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1.
23rd IEEE International Conference on Mobile Data Management, MDM 2022 ; 2022-June:169-178, 2022.
Article in English | Scopus | ID: covidwho-2037826

ABSTRACT

Epidemics such as COVID-19, SARS, H1N1 have highly transmissible viruses and spread wildly through the population with negative consequences. Multiple studies have shown the correlation between the contact networks between individuals and the transmission of infections due to contact between colocated individuals. To mitigate the transmission of the virus, intervention measures have been applied without decisive success. Therefore, reducing transmissions through suitable epidemicaware POI recommendations to users is necessary to cope with user mobility. Current POI recommendation approaches do not take into consideration the transmission of infections between co-located users. In this paper, we formulate a new query named Epidemic-aware POI Recommendation Query (EPQ), to timely recommend a set of POIs to users at different time steps, while considering the spread of infection between co-located users, their social friendships, and their preference. We prove that EPQ is NP-hard and propose an effective and efficient algorithm, Epidemic-aware POI Recommendation (EpRec) to tackle EPQ. We evaluate EpRec on existing location-based social networks and pandemic datasets against state-of-the-art algorithms. The experimental results show that EpRec outperforms the baselines in effectiveness and efficiency. © 2022 IEEE.

2.
38th IEEE International Conference on Data Engineering, ICDE 2022 ; 2022-May:2845-2858, 2022.
Article in English | Scopus | ID: covidwho-2018817

ABSTRACT

The potential impact of epidemics, e.g., COVID-19, H1N1, and SARS, is severe on public health, the economy, education, and society. Before effective treatments are available and vaccines are fully deployed, combining Non-Pharmaceutical Interventions (NPIs) and vaccination strategies is the main approaches to contain the epidemic or live with the virus. Therefore, research for deciding the best containment operations to contain the epidemic based on various objectives and concerns is much needed. In this paper, we formulate the problem of Containment Operation Optimization Design (COOD) that optimizes the epidemic containment by carefully analyzing contacts between individuals. We prove the hardness of COOD and propose an approximation algorithm, named Multi-Type Action Scheduling (MTAS), with the ideas of Infected Ratio, Contact Risk, and Severity Score to select and schedule appropriate actions that implement NPIs and allocate vaccines for different groups of people. We evaluate MTAS on real epidemic data of a population with real contacts and compare it against existing approaches in epidemic and misinformation containment. Experimental results demonstrate that MTAS improves at least 200% over the baselines in the test case of sustaining public health and the economy. Moreover, the applicability of MTAS to various epidemics of different dynamics is demonstrated, i.e., MTAS can effectively slow down the peak and reduce the number of infected individuals at the peak. © 2022 IEEE.

3.
Ieee Access ; 10:85199-85212, 2022.
Article in English | Web of Science | ID: covidwho-2005082

ABSTRACT

Due to the explosive increase in IoT devices and traffic, big data is developing into smart data that helps the data science experts understand human activities, through the relationship between mobility and resource application of the users in public spaces. For example, smart data markets help to predict crimes or understand the cause of COVID-19 infections. For these smart services, the users agree to the privacy policy so that the personal and sensitive information can be collected by a third party. But the conditions of the privacy policy do not specify whether the information of the users can be tracked. To ensure data transparency, many systems are applying consortium/private blockchains with raft algorithm. The raft algorithm requires nodes to check countless messages for a single transaction. Eventually, as the number of nodes increases, the overall system degradation is derived from the burden of the leader node. This paper proposes a method to process the collected transactions by dividing a certain amount of transactions into cells, without any extra protocol. The proposed scheme also uses the federated learning model with high accuracy and data privacy, in order to determine the optimized cell size in a blockchain system that should lead to consensus on multiple servers. Therefore, the proposed CBR (Cell-based Raft) consensus algorithm proposes a protocol that reduces the number of messages, without interfering with the concept of the existing raft algorithm, in order to maintain stable throughput in the smart data market where massive transactions occur.

4.
Advanced Functional Materials ; 2022.
Article in English | Web of Science | ID: covidwho-1995522

ABSTRACT

With the rapid progress in nanomaterials and biochemistry, there has been an explosion of interest in biomolecule-modified quantum dots (QDs) for biomedical applications. Metal chalcogenide quantum dots (MCQDs), as the most widely studied QDs, have attracted tremendous attention in the biomedical field on account of their unique and excellent optical properties and the ease of biomolecular modifications. Herein, important advances in MCQDs over recent years are reviewed, from materials design to biomedical applications. Especially, this review focuses on the challenges encountered in the applications of MCQDs in biomedical fields and how these problems can be solved by rational design of synthesis methods and modifications, which have opened a universal route to develop the functionalized MCQDs. Moreover, recent processes in bioimaging, biosensing, and cancer therapy based on MCQDs are examined, including the rapid detection and diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This review provides broad insights into MCQDs in the biomedical field and will inspire material researchers to develop MCQDs in the future.

5.
2022 24th International Conference on Advanced Communication Technology (Icact): Aritiflcial Intelligence Technologies toward Cybersecurity ; : 392-+, 2022.
Article in English | Web of Science | ID: covidwho-1995271

ABSTRACT

Recently, given the current situation the world is undergoing with the COVID-19 pandemic, a lot of companies encourage remote working from home. As a result, the use of the cloud has increased, but as a consequence, cyberattacks on the cloud systems have escalated. However, organizations and companies still have concerns about the use of the cloud as most of them remain unaware of the security threats against cloud system. This work researches through major concerns of the organizations and companies for cloud security and tries to explain why it is important for the organization to be aware of the threats that the cloud is facing. It also proposes a three-step strategy to help companies and organizations to secure their cloud system.

6.
Materials Chemistry Frontiers ; : 14, 2022.
Article in English | Web of Science | ID: covidwho-1984453

ABSTRACT

Bacterial infection is a major threat to public health around the world. Currently, antibiotics remain the most extensive mode of medical treatment for bacterial infection. However, the overuse and misuse of antibiotics have exacerbated the emergence of antibiotic-resistant strains, especially during the COVID-19 pandemic. In addition, the improper and excessive use of biocides and disinfectants has a catastrophic impact on antibiotic management plans worldwide. Therefore, there is an urgent need for alternative antibacterial treatments to alleviate this crisis. In recent years, nanozymes have become promising new antibacterial agents because of their broad-spectrum antibacterial activity, less drug resistance, and high stability. This review focuses on the classification of nanozymes and research progress of nanozymes as antibacterial agents, as well as perspectives for future research in this field.

7.
Annals of Child Neurology ; 30(3):111-119, 2022.
Article in English | Scopus | ID: covidwho-1965023

ABSTRACT

Purpose: Coronavirus disease 2019 (COVID-19) causes various neurological symptoms in children, as well as respiratory symptoms, and the number of reported cases is increasing with the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. This study aimed to investigate the neurological symptoms and incidence in pediatric patients hospitalized with COVID-19. Methods: We retrospectively analyzed the medical records of patients under the age of 18 diagnosed with COVID-19 and admitted to National Health Insurance Service Ilsan Hospital using real-time reverse transcription–polymerase chain reaction from December 2020 to March 2022. We reviewed data on the age of confirmed COVID-19 patients, fever, and respiratory, gastrointes-tinal, and neurological symptoms. We evaluated the chief complaints of hospitalization and classified them as non-neurological or neurological, according to the chief complaints that caused the most discomfort. Results: Among 376 patients, 63 (16.8%) and 313 (83.2%) patients were classified as having neurological and non-neurological symptoms, respectively. The most common neurological symptoms were headache (49, 13.0%), followed by seizures (39, 10.4%), myalgia (24, 6.4%), and diz-ziness (14, 3.7%). Additionally, there were patients with anosmia (nine, 2.4%), ageusia (four, 1.1%), and visual disturbance (two, 0.5%). Of the 39 patients who experienced seizures, 15 (15/39, 51.7%) had no symptoms except fever, and seizures were the only main presenting symptom of SARS-CoV-2 infection. Conclusion: Neurological symptoms are common in pediatric COVID-19 patients. Seizures can be an early symptom of SARS-CoV-2 infection and should not be underestimated during the COVID-19 pandemic. © 2022 Korean Child Neurology Society.

8.
Frontiers in Environmental Science ; 10, 2022.
Article in English | Scopus | ID: covidwho-1952299

ABSTRACT

The recent outbreak of epidemic disease (COVID-19) has dramatically changed the socio-economic and environmental dynamics of the world. In particular, it affects human movement, travel intentions, and ambient air pollution amid rising stringency measures. Therefore, this study examines the influence of tourism knowledge, environmental vulnerability, and risk knowledge on travelers’ intentions in China’s tourism industry during COVID-19. To address the study objectives, an online survey questionnaire was created, through which a valid sample of 402 respondents was achieved. The direct and indirect relationship between variables was tested through structural equation modeling, the outcomes confirm that both tourism knowledge and risk knowledge in terms of COVID-19 significantly and negatively define the travelers’ intention toward tourism. Moreover, environmental vulnerability moderately affected tourism behavior and augmented with COVID-19 stringency disclosures. The mediating effect of risk perception and attitude towards the relationship between exogenous and endogenous constructs was tested. It shows a significant mediating impact of risk perception, environmental hazards and attitude towards risk on the nexus between tourism knowledge and travelers’ intention. The study offers valuable recommendations for policymakers to understand tourist intentions and climate vulnerability. Copyright © 2022 Yang, Fang, Ramirez-Asis, Alashker, Abourehab and Zhang.

9.
Otolaryngology Case Reports ; 24, 2022.
Article in English | EMBASE | ID: covidwho-1926981

ABSTRACT

Spontaneous perforation of the pharynx has been rarely reported in the literature. Cases of barotrauma from stifling a sneeze have been reported previously. Symptoms are often non-specific which makes diagnosis challenging. We report a case of 63-year-old man nearing completion of chemoradiotherapy for base of tongue squamous cell carcinoma who was referred after feeling a ‘tearing’ sensation in his neck after sneezing. This is the first case reported in the literature of oropharyngeal perforation secondary to a sneeze in a patient with a head and neck tumour undergoing radiotherapy.

10.
Journal of Transport Geography ; 102, 2022.
Article in English | Scopus | ID: covidwho-1907384

ABSTRACT

Seaports play an important role in the global shipping network. Shipping participants often attach great importance to the measurement of container port connectivity, as it reflects countries' access to world markets. As a result, various port connectivity index systems have been proposed by members of the shipping industry and scholars. In recent years, technological developments especially the advancement of high coverage and real-time Automatic Identification System (AIS) data, have provided a chance to improve the scope and frequency of the existing index systems. An improved system is expected to reflect the dynamic changes in a port's connectivity which may be induced by either local disruptions or shocks in the wider economy. This study builds a monthly container port connectivity index system by applying big data mining techniques, graph theory, and principal component analysis (PCA) to AIS data, taking both port factors and shipping network factors into consideration. AIS records from 2020 are used to calculate the connectivity score of 25 major container ports. We also compare our system with the connectivity index commonly used in the shipping industry, the Liner Shipping Connectivity Index (LSCI). Our results show that the measurement of connectivity can be improved over indices that depend primarily on indicators of traffic volume. Ports like Antwerp and Tanjung Pelepas rank high in the proposed system due to their sound performance on their accessibility and strategic position in the local region instead of their traffic volume. The monthly index system is also proven to reflect timely changes in the shipping industry through its accurate portrayal of changes in port connectivity during the COVID-19 outbreak. © 2022 Elsevier Ltd

11.
Economics of the Pandemic: Weathering the Storm and Restoring Growth ; : 184-202, 2021.
Article in English | Scopus | ID: covidwho-1893166

ABSTRACT

The impact on economic activities has come from the COVID-19 pandemic;therefore, the strength and the epidemiologic features of the pandemic have become the main factor that will influence the process and mode of resumption of work and production. The degree of the pandemic’s impact will determine the process of work and production resumption and the strength of the support policies taken. From the outbreak of COVID-19, the impact of the pandemic on economic activities and resumption of production and work may be roughly divided into three stages, described in the remainder of this section. When COVID-19 was basically controlled in China, priorities shifted to recovery of economic activities, but the increase of imported cases disrupted the original rhythm of work and production resumption in some districts and postponed the process to a certain degree. © 2021 selection and editorial matter, Cai Fang.

12.
Cognitive Computation ; : 16, 2022.
Article in English | Web of Science | ID: covidwho-1885505

ABSTRACT

Nowadays, the global COVID-19 situation is still serious, and the new mutant virus Delta has already spread all over the world. The chest X-ray is one of the most common radiological examinations for screening catheters and diagnosis of many lung diseases, which plays an important role in assisting clinical diagnosis during the outbreak. This study considers the problem of multi-label catheters and thorax disease classification on chest X-ray images based on computer vision. Therefore, we propose a new variant of pyramid vision Transformer for multi-label chest X-ray image classification, named MXT, which can capture both short and long-range visual information through self-attention. Especially, downsampling spatial reduction attention can reduce the resource consumption of using Transformer. Meanwhile, multi-layer overlap patch (MLOP) embedding is used to tokenize images and dynamic position feed forward with zero paddings can encode position instead of adding a positional mask. Furthermore, class token Transformer block and multi-label attention (MLA) are utilized to offer more effective processing of multi-label classification. We evaluate our MXT on Chest X-ray14 dataset which has 14 disease pathologies and Catheter dataset containing 11 types of catheter placement. Each image is labeled one or more categories. Compared with some state-of-the-art baselines, our MXT can yield the highest mean AUC score of 83.0% on the Chest X-ray14 dataset and 94.6% on the Catheter dataset. According to the ablation study, we can obtain the following results: (1) The proposed MLOP embedding has a better performance than overlap patch (OP) embedding layer and non-overlap patch (N-OP) embedding layer that the mean AUC score is improved 0.6% and 0.4%, respectively. (2) Our demonstrate dynamic position feed forward can replace the traditional position mask which can learn the position information, and the mean AUC increased by 0.6%. (3) The mean AUC score by the designed MLA is more 0.2% and 0.6% than using the class token and calculating the mean scores of all tokens. The comprehensive experiments on two datasets demonstrate the effectiveness of the proposed method for multi-label chest X-ray image classification. Hence, our MXT can assist radiologists in diagnoses of lung diseases and check the placement of catheters, which can reduce the work pressure of medical staff.

13.
26th International Conference on Research in Computational Molecular Biology, RECOMB 2022 ; 13278 LNBI:126-142, 2022.
Article in English | Scopus | ID: covidwho-1877748

ABSTRACT

Combinatorial group testing and compressed sensing both focus on recovering a sparse vector of dimensionality n from a much smaller number m< n of measurements. In the first approach, the problem is defined over the Boolean field – the goal is to recover a Boolean vector and measurements are Boolean;in the second approach, the unknown vector and the measurements are over the reals. Here, we focus on real-valued group testing setting that more closely fits modern testing protocols relying on quantitative measurements, such as qPCR, where the goal is recovery of a sparse, Boolean vector and the pooling matrix needs to be Boolean and sparse, but the unknown input signal vector and the measurement outcomes are nonnegative reals, and the matrix algebra implied in the test protocol is over the reals. With the recent renewed interest in group testing, focus has been on quantitative measurements resulting from qPCR, but the method proposed for sample pooling were based on matrices designed with Boolean measurements in mind. Here, we investigate constructing pooling matrices dedicated for the real-valued group testing. We provide conditions for pooling matrices to guarantee unambiguous decoding of positives in this setting. We also show a deterministic algorithm for constructing matrices meeting the proposed condition, for small matrix sizes that can be implemented using a laboratory robot. Using simulated data, we show that the proposed approach leads to matrices that can be applied for higher positivity rates than combinatorial group testing matrices considered for viral testing previously. We also validate the approach through wet lab experiments involving SARS-CoV-2 nasopharyngeal swab samples. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

15.
Nature Machine Intelligence ; 2022.
Article in English | Scopus | ID: covidwho-1805663

ABSTRACT

In the version of this article initially published, the first name of Chuansheng Zheng was misspelled as Chuangsheng. The error has been corrected in the HTML and PDF versions of the article. © The Author(s) 2022.

16.
PubMed; 2020.
Preprint in English | PubMed | ID: ppcovidwho-333508

ABSTRACT

BACKGROUND: Serological tests are crucial tools for assessments of SARS-CoV-2 exposure, infection and potential immunity. Their appropriate use and interpretation require accurate assay performance data. METHOD: We conducted an evaluation of 10 lateral flow assays (LFAs) and two ELISAs to detect anti-SARS-CoV-2 antibodies. The specimen set comprised 128 plasma or serum samples from 79 symptomatic SARS-CoV-2 RT-PCR-positive individuals;108 pre-COVID-19 negative controls;and 52 recent samples from individuals who underwent respiratory viral testing but were not diagnosed with Coronavirus Disease 2019 (COVID-19). Samples were blinded and LFA results were interpreted by two independent readers, using a standardized intensity scoring system. RESULTS: Among specimens from SARS-CoV-2 RT-PCR-positive individuals, the percent seropositive increased with time interval, peaking at 81.8-100.0% in samples taken >20 days after symptom onset. Test specificity ranged from 84.3-100.0% in pre-COVID-19 specimens. Specificity was higher when weak LFA bands were considered negative, but this decreased sensitivity. IgM detection was more variable than IgG, and detection was highest when IgM and IgG results were combined. Agreement between ELISAs and LFAs ranged from 75.7-94.8%. No consistent cross-reactivity was observed. CONCLUSION: Our evaluation showed heterogeneous assay performance. Reader training is key to reliable LFA performance, and can be tailored for survey goals. Informed use of serology will require evaluations covering the full spectrum of SARS-CoV-2 infections, from asymptomatic and mild infection to severe disease, and later convalescence. Well-designed studies to elucidate the mechanisms and serological correlates of protective immunity will be crucial to guide rational clinical and public health policies.

17.
Working Paper Series National Bureau of Economic Research ; 45, 2021.
Article in English | GIM | ID: covidwho-1745153

ABSTRACT

Can informing people of high community support for social distancing encourage them to do more of it? In theory, the impact of such an intervention on social distancing is ambiguous, and depends on the relative magnitudes of free-riding and perceived-infectiousness effects. We randomly assigned a treatment providing information on true high rates of community social distancing support. We estimate impacts on social distancing, measured using a combination of self-reports and reports of others. While experts surveyed in advance expected the treatment to increase social distancing, we find that its average effect is close to zero and significantly lower than expert predictions. The treatment's effect is heterogeneous, as predicted by theory: it decreases social distancing where current COVID-19 cases are low (where free-riding dominates), but increases it where cases are high (where the perceived-infectiousness effect dominates).

18.
13th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021 ; : 90-94, 2021.
Article in English | Scopus | ID: covidwho-1705849

ABSTRACT

The spread of COVID-19 has sparked racism and hate on social media targeted towards Asian communities. However, little is known about how racial hate spreads during a pandemic and the role of counterspeech in mitigating this spread. In this work, we study the evolution and spread of anti-Asian hate speech through the lens of Twitter. We create COVID-HATE, the largest dataset of anti-Asian hate and counterspeech spanning 14 months, containing over 206 million tweets, and a social network with over 127 million nodes. By creating a novel hand-labeled dataset of 3,355 tweets, we train a text classifier to identify hateful and counterspeech tweets that achieves an average macro-F1 score of 0.832. Using this dataset, we conduct longitudinal analysis of tweets and users. Analysis of the social network reveals that hateful and counterspeech users interact and engage extensively with one another, instead of living in isolated polarized communities. We find that nodes were highly likely to become hateful after being exposed to hateful content in the year 2020. Notably, counterspeech messages discourage users from turning hateful, potentially suggesting a solution to curb hate on web and social media platforms. Data and code is available at http://claws.cc.gatech.edu/covid. © 2021 ACM.

19.
European Respiratory Journal ; 58:2, 2021.
Article in English | Web of Science | ID: covidwho-1704115
20.
Embase;
Preprint in English | EMBASE | ID: ppcovidwho-327049

ABSTRACT

Despite the efficacy of vaccines, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has killed over 5 million individuals worldwide and continues to spread in countries where the vaccines are not yet widely available or its citizens are hesitant to become vaccinated. Therefore, it is critical to unravel the molecular mechanisms that allow SARS-CoV-2 and other coronaviruses to infect and overtake the host machinery of human cells. Coronavirus replication triggers endoplasmic reticulum (ER) stress and activation of the unfolded protein response (UPR), a key host cell pathway widely believed essential for viral replication. We examined the activation status and requirement of the master UPR sensor IRE1a kinase/RNase and its downstream transcription factor effector XBP1s, which is processed through an IRE1a-mediated mRNA splicing event, in human lung-derived cells infected with betacoronaviruses. We found human respiratory coronavirus OC43 (HCoV-OC43), Middle East respiratory syndrome coronavirus (MERS-CoV), and the murine coronavirus (MHV) all induce ER stress and strongly trigger the kinase and RNase activities of IRE1a as well as XBP1 splicing. In contrast, SARS-CoV-2 only partially activates IRE1a whereby it autophosphorylates, but its RNase fails to splice XBP1. Moreover, IRE1a was dispensable for optimal replication in human cells for all coronaviruses tested. Our findings demonstrate that IRE1a activation status differs upon infection with distinct betacoronaviruses and is not essential for efficient replication of any of them. Our data suggest that SARS-CoV-2 actively inhibits the RNase of autophosphorylated IRE1a through an unknown mechanism, perhaps as a strategy to eliminate detection by the host immune system.

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