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1.
Journal of Information Processing Systems ; 18(3):359-373, 2022.
Article in English | Scopus | ID: covidwho-1954146

ABSTRACT

The new crown pneumonia (COVID-19) has become a global epidemic. The disease has spread to most countries and poses a challenge to the healthcare system. Contact tracing technology is an effective way for public health to deal with diseases. Many experts have studied traditional contact tracing and developed digital contact tracking. In order to better understand the field of contact tracking, it is necessary to analyze the development of contact tracking in the field of computer science by bibliometrics. The purpose of this research is to use literature statistics and topic analysis to characterize the research literature of contact tracking in the field of computer science, to gain an in-depth understanding of the literature development status of contact tracking and the trend of hot topics over the past decade. In order to achieve the aforementioned goals, we conducted a bibliometric study in this paper. The study uses data collected from the Scopus database. Which contains more than 10,000 articles, including more than 2,000 in the field of computer science. For popular trends, we use VOSviewer for visual analysis. The number of contact tracking documents published annually in the computer field is increasing. At present, there are 200 to 300 papers published in the field of computer science each year, and the number of uncited papers is relatively small. Through the visual analysis of the paper, we found that the hot topic of contact tracking has changed from the past “mathematical model,” “biological model,” and “algorithm” to the current “digital contact tracking,” “privacy,” and “mobile application” and other topics. Contact tracking is currently a hot research topic. By selecting the most cited papers, we can display high-quality literature in contact tracking and characterize the development trend of the entire field through topic analysis. This is useful for students and researchers new to field of contact tracking ai well as for presenting our results to other subjects. Especially when comprehensive research cannot be conducted due to time constraints or lack of precise research questions, our research analysis can provide value for it. © 2022. KIPS

2.
Lecture Notes in Educational Technology ; : 439-460, 2022.
Article in English | Scopus | ID: covidwho-1899075

ABSTRACT

Tertiary education in Hong Kong has dramatically changed after the outbreak of COVID-19. Teaching pedagogy and delivery method have been transformed into “Contactless Learning and Teaching” and online learning. However, the focus has been on online learning while seldom analyzing the effect of “Contactless Learning and Teaching” among previous research. This research addressed this gap by studying 156 university students in Hong Kong. ATLAS, a mobile app integrated with iBeacon technology was developed to deliver learning materials in “Contactless Learning and Teaching”. The findings indicated that students who spent more time on “Contactless Learning and Teaching” have better academic performance. The active participation in “Contactless Learning and Teaching” and better academic results could also be explained by the Technology Acceptance Model in this study. The current study proves that iBeacon displays the potential of delivering learning and teaching materials amid the pandemic using the “Contactless Learning and Teaching” approach. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

3.
7th IEEE International Conference on Collaboration and Internet Computing (CIC) ; : 96-104, 2021.
Article in English | English Web of Science | ID: covidwho-1883116

ABSTRACT

Since 2019, the world has been seriously impacted by the global pandemic, COVID-19, with millions of people adversely affected. This is coupled with a trend in which the intensity and frequency of natural disasters such as hurricanes, wildfires, and earthquakes have increased over the past decades. Larger and more diverse communities have been negatively influenced by these disasters and they might encounter crises socially and/or economically, further exacerbated when the natural disasters and pandemics co-occurred. However, conventional disaster response and management rely on human surveys and case studies to identify these in-crisis communities and their problems, which might not be effective and efficient due to the scale of the impacted population. In this paper, we propose to utilize the data-driven techniques and recent advances in artificial intelligence to automate the in-crisis community identification and improve its scalability and efficiency. Thus, immediate assistance to the in-crisis communities can be provided by society and timely disaster response and management can be achieved. A novel framework of the in-crisis community identification has been presented, which can be divided into three subtasks: (1) community detection, (2) in-crisis status detection, and (3) community demand and problem identification. Furthermore, the open issues and challenges toward automated in-crisis community identification are discussed to motivate future research and innovations in the area.

4.
Embase; 2022.
Preprint in English | EMBASE | ID: ppcovidwho-334805

ABSTRACT

Omicron sub-lineage BA.2 has rapidly surged globally, accounting for over 60% of recent SARS-CoV-2 infections. Newly acquired RBD mutations and high transmission advantage over BA.1 urge the investigation of BA.2's immune evasion capability. Here, we show that BA.2 causes strong neutralization resistance, comparable to BA.1, in vaccinated individuals' plasma. However, BA.2 displays more severe antibody evasion in BA.1 convalescents, and most prominently, in vaccinated SARS convalescents' plasma, suggesting a substantial antigenicity difference between BA.2 and BA.1. To specify, we determined the escaping mutation profiles1,2 of 714 SARS-CoV-2 RBD neutralizing antibodies, including 241 broad sarbecovirus neutralizing antibodies isolated from SARS convalescents, and measured their neutralization efficacy against BA.1, BA.1.1, BA.2. Importantly, BA.2 specifically induces large-scale escape of BA.1/BA.1.1effective broad sarbecovirus neutralizing antibodies via novel mutations T376A, D405N, and R408S. These sites were highly conserved across sarbecoviruses, suggesting that Omicron BA.2 arose from immune pressure selection instead of zoonotic spillover. Moreover, BA.2 reduces the efficacy of S309 (Sotrovimab)3,4 and broad sarbecovirus neutralizing antibodies targeting the similar epitope region, including BD55-5840. Structural comparisons of BD55-5840 in complexes with BA.1 and BA.2 spike suggest that BA.2 could hinder antibody binding through S371F-induced N343-glycan displacement. Intriguingly, the absence of G446S mutation in BA.2 enabled a proportion of 440-449 linear epitope targeting antibodies to retain neutralizing efficacy, including COV2-2130 (Cilgavimab)5. Together, we showed that BA.2 exhibits distinct antigenicity compared to BA.1 and provided a comprehensive profile of SARS-CoV-2 antibody escaping mutations. Our study offers critical insights into the humoral immune evading mechanism of current and future variants.

5.
Data Technologies and Applications ; : 19, 2022.
Article in English | Web of Science | ID: covidwho-1806795

ABSTRACT

Purpose The COVID-19 has become a global pandemic, which has caused large number of deaths and huge economic losses. These losses are not only caused by the virus but also by the related rumors. Nowadays, online social media are quite popular, where billions of people express their opinions and propagate information. Rumors about COVID-19 posted on online social media usually spread rapidly;it is hard to analyze and detect rumors only by artificial processing. The purpose of this paper is to propose a novel model called the Topic-Comment-based Rumor Detection model (TopCom) to detect rumors as soon as possible. Design/methodology/approach The authors conducted COVID-19 rumor detection from Sina Weibo, one of the most widely used Chinese online social media. The authors constructed a dataset about COVID-19 from January 1 to June 30, 2020 with a web crawler, including both rumor and non-rumors. The rumor detection task is regarded as a binary classification problem. The proposed TopCom model exploits the topical memory networks to fuse latent topic information with original microblogs, which solves the sparsity problems brought by short-text microblogs. In addition, TopCom fuses comments with corresponding microblogs to further improve the performance. Findings Experimental results on a publicly available dataset and the proposed COVID dataset have shown superiority and efficiency compared with baselines. The authors further randomly selected microblogs posted from July 1-31, 2020 for the case study, which also shows the effectiveness and application prospects for detecting rumors about COVID-19 automatically. Originality/value The originality of TopCom lies in the fusion of latent topic information of original microblogs and corresponding comments with DNNs-based models for the COVID-19 rumor detection task, whose value is to help detect rumors automatically in a short time.

6.
2021 International Conference on Intelligent Traffic Systems and Smart City, ITSSC 2021 ; 12165, 2022.
Article in English | Scopus | ID: covidwho-1779296

ABSTRACT

With the impact of COVID-19, more people are choosing to travel by private cars, which will cause problems such as traffic congestion. It is essential for traffic engineers to have real-time traffic volume, speed, and individual vehicle length. In this study, the ACC7350 millimeter-wave radar was tested, and its advantages and disadvantages were analyzed in vehicle speed, distance from the radar, and vehicle trajectory. The speed detection error between MWR and GPS was within ±6%, and the distance detection error was ±20%. Then the traffic flow detection results of the camera and millimeter-wave radar were compared and analyzed. Results show that the mistakes of traffic flow detection based on vision and MWR are ±4% and ±13%, respectively. Finally, we proposed a traffic data processing method combined with a camera-based target tracking algorithm. © 2021 SPIE.

7.
2021 IEEE International Conference on Big Data, Big Data 2021 ; : 5633-5638, 2021.
Article in English | Scopus | ID: covidwho-1730853

ABSTRACT

The Covid-19 pandemic disrupted the world as businesses and schools shifted to work-from-home (WFH), and comprehensive maps have helped visualize how those policies changed over time and in different places. We recently developed algorithms that infer the onset of WFH based on changes in observed Internet usage. Measurements of WFH are important to evaluate how effectively policies are implemented and followed, or to confirm policies in countries with less transparent journalism. This paper describes a web-based visualization system for measurements of Covid-19-induced WFH. We build on a web-based world map, showing a geographic grid of observations about WFH. We extend typical map interaction (zoom and pan, plus animation over time) with two new forms of pop-up information that allow users to drill-down to investigate our underlying data. We use sparklines to show changes over the first 6 months of 2020 for a given location, supporting identification and navigation to hot spots. Alternatively, users can report particular networks (Internet Service Providers) that show WFH on a given day. We show that these tools help us relate our observations to news reports of Covid-19-induced changes and, in some cases, lockdowns due to other causes. Our visualization is publicly available at https://covid.ant.isi.edu, as is our underlying data. © 2021 IEEE.

8.
Frontiers in Sustainable Cities ; 3:15, 2021.
Article in English | Web of Science | ID: covidwho-1700864

ABSTRACT

COVID-19 poses a massive challenge to urban public-health emergency and governance systems. Urban planners and policymakers engaged in spatial planning and management should carefully consider how a "people-oriented" principle can be incorporated into spatial-planning systems to reduce the negative impacts on both cities and people. However, there is limited literature discussing the aforementioned issues, particularly using qualitative methods. Therefore, this research aims to explore the implications of COVID-19 on spatial planning, well-being, and behavioural change using Changchun as a case study. Semi-structured interviews are used to examine the views and insights of 23 participants. Our results show that, first, the shift to home working has changed people's way of life, affected their subjective well-being, and significantly affected spatial planning within cities, placing greater demands on architectural design and community spatial planning. Therefore, additional open public spaces and a more supportive infrastructure are required. Second, it is found that Changchun has not established an effective community-based spatial planning system, something which should have been taken into consideration in the master plan for the future. Third, our findings suggest that being a resilient city is vital for the sustainable development of second-tier cities like Changchun, which is reflected in urban development patterns, disaster prevention, and long-term functional layout, among other aspects. This study contributes to the existing literature on resilient cities, particularly from the perspective of sustainability with regard to resilience to and recovery from major urban crises. In terms of policy implications, planning departments should work with public health and public safety departments to formulate guidelines and management rules in order to improve the spatial planning of cities during periods of extraordinary change and challenge.

9.
4th International Conference on Innovative Computing, IC 2021 ; 791:999-1005, 2022.
Article in English | Scopus | ID: covidwho-1653371

ABSTRACT

E-learning is a very important way for busy modern people to obtain knowledge because of its convenience and efficiency. Especially it’s a key for most of the students to sustain learning during COVID-19 pandemic. And curriculum modularizing makes curriculum system flexible and easy to add new knowledge to train proper talents meeting the requirement of society conveniently. However, the complex relationships and constraints between modules, curriculum and curriculum system make the adjustment of teaching plan very difficult. This paper puts forward a solution for the modularized-curriculum-oriented E-learning teaching plan adjustment system. The solution can insure the curriculum system being overall optimized based on the idea of information system, knowledge management and data mining. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

10.
American Journal of Translational Research ; 13(12):14157-14167, 2021.
Article in English | EMBASE | ID: covidwho-1610152

ABSTRACT

Background: Previous studies have unveiled the occurrence of re-detectable positive (RP) RNA test result after hospital discharge among recovered COVID-19 patients, but the clinical characteristics of RP patients (RP patients) and the potential features affecting RP RNA test outcome remain unclear. Methods: A total of 742 COVID-19 patients discharged between March 1st, 2020 and March 20th, 2020 were enrolled. All patients were followed-up for SARS-CoV-2 RNA test and RP patents were identified. The clinical characteristics between RP patients and NRP patients were compared, and the potential features affecting re-detectable RNA test outcome were further evaluated. Results: Up to April 9th, 2020, 60 recovered patients (8.09%) had been re-detected to be SARS-CoV-2 RNA positive. Among those 60 RP patients, the median RP time was 12 days from the last negative result of SARS-CoV-2 RNA test or 10 days from hospital discharge. RP patients were prone to be older, having mild/moderate conditions, unilateral lung involvement and fatigue, chills, stuffy or runny nose, with high lymphocyte count. Multivariate logistic analysis and COX regression analysis demonstrated that age, lymphocyte count, urea nitrogen, stuffy or runny nose as well as lung involvement were independently associated with RP RNA test (P<0.05). Conclusions: Older patients accompanied with stuffy or runny nose, low urea nitrogen as well as unilateral lung involvement were more likely to develop RP RNA test result after hospital discharge. Therefore, we strongly suggest using broncho-alveolar lavage fluid for RNA detection, extending quarantine time, and conducting continual follow-up medical examination for those discharged patients.

11.
International Journal of Learning, Teaching and Educational Research ; 20(11):96-114, 2021.
Article in English | Scopus | ID: covidwho-1597080

ABSTRACT

Many pre-service and in-service mathematics teachers have reflected vulnerabilities and unpreparedness for online teaching during the period of the COVID-19 pandemic. They searched for supports and resources to enhance their knowledge, skills, and dispositions relative to online teaching and learning. However, there is no clear path towards reaching these goals. This qualitative and interpretive research focuses on 48 pre-service and in-service teachers' online teaching and learning experiences;while they were engaged in a semester-long mathematics-method course. The findings of this study suggest that factors, like interactions, communication, and peer support impact the pre-service and the in-service mathematics teachers' beliefs and practices toward online teaching and learning. The findings also suggest that social and cultural factors, such as knowing and understanding students' cultural background, access and equity in mathematics education, learners' social and emotional development, and parents' involvement influence mathematics teachers' practices regarding online teaching and learning. The findings indicate that the transformation from in-person to online learning requires the enhancement of pre-service and in-service mathematics teachers' online preparations, particularly in the areas of technology, pedagogy, communication skills, and classroom management. ©Authors.

12.
Zhonghua Liu Xing Bing Xue Za Zhi ; 42(10): 1769-1773, 2021 Oct 10.
Article in Chinese | MEDLINE | ID: covidwho-1534276

ABSTRACT

Objective: To describe the epidemiological characteristics of COVID-19 outbreak in Gaocheng district of Shijiazhuan. Methods: Data and epidemiological survey reports of COVID-19 cases in the outbreak were collected from China's Infectious Disease Information System, Shijazhuang Municipal Center for Diseases Prevention and Control and official information published by the National Health Commission of China. The data were analyzed, using the descriptive epidemiological method. Results: From January 2nd to February 14th, 2021, a total of 1 033 laboratory confirmed COVID-19 cases were reported in Shijiazhuang. The attack rate was 9.36/100 000. The cases were distributed in 14 counties, and most cases (859/1 033, 83.16%) were reported in Gaocheng, and the disease spread to 5 provinces. The cases in Xiaoguo village (299 cases), Liujiazhuo village (107 cases) and Nanqiaozhai village (162 cases) of Zengcun township in Gaocheng accounted for 54.99% of the total cases in Shijiazhuang. The attack rates in the villages mentioned above were 7 412.00/100 000, 10 348.16/100 000 and 6 612.24/100 000, respectively. The ratio of urban cases to rural cases was 1∶15.53. The male to female ratio of the cases was 1∶1.34. The average age of the cases was 40.49 years. The incidence peaks occurred on January 3rd (9.97%, 103 cases) and on January 9th (9.10%, 94 cases). A total of 307 clusters occurred, in which 228(74.27%) occurred in households and 48 (15.64%) occurred in schools or child care settings. But the clusters related with church ceremony had the highest case numbers (82.67 cases/time), followed by wedding feast or feast celebrating the first month of newborn (28.29 cases/time). About 33.02% (313/948) of symptomatic cases only visited the village doctors or private clinics and had no medical care seeking history before the outbreak. Conclusions: The COVID-19 epidemic in Gaocheng of Shijiazhuang was a typical one in rural area. The rapid and hiding transmission of the outbreak was mainly due to the poor health service seeking of the rural residents and the frequent mass gathering.


Subject(s)
COVID-19 , Adult , China/epidemiology , Disease Outbreaks , Female , Humans , Incidence , Infant, Newborn , Male , SARS-CoV-2
13.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021 ; 12978 LNAI:319-334, 2021.
Article in English | Scopus | ID: covidwho-1446044

ABSTRACT

Modeling and predicting human mobility are of great significance to various application scenarios such as intelligent transportation system, crowd management, and disaster response. In particular, in a severe pandemic situation like COVID-19, human movements among different regions are taken as the most important point for understanding and forecasting the epidemic spread in a country. Thus, in this study, we collect big human GPS trajectory data covering the total 47 prefectures of Japan and model the daily human movements between each pair of prefectures with time-series Origin-Destination (OD) matrix. Then, given the historical observations from past days, we predict the countrywide OD matrices for the future one or more weeks by proposing a novel deep learning model called Origin-Destination Convolutional Recurrent Network (ODCRN). It integrates the recurrent and 2-dimensional graph convolutional components to deal with the highly complex spatiotemporal dependencies in sequential OD matrices. Experiment results over the entire COVID-19 period demonstrate the superiority of our proposed methodology over existing OD prediction models. Last, we apply the predicted countrywide OD matrices to the SEIR model, one of the most classic and widely used epidemic simulation model, to forecast the COVID-19 infection numbers for the entire Japan. The simulation results also demonstrate the high reliability and applicability of our countrywide OD prediction model for a pandemic scenario like COVID-19. © 2021, Springer Nature Switzerland AG.

14.
6th International Conference on Advances in Energy Resources and Environment Engineering (ICAESEE) ; 647, 2020.
Article in English | Web of Science | ID: covidwho-1396593

ABSTRACT

Affected by Coronavirus and low oil price, global oil and gas M&A has been in a downturn in 2020. Countries with oil and gas resource intended to revise fiscal terms, enhancing the economics of producing assets and new developments. Many oil companies have faced severe challenges with impact on Coronavirus and oil price crash. According to IHS expectation, the same tightness will continue into 2021. Learned from international oil companies experience, Chinese oil companies should pay more attention to cost reduction, seize opportunities of the M&A market to get high quality projects , and focus more on energy transition.

15.
Pharmacoepidemiology and Drug Safety ; 30:359-360, 2021.
Article in English | Web of Science | ID: covidwho-1381704
16.
7th International Conference on Artificial Intelligence and Security, ICAIS 2021 ; 1424:99-111, 2021.
Article in English | Scopus | ID: covidwho-1355925

ABSTRACT

With the rapid development of mobile Internet, public access to information channels have undergone disruptive changes. This article novel coronavirus pneumonia epidemic situation and other public emergencies, analyzed the current public access to information channel characteristics, discussed the new situation of traditional media information dissemination challenges, and put forward some measures to enhance the transmission power, aimed at improving the effectiveness of journalism. © 2021, Springer Nature Switzerland AG.

17.
Medical Journal of Wuhan University ; 42(5):718-723, 2021.
Article in Chinese | Scopus | ID: covidwho-1350551

ABSTRACT

Objective: To investigate the characteristics of disease transmission, diagnosis, and treatment of COVID‑19 in children. Methods: We retrospectively studied 20 children with COVID‑19 from 4 medical centers in Hubei, China. Results: Among the 20 children, 18 (90.0%) were contaminated by close contact and characterized by family clustering. Seven cases (35.0%) had all family members infected, and 11 cases (55.0%) were confirmed by either of the parents infected. Twelve cases (60.0%) had fever, which was the primary symptom in 10 cases (50.0%). Only one child was in severe degree and combined with severe underlying disease (congenital heart disease). Seven cases (35.0%) presented typical ground‑glass opacity in CT. All patients were confirmed to be infected with SARS‑CoV‑2. Eleven cases (55.0%) had normal white blood cell counts, and one case (5.0%) with severe COVID‑19 showed a continuous decline in T cells subsets. Conclusion: COVID‑19 in children is transmitted by close contact and characterized by family clustering. Fever is the most common symptom or initial symptom. However, the sustained low levels of T cells and underlying diseases are risk factors for severe COVID‑19 children. © 2021, Editorial Board of Medical Journal of Wuhan University. All right reserved.

18.
Ieee Transactions on Industrial Informatics ; 17(9):6499-6509, 2021.
Article in English | Web of Science | ID: covidwho-1307653

ABSTRACT

Chest computed tomography (CT) scans of coronavirus 2019 (COVID-19) disease usually come from multiple datasets gathered from different medical centers, and these images are sampled using different acquisition protocols. While integrating multicenter datasets increases sample size, it suffers from inter-center heterogeneity. To address this issue, we propose an augmented multicenter graph convolutional network (AM-GCN) to diagnose COVID-19 with steps as follows. First, we use a 3-D convolutional neural network to extract features from the initial CT scans, where a ghost module and a multitask framework are integrated to improve the network's performance. Second, we exploit the extracted features to construct a multicenter graph, which considers the intercenter heterogeneity and the disease status of training samples. Third, we propose an augmentation mechanism to augment training samples which forms an augmented multicenter graph. Finally, the diagnosis results are obtained by inputting the augmented multi-center graph into GCN. Based on 2223 COVID-19 subjects and 2221 normal controls from seven medical centers, our method has achieved a mean accuracy of 97.76%. The code for our model is made publicly.(1)

19.
ISPRS International Journal of Geo-Information ; 10(5), 2021.
Article in English | Scopus | ID: covidwho-1256552

ABSTRACT

The development of location-based services facilitates the use of location data for detecting urban events. Currently, most studies based on location data model the pattern of an urban dynamic and then extract the anomalies, which deviate significantly from the pattern as urban events. However, few studies have considered the long temporal dependency of sentiment strength in geotagged social media data, and thus it is difficult to further improve the reliability of detection results. In this paper, we combined a sentiment analysis method and long short-term memory neural network for detecting urban events with geotagged social media data. We first applied a dictionary-based method to evaluate the positive and negative sentiment strength. Based on long short-term memory neural network, the long temporal dependency of sentiment strength in geotagged social media data was constructed. By considering the long temporal dependency, daily positive and negative sentiment strength are predicted. We extracted anomalies that deviated significantly from the prediction as urban events. For each event, event-related information was obtained by analyzing social media texts. Our results indicate that the proposed approach is a cost-effective way to detect urban events, such as festivals, COVID-19-related events and traffic jams. In addition, compared to existing methods, we found that accounting for a long temporal dependency of sentiment strength can significantly improve the reliability of event detection. © 2021 by the authors.

20.
J Biol Regul Homeost Agents ; 35(3): 865-880, 2021.
Article in English | MEDLINE | ID: covidwho-1248534

ABSTRACT

Human Coronavirus (CoV) infections, including SARS-COV, MERS-COV, and SARS-CoV-2, usually cause fatal lower and upper respiratory tract infections due to exacerbated expression of pro-inflammatory cytokines and chemokines. We aim to summarize different aspects, such as CoV immune evasion mechanisms and host innate immune response to these infections, and their role in pathogenesis. We have also elaborated the up-to-date findings on different vaccine development strategies and progress against CoVs in both humans and non-human models. Most importantly, we have described the Phageome-human immune interaction, its therapeutic usage as anti-viral, anti-inflammatory agent, and implications for multiple vaccine development systems. The data suggest that endogenous phages might play a vital role in eliminating the infection and regulating the body's immune system. Considering the innate-immune-induced pathogenesis against CoVs and the therapeutic aptitude of phageome, we propose that the prophylactic administration of phages and phage-based vaccines could be a useful strategy to control the emerging CoV infections.


Subject(s)
COVID-19 , Virome , Humans , Immunity, Innate , SARS-CoV-2 , Vaccination
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