM/ISHAM consensus definitions. Characteristics of interest included immunosuppressive/modulatory agents used prior to onset of CAPA, COVID-19 disease course, length of hospitalization, and mortality. Results. Seventeen patients with probable (18%) or possible (82%) CAPA were identified from April 2020 to March 2021. Values below reported as medians. All patients were male and 13 (76%) were white, with age 74 years and BMI 26 kg/m2. Baseline comorbidities included diabetes mellitus (47%), cardiovascular disease (65%), and pulmonary disease (71%). Evidence of aspergillosis was mostly based on respiratory culture, with mainly A. fumigatus (75%). Systemic corticosteroids were used in 14 patients, with a total dose of 400 mg prednisone equivalents starting 10 days prior to Aspergillus detection. Patients also received tocilizumab (18%), leflunomide (6%), tacrolimus (6%), mycophenolate (6%), and investigational agent LSALT or placebo (6%);2 patients (12%) did not receive any immunosuppression/modulation. Length of hospitalization for COVID-19 was 22 days. Death occurred in 12 patients (71%), including all patients with probable CAPA, at 34 days after COVID-19 diagnosis and 16 days after CAPA diagnosis. Eight patients (47%) were treated for aspergillosis;mortality did not appear to differ with treatment (75% vs. 67%). Conclusion. This case series reports high mortality among patients with CAPA;the primary contributor to this outcome is unclear. Frequency of lower respiratory tract sampling in patients with COVID-19 may have limited diagnosis of CAPA. Interestingly, inpatient respiratory cultures with Aspergillus spp. increased compared to previous years. Future work will attempt to identify risk factors for CAPA and attributable mortality via comparison to inpatients with COVID-19 without CAPA.

19.
Open Forum Infectious Diseases ; 8(SUPPL 1):S367-S368, 2021.
Article in English | EMBASE | ID: covidwho-1746464

ABSTRACT

Background. Bamlanivimab and casirivimab/imdevimab were the first monoclonal antibodies (mAb) developed against SARS-CoV-2 and proved beneficial early in the course of infection. However, real-world administration of these therapies presents logistical challenges. We present our experience implementing mAb treatment at a large VA Medical Center and review the efficacy of therapy in preventing hospitalization from COVID-19 in a closed healthcare system. Methods. All positive outpatient COVID tests performed at VA Greater Los Angeles Healthcare System (GLA) were reviewed by the Emergency Medicine (EM) and Infectious Diseases (ID) Sections for mAb eligibility beginning 12/2/2020. Due to limited supply, treatment was prioritized for patients at highest risk of developing severe disease, as determined by EM/ID with input from a machine learning ensemble risk estimation model produced by VA National Artificial Intelligence Institute (Figure 1). If a patient declined or did not reply, treatment was offered to the next patient on a ranked eligibility list. Those who declined or were eligible but not treated were included in the analysis. Patients were excluded if they were hospitalized before treatment was offered. We collected data on age, comorbidities, date of diagnosis, and admission at 30 days after diagnosis. A multivariate log binomial regression was performed to determine the relative risk of admission within 30 days of diagnosis for those who received mAb therapy as compared to those who did not, adjusting for age and comorbidity. All analysis was done in R (version 4.0.5). Results. 139 patients met inclusion criteria. 45 (32%) received mAb therapy, 48 (35%) declined mAb therapy, and the remaining 46 (33%) either did not respond or were not offered mAb therapy. Hospitalizations following diagnosis in each group are illustrated in Figure 2. There was a trend towards reduced absolute and relative risk of hospitalization (Table 1). There were no anaphylactic events in patients who received mAb therapy. Conclusion. At our facility, a system for rapid identification of candidates and a coordinated distribution plan was essential in ensuring timely administration of mAb therapy to eligible patients. Administration of mAb showed a trend towards decreased risk of hospitalization due to SARS-CoV-2.

20.
2021 IEEE International Conference on Big Data, Big Data 2021 ; : 4387-4395, 2021.
Article in English | Scopus | ID: covidwho-1730874

ABSTRACT

COVID-19 is an air-borne viral infection, which infects the respiratory system in the human body, and it became a global pandemic in early March 2020. The damage caused by the COVID-19 disease in a human lung region can be identified using Computed Tomography (CT) scans. We present a novel approach in classifying COVID-19 infection and normal patients using a Random Forest (RF) model to train on a combination of Deep Learning (DL) features and Radiomic texture features extracted from CT scans of patient's lungs. We developed and trained DL models using CNN architectures for extracting DL features. The Radiomic texture features are calculated using CT scans and its associated infection masks. In this work, we claim that the RFs classification using the DL features in conjunction with Radiomic texture features enhances prediction performance. The experiment results show that our proposed models achieve a higher True Positive rate with the average Area Under the Receiver Curve (AUC) of 0.9768, 95% Confidence Interval (CI) [0.9757, 0.9780]. © 2021 IEEE.

SELECTION OF CITATIONS
SEARCH DETAIL