Sustainable response strategy for COVID-19: Pandemic zoning with urban multimodal transport data.

In the post-COVID-19 era, the pandemic response is increasingly difficult and entails a high cost to society. Existing pandemic control methods, such as lockdowns, greatly affect residents' normal lives. This paper proposes a pandemic control method, consisting of the scientific delineation of urban areas based on multimodal transportation data. An improved Leiden method based on the gravity model is used to construct a preliminary zoning scheme, which is then modified by spatial constraints. The modularity index demonstrates the suitability of this method for community detection. This method can minimize cut-off traffic flows between pandemic control areas. The results show that only 24.8% of travel links are disrupted using our method, which could reduce both the impact of pandemic control on the daily life of residents and its cost. These findings can help develop sustainable strategies and proposals for effective pandemic response.

Information retrieval and extraction on COVID-19 clinical articles using graph community detection and Bio-BERT embeddings

In this paper, we present an information retrieval system on a corpus of scientific articles related to COVID-19. We build a similarity network on the articles where similarity is determined via shared citations and biological domain-specific sentence embeddings. Ego-splitting community detection on the article network is employed to cluster the articles and then the queries are matched with the clusters. Extractive summarization using BERT and PageRank methods is used to provide responses to the query. We also provide a Question-Answer bot on a small set of intents to demonstrate the efficacy of our model for an information extraction module. © ACL 2020.All right reserved.

Disentangling the Information Flood on OSNs: Finding Notable Posts and Topics

Online Social Networks (OSN s) are an integral part of modern life for sharing thoughts, stories, and news. An ecosystem of influencers generates a flood of content in the form of posts, some of which have an unusually high level of engagement with the influencer's fan base. These posts relate to blossoming topics of discussion that generate particular interest among users: The COVID-19 pandemic is a prominent example. Studying these phenomena provides an understanding of the OSN landscape and requires appropriate methods. This paper presents a methodology to discover notable posts and group them according to their related topic. By combining anomaly detection, graph modelling and community detection techniques, we pinpoint salient events automatically, with the ability to tune the amount of them. We showcase our approach using a large Instagram dataset and extract some notable weekly topics that gained momentum from 1.4 million posts. We then illustrate some use cases ranging from the COVID-19 outbreak to sporting events. © 2022 IEEE.

Contact Tracing Platform in OSN for Prevention of Infectious Disease Outbreaks

To limit the spread of COVID-19, social distancing measurements and contact tracing have become popular strategies implemented worldwide. In addition to manual contact tracing, smartphone-based applications based on proximity detection have emerged to speed up the discovery of potential infectious individuals. However, so far, their effectiveness has been limited, mainly due to privacy issues. A new tracing mechanism is represented by Online Social Networks (OSNs), which provide a successful way to track, share and exchange information in real-time. Being extremely popular and largely used by citizens, OSNs are less exposed to privacy concerns. In this paper, we present an OSN-based contact tracing platform called TraceMe to reduce the spread of the epidemic. The proposal currently targets COVID-19, but it can be used in presence of other infectious diseases, like Ebola, swine flue, etc. TraceMe implements conventional contact tracing based on physical proximity and, in addition, it leverages OSNs to identify other contacts potentially exposed to the virus. To efficiently find the targeted social community, while saving the time complexity, a clique-based method is applied. Performance evaluation based on a realistic dataset shows that TraceMe is able to analyse large-scale social networks in order to find, and then alert, the tight communities of contacts that are at high risk of infection. © 2023 IEEE.

Driving Factors of Polarization on Twitter During Protests Against COVID-19 Mitigation Measures in Vienna

We conduct the analysis of the Twitter discourse related to the anti-lockdown and anti-vaccination protests during the so-called 4th wave of COVID-19 infections in Austria (particularly in Vienna). We focus on predicting users' protest activity by leveraging machine learning methods and individual driving factors such as language features of users supporting/opposing Corona protests. For evaluation of our methods we utilize novel datasets, collected from discussions about a series of protests on Twitter (40488 tweets related to 20.11.2021;7639 from 15.01.2022 – the two biggest protests as well as 192 from 22.01.2022;8412 from 11.12.2021;3945 from 11.02.2022). We clustered users via the Louvain community detection algorithm on a retweet network into pro- and anti-protest classes. We show that the number of users engaged in the discourse and the share of users classified as pro-protest are decreasing with time. We have created language-based classifiers for single tweets of the two protest sides – random forest, neural networks and a regression-based approach. To gain insights into language-related differences between clusters we also investigated variable importance for a word-list-based modeling approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Enhanced link prediction using sentiment attribute and community detection.

In social network analysis, link prediction is an important area where the researchers can find the missing links and the future links possible among the users. Often, link prediction is made by analyzing the social linkage of the users in the given networks, i.e., the Topological structure of the networks. However, this approach leads to inconsistencies when researchers want to emphasize topics on which users have mainly engaged their selves in discussions. Mainly, this approach predicts future links based on available network structures without considering the topics on which the users are participating. This can be enhanced by incorporating the sentiment attributes and the community structure of the users in the network. In this paper, we propose an algorithm that incorporates the sentiment attribute of users and community structures along with the topological features. To evaluate the same, we have crawled the tweets of various countries concerning COVID-19 from Twitter. Experimental results show that users exhibiting the same emotion and belonging to the same community will influence other users to connect, thereby improving the performance of the link prediction.

Telemedicine as technoinnovation to tackle COVID-19: A bibliometric analysis

Telemedicine has become fundamental for the challenges posed to healthcare. This set of instruments turns pivotal for facing one of the most relevant emergencies in human history: the COVID-19 pandemic. The multisectoral crisis led to a vigorously sustained adoption of innovations, including telemedicine technology. Telehealth was proven, in this context, to be a relevant tool to reduce healthcare costs, reduce not-needed hospitalizations, and improve the results in health care. Some barriers such as the costs of technologies, patient privacy and technical literacy have slowed down telemedicine adoption. Amidst the COVID-19 era, telemedicine calls for a managerial duty to change healthcare's organizational models. The present work aims to explore the growing literature to illuminate the relationships between telemedicine, innovations and healthcare in the COVID-19 framework. A bibliometric analysis of the existing literature based on 285 published works in 2019–2020 is put forward with the aim to detect the relevant literature, themes and approaches on telemedicine and COVID-19. Making use of community detection on the co-occurrence keywords network, we identify the "semantic cores” in the literature representing the relevant results on critical themes. The sorting implications are important for researchers and policymakers by mapping the existing literature and results in evidence-based analysis. We provide the key communities as the "semantic core” of the publications and results for the considered period. This allows for future research to be oriented towards perduring health policies that could lead to the adoption of telemedicine technologies in a post-pandemic scenario. © 2021

Early detection of SARS-CoV-2 variants through dynamic co-mutation network surveillance.

Background: Precise public health and clinical interventions for the COVID-19 pandemic has spurred a global rush on SARS-CoV-2 variant tracking, but current approaches to variant tracking are challenged by the flood of viral genome sequences leading to a loss of timeliness, accuracy, and reliability. Here, we devised a new co-mutation network framework, aiming to tackle these difficulties in variant surveillance. Methods: To avoid simultaneous input and modeling of the whole large-scale data, we dynamically investigate the nucleotide covarying pattern of weekly sequences. The community detection algorithm is applied to a co-occurring genomic alteration network constructed from mutation corpora of weekly collected data. Co-mutation communities are identified, extracted, and characterized as variant markers. They contribute to the creation and weekly updates of a community-based variant dictionary tree representing SARS-CoV-2 evolution, where highly similar ones between weeks have been merged to represent the same variants. Emerging communities imply the presence of novel viral variants or new branches of existing variants. This process was benchmarked with worldwide GISAID data and validated using national level data from six COVID-19 hotspot countries. Results: A total of 235 co-mutation communities were identified after a 120 weeks' investigation of worldwide sequence data, from March 2020 to mid-June 2022. The dictionary tree progressively developed from these communities perfectly recorded the time course of SARS-CoV-2 branching, coinciding with GISAID clades. The time-varying prevalence of these communities in the viral population showed a good match with the emergence and circulation of the variants they represented. All these benchmark results not only exhibited the methodology features but also demonstrated high efficiency in detection of the pandemic variants. When it was applied to regional variant surveillance, our method displayed significantly earlier identification of feature communities of major WHO-named SARS-CoV-2 variants in contrast with Pangolin's monitoring. Conclusion: An efficient genomic surveillance framework built from weekly co-mutation networks and a dynamic community-based variant dictionary tree enables early detection and continuous investigation of SARS-CoV-2 variants overcoming genomic data flood, aiding in the response to the COVID-19 pandemic.

Research on COVID-19 network public opinion events based on topic model and community detection algorithm

COVID-19 has caused a large number of online public opinion incidents. How to timely and effectively guide the resulting network public opinion has become an urgent problem to be solved. This paper collects more than 130,000 original Weibo posts during the Wuhan "city closure” incident, and analyses the topic characteristics of the incident on the basis of user classification through topic models and community detection algorithms. It was found that during this period, the government responded quickly to the epidemic and gained public support. For different Weibo users, officially certified users mainly publish information about the epidemic and epidemic prevention measures. Personally certified users mainly forwarded and transmitted official information actively, and they also expressed their opinions and made suggestions. Non-certified users actively expressed their emotions and opinions, so they were important users that reflect public opinion. © 2022 SPIE.

Extracting the Relationships Among Students Based on Accessing Pattern of Digital Learning Attributes

A paradigm shift can be expected in the education sector, especially after the COVID-19 pandemic. E-learning systems are being adopted by all the stakeholders as physical meetings are not feasible. Different online learning attributes, such as video conferencing tools, coding platforms, online learning frameworks, digital books, and online videos, are available, which are enhancing the traditional learning methodology. Now, the main challenge for the educationists is to identify how these attributes are utilized by the learners. In this article, we have represented any online class as a social network where students are connected through learning platforms. We have mined the network based on several network measuring parameters to recognize the maneuvering pattern of these digital resources or attributes by the students. We have also proposed a community detection method that would form different groups among the students based on their comfortable learning patterns. As a case study, we have scrutinized the accessing patterns of different digital learning resources by some particular students. The experimental results show the significant relationship between the digital resource accessing patterns of the students with their immediate performance in the test. The satisfactory inquisitive results of our approach would definitely inspire the researchers of interdisciplinary areas to probe further in this domain.

PUBLIC OPINION, CLUSTERS, AND STATISTICS OF COVID-19 VACCINATION: A TWITTER ANALYSIS

The coronavirus disease (COVID-19) pandemic has drastically affected the entire world. Vaccinations have been developed to contain the spread of the virus and help humanity recover from the pandemic. However, people are often reluctant to adopt new medical interventions, and COVID-19 vaccines are no exception. Social media platforms like Twitter are flooded with discussions, opinions, and rumors about vaccines. Numerous people access news regarding COVID-19 vaccines on Twitter-rather than through official media channels-which offers a wealth of comments about news and medical breakthroughs. However, as people are panicked due to this alarming situation, they tend to share any information they receive without checking its credibility, creating more panic. Analyzing interaction between social media users provides insight into the spreading pattern of news, people's opinions towards a particular issue, who the influencers are, and how they influence others, among other findings. This study investigated opinions about COVID-19 vaccines by applying sentiment analysis and detecting communities on Twitter. We constructed an interaction network of discussions related to COVID-19 vaccines and applied social network analysis methods to find communities and nodes with high centrality measures. Next, we analyzed how these nodes affect the overall community opinion. Two main communities were detected, with the larger community displaying a higher positive sentiment ratio than the smaller one. Furthermore, the polarity of the high centrality nodes in each community was close to the average polarity of the community as a whole. These findings highlight the potency of the node's position in terms of centrality measures. In conclusion, analyzing discussion networks should not be overlooked when public health is concerned, as influencers are not necessarily those with high numbers of followers but rather those with high centrality measures within the interaction network regarding the topic being discussed. © 2022 Little Lion Scientific.

Resorting to Context-Aware Background Knowledge for Unveiling Semantically Related Social Media Posts

Social media networks have become a prime source for sharing news, opinions, and research accomplishments in various domains, and hundreds of millions of posts are announced daily. Given this wealth of information in social media, finding related announcements has become a relevant task, particularly in trending news (e.g., COVID-19 or lung cancer). To facilitate the search of connected posts, social networks enable users to annotate their posts, e.g., with hashtags in tweets. Albeit effective, an annotation-based search is limited because results will only include the posts that share the same annotations. This paper focuses on retrieving context-related posts based on a specific topic, and presents PINYON, a knowledge-driven framework, that retrieves associated posts effectively. PINYON implements a two-fold pipeline. First, it encodes, in a graph, a CORPUS of posts and an input post;posts are annotated with entities for existing knowledge graphs and connected based on the similarity of their entities. In a decoding phase, the encoded graph is used to discover communities of related posts. We cast this problem into the Vertex Coloring Problem, where communities of similar posts include the posts annotated with entities colored with the same colors. Built on results reported in the graph theory, PINYON implements the decoding phase guided by a heuristic-based method that determines relatedness among posts based on contextual knowledge, and efficiently groups the most similar posts in the same communities. PINYON is empirically evaluated on various datasets and compared with state-of-the-art implementations of the decoding phase. The quality of the generated communities is also analyzed based on multiple metrics. The observed outcomes indicate that PINYON accurately identifies semantically related posts in different contexts. Moreover, the reported results put in perspective the impact of known properties about the optimality of existing heuristics for vertex graph coloring and their implications on PINYON scalability.

The Cryptocurrency Market in Transition before and after COVID-19: An Opportunity for Investors?

We analyze the correlation between different assets in the cryptocurrency market throughout different phases, specifically bearish and bullish periods. Taking advantage of a fine-grained dataset comprising 34 historical cryptocurrency price time series collected tick-by-tick on the HitBTC exchange, we observe the changes in interactions among these cryptocurrencies from two aspects: time and level of granularity. Moreover, the investment decisions of investors during turbulent times caused by the COVID-19 pandemic are assessed by looking at the cryptocurrency community structure using various community detection algorithms. We found that finer-grain time series describes clearer the correlations between cryptocurrencies. Notably, a noise and trend removal scheme is applied to the original correlations thanks to the theory of random matrices and the concept of Market Component, which has never been considered in existing studies in quantitative finance. To this end, we recognized that investment decisions of cryptocurrency traders vary between bearish and bullish markets. The results of our work can help scholars, especially investors, better understand the operation of the cryptocurrency market, thereby building up an appropriate investment strategy suitable to the prevailing certain economic situation.

Information propagation on cyber, relational and physical spaces about covid-19 vaccine: Using social media and splatial framework.

With the advent of social media, human dynamics studied in purely physical space have been extended to that of a cyber and relational context. However, connections and interactions between these hybrid spaces have not been sufficiently investigated. The "space-place (Splatial)" framework proposed in recent years allows capturing human activities in the hybrid of spaces. This study applies the Splatial framework to examine the information propagation between cyber, relational, and physical spaces through a case study of Covid-19 vaccine debates in New York State (NYS). Whereby the physical space represents the regional boundaries and locations of social media (i.e., Twitter) users in NYS, the relational space indicates the social networks of these NYS users, and the cyber space captures the larger conversational context of the vaccination debate. Our results suggest that the Covid-19 vaccine debate is not polarized across all three spaces as compared to that of other vaccines. However, the rate of users with a pro-vaccine stance decreases from physical to relational and cyber spaces. We also found that while users from different spaces interact with each other, they also engage in local communications with users from the same region or same space, and distance-based and boundary-confined clusters exist in cyber and relational space communities. These results based on the Splatial framework not only shed light on the vaccination debates but also help to define and elucidate the relationships between the three spaces. The intense interactions between spaces suggest incorporating people's relational network and cyber presence in physical place-making.

New Approaches to Extract Information from Posts on COVID-19 Published on Reddit

In the last two years, we have seen a huge number of debates and discussions on COVID-19 in social media. Many authors have analyzed these debates on Facebook and Twitter, while very few ones have considered Reddit. In this paper, we focus on this social network and propose three approaches to extract information from posts on COVID-19 published in it. The first performs a semi-automatic and dynamic classification of Reddit posts. The second automatically constructs virtual subreddits, each characterized by homogeneous themes. The third automatically identifies virtual communities of users with homogeneous themes. The three approaches represent an advance over the past literature. In fact, the latter lacks studies regarding classification algorithms capable of outlining the differences among the thousands of posts on COVID-19 in Reddit. Analogously, it lacks approaches able to build virtual subreddits with homogeneous topics or virtual communities of users with common interests.

Modularity maximization to design contiguous policy zones for pandemic response.

The health and economic devastation caused by the COVID-19 pandemic has created a significant global humanitarian disaster. Pandemic response policies guided by geospatial approaches are appropriate additions to traditional epidemiological responses when addressing this disaster. However, little is known about finding the optimal set of locations or jurisdictions to create policy coordination zones. In this study, we propose optimization models and algorithms to identify coordination communities based on the natural movement of people. To do so, we develop a mixed-integer quadratic-programming model to maximize the modularity of detected communities while ensuring that the jurisdictions within each community are contiguous. To solve the problem, we present a heuristic and a column-generation algorithm. Our computational experiments highlight the effectiveness of the models and algorithms in various instances. We also apply the proposed optimization-based solutions to identify coordination zones within North Carolina and South Carolina, two highly interconnected states in the U.S. Results of our case study show that the proposed model detects communities that are significantly better for coordinating pandemic related policies than the existing geopolitical boundaries.

Global Container Port Network Linkages and Topology in 2021.

The maritime transport of containers between ports accounts for the bulk of global trade by weight and value. Transport impedance among ports through transit times and port infrastructures can, however, impact accessibility, trade performance, and the attractiveness of ports. Assessments of the transit routes between ports based on performance and attractiveness criteria can provide a topological liner shipping network that quantifies the performance profile of ports. Here, we constructed a directed global liner shipping network (GLSN) of the top six liner shipping companies between the ports of Africa, Asia, North/South America, Europe, and Oceania. Network linkages and community groupings were quantified through a container port accessibility evaluation model, which quantified the performance of the port using betweenness centrality, the transport impedance among ports with the transit time, and the performance of ports using the Port Liner Shipping Connectivity Index. The in-degree and out-degree of the GLSN conformed to the power-law distribution, respectively, and their R-square fitting accuracy was greater than 0.96. The community partition illustrated an obvious consistence with the actual trading flow. The accessibility evaluation result showed that the ports in Asia and Europe had a higher accessibility than those of other regions. Most of the top 30 ports with the highest accessibility are Asian (17) and European (10) ports. Singapore, Port Klang, and Rotterdam have the highest accessibility. Our research may be helpful for further studies such as species invasion and the planning of ports.

Application of CCTV Methodology to Analyze COVID-19 Evolution in Italy.

Italy was one of the European countries most afflicted by the COVID-19 pandemic. From 2020 to 2022, Italy adopted strong containment measures against the COVID-19 epidemic and then started an important vaccination campaign. Here, we extended previous work by applying the COVID-19 Community Temporal Visualizer (CCTV) methodology to Italian COVID-19 data related to 2020, 2021, and five months of 2022. The aim of this work was to evaluate how Italy reacted to the pandemic in the first two waves of COVID-19, in which only containment measures such as the lockdown had been adopted, in the months following the start of the vaccination campaign, the months with the mildest weather, and the months affected by the new COVID-19 variants. This assessment was conducted by observing the behavior of single regions. CCTV methodology allows us to map the similarities in the behavior of Italian regions on a graph and use a community detection algorithm to visualize and analyze the spatio-temporal evolution of data. The results depict that the communities formed by Italian regions change with respect to the ten data measures and time.

Evolution and insights into the network and pattern of the rare earths trade from an industry chain perspective

As strategic mineral resources, rare earths (RE) are globally significant, with demand growing rapidly as the world moves to new energy technologies. This study applied complex network theory to analyze global RE trading data from the perspective of the industry chain from 2005 to 2020. We find the following: (i) The increase in uncertain global events has implications for the stability and security of the global RE industry;(ii) The trade flow upstream is characterized by “Europeanization”, and midstream and downstream are characterized by “Asianization”;(iii) The international RE trade network has gradually become closer, and the participation of various countries has gradually increased. The upstream and downstream trade is mainly concentrated in China, Germany, Australia, the United States, Japan, etc., and the midstream trade shows a hollowing-out trend in Europe and the United States;(iv) The international RE trade both upstream and downstream shows a trend of integration, while the midstream shows a trend of diversification, and COVID-19 has exacerbated the reshaping of the global trade pattern. Based on the above results, we recommend diversifying the supply, expanding the global circle of trusted trading partners and establishing a risk response mechanism. This study can provide policymakers and industry practitioners with an in-depth interpretation based on the industry chain and reference for their adjustment and formulation of RE trade policies.

Contact Tracing Research: A Literature Review Based on Scientific Collaboration Network.

Contact tracing is a monitoring process including contact identification, listing, and follow-up, which is a key to slowing down pandemics of infectious diseases, such as COVID-19. In this study, we use the scientific collaboration network technique to explore the evolving history and scientific collaboration patterns of contact tracing. It is observed that the number of articles on the subject remained at a low level before 2020, probably because the practical significance of the contact tracing model was not widely accepted by the academic community. The COVID-19 pandemic has brought an unprecedented research boom to contact tracing, as evidenced by the explosion of the literature after 2020. Tuberculosis, HIV, and other sexually transmitted diseases were common types of diseases studied in contact tracing before 2020. In contrast, research on contact tracing regarding COVID-19 occupies a significantly large proportion after 2000. It is also found from the collaboration networks that academic teams in the field tend to conduct independent research, rather than cross-team collaboration, which is not conducive to knowledge dissemination and information flow.