PANACEA: An Automated Misinformation Detection System on COVID-19

In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection. Our fact-checking module, which is supported by novel natural language inference methods with a self-attention network, outperforms state-of-the-art approaches. It is also able to give automated veracity assessment and ranked supporting evidence with the stance towards the claim to be checked. In addition, PANACEA adapts the bi-directional graph convolutional networks model, which is able to detect rumours based on comment networks of related tweets, instead of relying on the knowledge base. This rumour detection module assists by warning the users in the early stages when a knowledge base may not be available. © 2023 Association for Computational Linguistics.

A Dynamic-Static Dual Input Deep Neural Network Algorithm for Diagnosing COVID-19 by Cough

The COVID-19 (corona virus disease 2019) has caused serious impacts worldwide. Many scholars have done a lot of research on the prevention and control of the epidemic. The diagnosis of COVID-19 by cough is non-contact, low-cost, and easy-access, however, such research is still relatively scarce in China. Mel frequency cepstral coefficients (MFCC) feature can only represent the static sound feature, while the first-order differential MFCC feature can also reflect the dynamic feature of sound. In order to better prevent and treat COVID-19, the paper proposes a dynamic-static dual input deep neural network algorithm for diagnosing COVID-19 by cough. Based on Coswara dataset, cough audio is clipped, MFCC and first-order differential MFCC features are extracted, and a dynamic and static feature dual-input neural network model is trained. The model adopts a statistic pooling layer so that different length of MFCC features can be input. The experiment results show the proposed algorithm can significantly improve the recognition accuracy, recall rate, specificity, and F1-score compared with the existing models. © 2023 Chinese Institute of Electronics. All rights reserved.

Politics Go "Viral": A Computational Text Analysis of the Public Attribution and Attitude Regarding the COVID-19 Crisis and Governmental Responses on Twitter

The U.S. confronts an unprecedented public health crisis, the COVID-19 pandemic, in the presidential election year in 2020. In such a compound situation, a real-time dynamic examination of how the general public ascribe the crisis responsibilities taking account to their political ideologies is helpful for developing effective strategies to manage the crisis and diminish hostility toward particular groups caused by polarization. Social media, such as Twitter, provide platforms for the public's COVID-related discourse to form, accumulate, and visibly present. Meanwhile, those features also make social media a window to monitor the public responses in real-time. This research conducted a computational text analysis of 2,918,376 tweets sent by 829,686 different U.S. users regarding COVID-19 from January 24 to May 25, 2020. Results indicate that the public's crisis attribution and attitude toward governmental crisis responses are driven by their political identities. One crisis factor identified by this study (i.e., threat level) also affects the public's attribution and attitude polarization. Additionally, we note that pandemic fatigue was identified in our findings as early as in March 2020. This study has theoretical, practical, and methodological implications informing further health communication in a heated political environment. [ FROM AUTHOR] Copyright of Social Science Computer Review is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Spatial Interactions and the Spread of COVID-19: A Network Perspective

We use unique data on the travel history of confirmed patients at a daily frequency across 31 provinces in China to study how spatial interactions influence the geographic spread of pandemic COVID-19. We develop and simultaneously estimate a structural model of dynamic disease transmission network formation and spatial interaction. This allows us to understand what externalities the disease risk associated with a single place may create for the entire country. We find a positive and significant spatial interaction effect that strongly influences the duration and severity of pandemic COVID-19. And there exists heterogeneity in this interaction effect: the spatial spillover effect from the source province is significantly higher than from other provinces. Further counterfactual policy analysis shows that targeting the key province can improve the effectiveness of policy interventions for containing the geographic spread of pandemic COVID-19, and the effect of such targeted policy decreases with an increase in the time of delay.

Explainable artificial intelligence approach towards classifying educational android app reviews using deep learning

Mobile application developers rely largely on user reviews for identifying issues in mobile applications and meeting the users' expectations. User reviews are unstructured, unorganized and very informal. Identifying and classifying issues by extracting required information from reviews is difficult due to a large number of reviews. To automate the process of classifying reviews many researchers have adopted machine learning approaches. Keeping in view, the rising demand for educational applications, especially during COVID-19, this research aims to automate Android application education reviews' classification and sentiment analysis using natural language processing and machine learning techniques. A baseline corpus comprising 13,000 records has been built by collecting reviews of more than 20 educational applications. The reviews were then manually labelled with respect to sentiment and issue types mentioned in each review. User reviews are classified into eight categories and various machine learning algorithms are applied to classify users' sentiments and issues of applications. The results demonstrate that our proposed framework achieved an accuracy of 97% for sentiment identification and an accuracy of 94% in classifying the most significant issues. Moreover, the interpretability of the model is verified by using the explainable artificial intelligence technique of local interpretable model-agnostic explanations. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Large-Scale Analysis of New Employee Network Dynamics

The COVID-19 pandemic has accelerated digital transformations across industries, but also introduced new challenges into workplaces, including the difficulties of effectively socializing with colleagues when working remotely. This challenge is exacerbated for new employees who need to develop workplace networks from the outset. In this paper, by analyzing a large-scale telemetry dataset of more than 10,000 Microsoft employees who joined the company in the first three months of 2022, we describe how new employees interact and telecommute with their colleagues during their "onboarding"period. Our results reveal that although new hires are gradually expanding networks over time, there still exists significant gaps between their network statistics and those of tenured employees even after the six-month onboarding phase. We also observe that heterogeneity exists among new employees in how their networks change over time, where employees whose job tasks do not necessarily require extensive and diverse connections could be at a disadvantaged position in this onboarding process. By investigating how web-based people recommendations in organizational knowledge base facilitate new employees naturally expand their networks, we also demonstrate the potential of web-based applications for addressing the aforementioned socialization challenges. Altogether, our findings provide insights on new employee network dynamics in remote and hybrid work environments, which may help guide organizational leaders and web application developers on quantifying and improving the socialization experiences of new employees in digital workplaces. © 2023 ACM.

The spread of COVID-19 in stock returns along global value chains

This paper examines the performance of industries in the trade network in international stock markets during the onset of COVID-19. In general, the value of all industries in G20 countries declines significantly in the pandemic. Stock returns of industries in the central positions of global value chains exhibit remarkable resilience despite the economic hardship caused by COVID-19. This pattern is more pronounced when the disruptions caused by social distancing requirements are considered. We postulate that this is related to the essential services provided by the central industries.

Telemedicine in the Era of Covid-19: Teleconsultation Architecture Platform

Teleconsultation is a type of medical practice similar to face-to-face consultations, and it allows a health professional to give a consultation remotely through information and communication technologies. In the context of the management of the coronavirus epidemic, the use of teleconsultation practices can facilitate healthcare access and limit the risk of avoidable propagation in medical cabinets. This paper presents the monitoring of international teleconsultation referrals in the era of Covid-19 to facilitate and prevent the suspension of access to care, the most common architecture for teleconsultation, communication technologies and protocols, vital body signals, video transmission, and the conduct of teleconsultation. The aim is to develop a teleconsultation platform to diagnose the patient in real time, transmit data from the remote location to the doctor, and provide a teleconsultation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Residual Feature Attentional Fusion Network for Lightweight Chest CT Image Super-Resolution

The diagnosis of COVID-19 requires chest computed tomography (CT). High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease, so it is of clinical importance to study super-resolution (SR) algorithms applied to CT images to improve the reso-lution of CT images. However, most of the existing SR algorithms are studied based on natural images, which are not suitable for medical images;and most of these algorithms improve the reconstruction quality by increasing the network depth, which is not suitable for machines with limited resources. To alleviate these issues, we propose a residual feature attentional fusion network for lightweight chest CT image super-resolution (RFAFN). Specifically, we design a contextual feature extraction block (CFEB) that can extract CT image features more efficiently and accurately than ordinary residual blocks. In addition, we propose a feature-weighted cascading strategy (FWCS) based on attentional feature fusion blocks (AFFB) to utilize the high-frequency detail information extracted by CFEB as much as possible via selectively fusing adjacent level feature information. Finally, we suggest a global hierarchical feature fusion strategy (GHFFS), which can utilize the hierarchical features more effectively than dense concatenation by progressively aggregating the feature information at various levels. Numerous experiments show that our method performs better than most of the state-of-the-art (SOTA) methods on the COVID-19 chest CT dataset. In detail, the peak signal-to-noise ratio (PSNR) is 0.11 dB and 0.47 dB higher on CTtest1 and CTtest2 at x3 SR compared to the suboptimal method, but the number of parameters and multi-adds are reduced by 22K and 0.43G, respectively. Our method can better recover chest CT image quality with fewer computational resources and effectively assist in COVID-19.

Study on Infection Probabilities of COVID-19 by SIR Network

Severe Acute Respiratory Syndrome Coronavirus 2 Related Diseases (COVID-19) is now one of the most challenging and concerning epidemics, which has been affecting the world so much. After that, countries around the world have been actively developing vaccines to deal with the sudden disease. How to carry out more efficient epidemic prevention has also become a problem of our concern. Unlike traditional SIR disease transmission models, network percolation has unique advantages in disease immune modelling, which makes it closer to reality in the simulation. This article introduces the study of SIR percolation network on infection probabilities of COVID-19, and proposes a method to preventing the spread of disease. © 2022 IEEE.

Monitoring and Early Warning of SMEs' Shutdown Risk under the Impact of Global Pandemic Shock

The COVID-19 outbreak devastated business operations and the world economy, especially for small and medium-sized enterprises (SMEs). With limited capital, poorer risk tolerance, and difficulty in withstanding prolonged crises, SMEs are more vulnerable to pandemics and face a higher risk of shutdown. This research sought to establish a model response to shutdown risk by investigating two questions: How do you measure SMEs' shutdown risk due to pandemics? How do SMEs reduce shutdown risk? To the best of our knowledge, existing studies only analyzed the impact of the pandemic on SMEs through statistical surveys and trivial recommendations. Particularly, there is no case study focusing on an elaboration of SMEs' shutdown risk. We developed a model to reduce cognitive uncertainty and differences in opinion among experts on COVID-19. The model was built by integrating the improved Dempster's rule of combination and a Bayesian network, where the former is based on the method of weight assignment and matrix analysis. The model was first applied to a representative SME with basic characteristics for survival analysis during the pandemic. The results show that this SME has a probability of 79% on a lower risk of shutdown, 15% on a medium risk of shutdown, and 6% of high risk of shutdown. SMEs solving the capital chain problem and changing external conditions such as market demand are more difficult during a pandemic. Based on the counterfactual elaboration of the inferred results, the probability of occurrence of each risk factor was obtained by simulating the interventions. The most likely causal chain analysis based on counterfactual elaboration revealed that it is simpler to solve employee health problems. For the SMEs in the study, this approach can reduce the probability of being at high risk of shutdown by 16%. The results of the model are consistent with those identified by the SME respondents, which validates the model.

COVID-19 Image Segmentation Algorithms Based on Conditional Generative Adversarial Network

The size and location of the lesions in CT images of novel corona virus pneumonia (COVID-19) change all the time, and the lesion areas have low contrast and blurred boundaries, resulting in difficult segmentation. To solve this problem, a COVID-19 image segmentation algorithm based on conditional generative adversarial network (CGAN) is proposed. Uses the improved DeeplabV3+ network as a generator, which enhances the extraction of multi-scale contextual features, reduces the number of network parameters and improves the training speed. A Markov discriminator with 6 fully convolutional layers is proposed instead of a common discriminator, with the aim of focusing more on the local features of the CT image. By continuously adversarial training between the generator and the discriminator, the network weights are optimised so that the final segmented image generated by the generator is infinitely close to the ground truth. On the COVID-19 CT public dataset, the area under the curve of ROC, F1-Score and dice similarity coefficient achieved 96.64%, 84.15% and 86.14% respectively. The experimental results show that the proposed algorithm is accurate and robust, and it has the possibility of becoming a safe, inexpensive, and time-saving medical assistant tool in clinical diagnosis, which provides a reference for computer-aided diagnosis. © 2022 ACM.

A Hybrid Brain Storm Optimization Algorithm to Solve the Emergency Relief Routing Model

Due to the inappropriate or untimely distribution of post-disaster goods, many regions did not receive timely and efficient relief for infected people in the coronavirus disease outbreak that began in 2019. This study develops a model for the emergency relief routing problem (ERRP) to distribute post-disaster relief more reasonably. Unlike general route optimizations, patients' suffering is taken into account in the model, allowing patients in more urgent situations to receive relief operations first. A new metaheuristic algorithm, the hybrid brain storm optimization (HBSO) algorithm, is proposed to deal with the model. The hybrid algorithm adds the ideas of the simulated annealing (SA) algorithm and large neighborhood search (LNS) algorithm into the BSO algorithm, improving its ability to escape from the local optimum trap and speeding up the convergence. In simulation experiments, the BSO algorithm, BSO+LNS algorithm (combining the BSO with the LNS), and HBSO algorithm (combining the BSO with the LNS and SA) are compared. The results of simulation experiments show the following: (1) The HBSO algorithm outperforms its rivals, obtaining a smaller total cost and providing a more stable ability to discover the best solution for the ERRP;(2) the ERRP model can greatly reduce the level of patient suffering and can prioritize patients in more urgent situations.

Dynamic Safety Assessment and Enhancement of Port Operational Infrastructure Systems during the COVID-19 Era

Seaports function as lifeline systems in maritime transportation, facilitating critical processes like shipping, distribution, and allied cargo handling. These diverse subsystems constitute the Port Infrastructure System (PIS) and have intricate functional interdependencies. The PIS is vulnerable to several external disruptions, and the impact of COVID-19 is severe and unprecedented in this domain. Therefore, this study proposes a novel general port safety framework to cope with recurring hazards and crisis events like COVID-19 and to augment PIS safety through a multi-state failure system. The PIS is divided into three critical subsystems: shipping, terminal, and distribution infrastructure, thereby capturing its functional interdependency and intricacy. A dynamic input-output model is employed, incorporating the spatial variability and average delay of the disruption, to determine the PIS resilience capacity under the stated disruptions. This study simulates three disruption scenarios and determines the functional failure capacity of the system by generating a functional change curve in Simulink. This study offers viable solutions to port managers, terminal operators, and concerned authorities in the efficient running of intricate interdependent processes and in devising efficient risk control measures to enhance overall PIS resilience and reliability. As part of future studies, given the difficulty in obtaining relevant data and the relatively limited validation of the current model, we aim to improve the accuracy and reliability of our model and enhance its practical applicability to real-world situations with data collected from a real-world case study of a PIS system.

Hashtag framing and stakeholder targeting: An affordance perspective on China's digital public diplomacy campaign during COVID-19

Social media platforms such as Twitter provide opportunities for governments to connect to foreign publics and influence global public opinion. In the current study, we used social and semantic network analysis to investigate China's digital public diplomacy campaign during COVID-19. Our results show that Chinese state-affiliated media and diplomatic accounts created hashtag frames and targeted stakeholders to challenge the United States or to cooperate with other countries and international organizations, especially the World Health Organization. Telling China's stories was the central theme of the digital campaign. From the perspective of social media platform affordance, we addressed the lack of attention paid to hashtag framing and stakeholder targeting in the public diplomacy literature. [ FROM AUTHOR] Copyright of Journal of Information Technology & Politics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The Application of PageRank model in urban epidemic prevention and control

The ongoing COVID-19 epidemic has had a great impact on social activities and the economy. The usage technical analysis tools to provide a more accurate and efficient reference for epidemic control measures is of great significance. This paper analyzes the characteristics and deficiencies of the existing technical methods, such as regression model, simulation calculation, differential equation and so on. By analyzing past outbreak cases and comparing the epidemic prevention measures of different cities, we discuss the importance of early and timely prevention in controlling the epidemic, and the importance of analyzing and formulating plans in advance. We then make the key observation that the spread of the virus is related to the topology of the urban network. This paper further proposes an epidemic analysis model of the optimized PageRank model, and gives a ranking algorithm for virus transmission risk levels based on road nodes, forming a visual risk warning level map, and applies the algorithm to the epidemic analysis of Yuegezhuang area in Beijing. Finally, more in-depth research directions and suggestions for prevention and control measures are put forward. © 2023 SPIE.

Numerical Simulation of the COVID-19 Herd Immunity Based on Complex Network Modeling

Constructing an epidemic dynamic model and exploring the spreading law of epidemic have very important theoretical significance for epidemic prevention and control. Based on the existing homogeneous mixing model, in view of the increasingly obvious heterogeneity of individual contact relationships, and each individual is in a different contact relationship, a dynamic small-world network model that takes into account individual status. Contact tracking has been established to simulate the spread of the COVID-19 in society. By comparing the simulation results, the rationality of the built model is explained. On this basis, the simulation calculated the impact of the network topology and the proportion of vaccinated people on the spread of the COVID-19, analyzed the critical value of herd immunity. The established propagation model is reasonable, and feasible to achieve herd immunization by vaccination. © 2023 Editorial Borad of Complex Systems and Complexity Science. All rights reserved.

Resilience and recovery of public transport use during COVID-19

To better understand how public transport use varied during the first year of COVID-19, we define and measure travel behavior resilience. With trip records between November 2019 and September 2020 in Kunming, China, we identify people who relied on traveling by subway both before and after the first pandemic wave. We investigate whether and how travelers recover to their pre-pandemic mobility level. We find that public transport use recovered slowly, as urban mobility is a result of urban functionality, transport supply, social context, and inter-personal differences. In general, urban mobility represents a strengthened revisiting tendency during COVID-19, as individual's trips occur within a more limited space. We confirm that travel behavior resilience differs by groups. Commuters recover travel frequency and length, while older people decrease frequency but retain activity space. The study suggests that policymakers take group heterogeneity and travel behavior resilience into account for transport management and city restoration.

Study on potential common action and mechanism of Reduning Injection against SARS, MERS and COVID-19 based on network pharmacology and molecular docking technology.

Objective To explore the potential common mechanism and active ingredients of Reduning Injection against SARS, MERS and COVID-19 through network pharmacology and molecular docking technology. Methods The TCMSP database was used to retrieve the chemical components and targets of Artemisiae Annuae Herba, Lonicerae Japonicae Flos and Gardeniae Fructus in Reduning Injection. The gene corresponding to the target was searched by UniProt database, and Cytoscape 3.8.2 was used to build a medicinal material-compound-target (gene) network. Three coronavirus-related targets were collected in the Gene Cards database with the key words of "SARS""MERS" and "COVID-19", and common target of three coronavirus infection diseases were screened out through Venny 2.1.0 database. The common targets of SARS, MERS and COVID-19 were intersected with the targets of Reduning Injection, and the common targets were selected as research targets. Protein-protein interaction (PPI) network map were constructed by Cytoscape3.8.2 software after importing the common targets into the STRING database to obtain data. R language was used to carry out GO biological function enrichment analysis and KEGG signaling pathway enrichment analysis, histograms and bubble charts were drew, and component-target-pathway network diagrams was constructed. The key compounds in the component-target-pathway network were selected for molecular docking with important target proteins, novel coronavirus (SARS-CoV-2) 3CL hydrolase, and angiotensin-converting enzyme II (ACE2). Results 31 active compounds and 207 corresponding targets were obtained from Reduning Injection. 2 453 SARS-related targets, 805 MERS-related targets, 2 571 COVID-19-related targets, and 786 targets for the three diseases. 11 common targets with Reduning Injection: HSPA5, CRP, MAPK1, HMOX1, TGFB1, HSP90AA1, TP53, DPP4, CXCL10, PLAT, PRKACA. GO function enrichment analysis revealed 995 biological processes (BP), 71 molecular functions (MF), and 31 cellular components (CC). KEGG pathway enrichment analysis screened 99 signal pathways (P < 0.05), mainly related to prostate cancer, fluid shear stress and atherosclerosis, hepatocellular carcinoma, proteoglycans in cancer, lipid and atherosclerosis, human T-cell leukemia virus 1 infection, MAPK signaling pathway, etc. The molecular docking results showed that the three core active flavonoids of quercetin, luteolin, and kaempferol in Reduning Injection had good affinity with key targets MAPK1, PRKACA, and HSP90AA1, and the combination of the three active compounds with SARS-CoV-2 3CL hydrolase and ACE2 was less than the recommended chemical drugs. Conclusion Reduning Injection has potential common effects on the three diseases of SARS, MERS and COVID-19. This effect may be related to those active compounds such as quercetin, luteolin, and kaempferol acting on targets such as MAPK1, PRKACA, HSP90AA1 to regulate multiple signal pathways and exert anti-virus, suppression of inflammatory storm, and regulation of immune function.Copyright © 2022 Drug Evaluation Research. All rights reserved.