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Big Data Analytics amid COVID-19: Toward Sustainable Society

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Management".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 27614

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Special Issue Editors

Prof. Dr. Ohbyung Kwon

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Guest Editor

School of Management, Kyung Hee University, Seoul 130-701, Gyeonggi-do, Republic of Korea
Interests: big data analytics; AI business; metaverse/XR business
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Kyoung-yun “Joseph” Kim

E-Mail Website
Guest Editor

Wayne State University
Interests: design science, semantic engineering, data-driven design, sustainable design and education

Prof. Dr. Namgyu Kim

E-Mail Website
Guest Editor

Kookmin University
Interests: text analytics, deep learning, data modeling

Prof. Dr. Namyeon Lee

E-Mail Website
Guest Editor

Hanshin University
Interests: big data analysis, text-mining, artificial intelligence, blockchain

Special Issue Information

Dear Colleagues,

As the new coronavirus (COVID-19) holds the world in limbo, ending it as soon as possible has become an urgent, important task for mankind. New coronavirus outbreaks are announced each day, related statistics fluctuate rapidly, and enormous amounts of data are generated. In addition, lessons learned from similar pandemics in the past, such as SARS and MERS, have also created enormous amounts of data, sometimes called big data. Big data could potentially help us understand the nature of the new coronavirus, providing a source of information that can inspire prevention and treatment. Big data analysis may also facilitate prediction of structural changes in our society, economy, and lifestyle that may result from the current pandemic.

This special issue offers a platform to identify new issues, developments, and case studies related to the coronavirus to which big data analytics may be applicable. This issue seeks to improve understanding of coronavirus activity through processing and visualization of massive and unstructured data such as medical images, patient records, social media, newspapers, and other sources. Data mining, disease determination, and predictions regarding the coronavirus pandemic may be possible through deep learning. This issue also includes useful examples of big data technologies that can be used for estimation of social, psychological, economic, and political impacts of this virus all over the world.

Prof. Dr. Ohbyung Kwon
Prof. Dr. Kyoung-yun “Joseph” Kim
Prof. Dr. Namgyu Kim
Prof. Dr. Namyeon Lee
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

new approaches to big data analytics
big data management
machine learning for big data
social media
COVID-19

Published Papers (7 papers)

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Research

17 pages, 2248 KiB

Open AccessArticle

The Impact of the COVID-19 on Economic Sustainability—A Case Study of Fluctuation in Stock Prices for China and South Korea

by Jialei Jiang, Eun-Mi Park and Seong-Taek Park

Sustainability 2021, 13(12), 6642; https://doi.org/10.3390/su13126642 - 10 Jun 2021

Cited by 7 | Viewed by 3088

Abstract

The coronavirus disease (COVID-19) pandemic has had a global impact on lives, livelihoods, and economies. This study investigates whether a contagious infectious disease can affect the prices of the Chinese and Korean stock markets. Specifically, we aim to discover discrepancies in the impact of COVID-19 on the stock prices of China and South Korea through panel data. To test these discrepancies, we first regressed the stock indices on confirmed cases and deaths. We then validated the stability of coefficients over the past days. The empirical results show that (1) responses of stock indices are stable and impulsive and (2) response patterns toward COVID-19 events considerably vary across nations, especially in the counties such as China and South Korea. Full article

(This article belongs to the Special Issue Big Data Analytics amid COVID-19: Toward Sustainable Society)

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10 pages, 580 KiB

Open AccessArticle

COVID-19 Pandemic Analysis for a Country’s Ability to Control the Outbreak Using Little’s Law: Infodemiology Approach

by Yao-Hua Ho, Yun-Juo Tai and Ling-Jyh Chen

Sustainability 2021, 13(10), 5628; https://doi.org/10.3390/su13105628 - 18 May 2021

Cited by 2 | Viewed by 2799

Abstract

Since the outbreak of the coronavirus disease (COVID-19), all countries across the globe have been trying to control its spread. A country’s ability to control the epidemic depends on how well its health system accommodates COVID-19 patients. This study aimed to assess the ability of different countries to contain the COVID-19 epidemic in real-time with the number of confirmed cases, deaths and recovered cases. Using the dataset provided by the Humanitarian Data Exchange (HDX), we analyzed the spread of the virus from 22 January 2020 to 15 September 2020 and used Little’s Law to predict a country’s ability to control the epidemic. According to the average recovery time curve changes, 16 countries are divided into different categories—Outbreak, Under Control, Second Wave of Outbreak, and Premature Lockdown Lift. The curves of outbreak countries (i.e., U.S., Spain, Netherlands, Serbia, France, Sweden, and Belgium) showed an upward trend representing that their medical systems have been overloaded and are unable to provide effective medical services to patients. On the other hand, after the pandemic-prevention policy was applied, the average recovery time dropped in under control countries (i.e., Iceland, New Zealand, Taiwan, Thailand, and Singapore). Finally, we study the impact of interventions on the average recovery time in some of the countries. The interventions, e.g., lockdown and gathering restrictions, show the effect after 14 days, which is the same as the incubation period of COVID-19. The results show that the average recovery time (T) can be used as an indicator of the ability to control the pandemic. Full article

(This article belongs to the Special Issue Big Data Analytics amid COVID-19: Toward Sustainable Society)

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13 pages, 1493 KiB

Open AccessArticle

Derailment or Turning Point? The Effect of the COVID-19 Pandemic on Sustainability-Related Thinking

by Zoltán Lakner, Brigitta Plasek, Anna Kiss, Sándor Soós and Ágoston Temesi

Sustainability 2021, 13(10), 5506; https://doi.org/10.3390/su13105506 - 14 May 2021

Cited by 8 | Viewed by 2140

Abstract

A pandemic has always been a milestone, forcing intellectuals to reassess the directions of development at their time. This fact has generated vivid debates about the possible reactions to the new situation, highlighting the vulnerability of current socio-economic structures as well as the need to reconsider the current way of development. The new challenge has created an unprecedented increase in academic publications. The aim of the current paper is to analyze the socio-economic aspects of the growing interest in the sustainability-related facets of the pandemic. Based on English language journal articles (n = 1326), collected on the Web of Science website, the authors analyze the different aspects of COVID-related discussions connected to sustainability. Applying the triangulation approach, the publications have been classified on the basis of their intellectual roots, co-occurrence of different words and strategic diagramming. Results highlight that, notwithstanding the remarkable number of papers, there is a strong need for the in-depth analysis of the long-term consequences in the fields of (1) health logistics and policy; (2) the future of education and work, based on experience and evidence; (3) the re-thinking of the resilience of large-scale supply systems; (4) global governance of world affairs, (5) the role of distant teaching, telecommunication, telework, telehealth, teleservices. Full article

(This article belongs to the Special Issue Big Data Analytics amid COVID-19: Toward Sustainable Society)

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14 pages, 1407 KiB

Open AccessArticle

Evaluating Polarity Trend Amidst the Coronavirus Crisis in Peoples’ Attitudes toward the Vaccination Drive

by Rakhi Batra, Ali Shariq Imran, Zenun Kastrati, Abdul Ghafoor, Sher Muhammad Daudpota and Sarang Shaikh

Sustainability 2021, 13(10), 5344; https://doi.org/10.3390/su13105344 - 11 May 2021

Cited by 14 | Viewed by 3200

Abstract

It has been more than a year since the coronavirus (COVID-19) engulfed the whole world, disturbing the daily routine, bringing down the economies, and killing two million people across the globe at the time of writing. The pandemic brought the world together to a joint effort to find a cure and work toward developing a vaccine. Much to the anticipation, the first batch of vaccines started rolling out by the end of 2020, and many countries began the vaccination drive early on while others still waiting in anticipation for a successful trial. Social media, meanwhile, was bombarded with all sorts of both positive and negative stories of the development and the evolving coronavirus situation. Many people were looking forward to the vaccines, while others were cautious about the side-effects and the conspiracy theories resulting in mixed emotions. This study explores users’ tweets concerning the COVID-19 vaccine and the sentiments expressed on Twitter. It tries to evaluate the polarity trend and a shift since the start of the coronavirus to the vaccination drive across six countries. The findings suggest that people of neighboring countries have shown quite a similar attitude regarding the vaccination in contrast to their different reactions to the coronavirus outbreak. Full article

(This article belongs to the Special Issue Big Data Analytics amid COVID-19: Toward Sustainable Society)

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18 pages, 941 KiB

Open AccessArticle

A Model for Rapid Selection and COVID-19 Prediction with Dynamic and Imbalanced Data

by Jeonghun Kim and Ohbyung Kwon

Sustainability 2021, 13(6), 3099; https://doi.org/10.3390/su13063099 - 11 Mar 2021

Cited by 5 | Viewed by 2315

Abstract

The COVID-19 pandemic is threatening our quality of life and economic sustainability. The rapid spread of COVID-19 around the world requires each country or region to establish appropriate anti-proliferation policies in a timely manner. It is important, in making COVID-19-related health policy decisions, to predict the number of confirmed COVID-19 patients as accurately and quickly as possible. Predictions are already being made using several traditional models such as the susceptible, infected, and recovered (SIR) and susceptible, exposed, infected, and resistant (SEIR) frameworks, but these predictions may not be accurate due to the simplicity of the models, so a prediction model with more diverse input features is needed. However, it is difficult to propose a universal predictive model globally because there are differences in data availability by country and region. Moreover, the training data for predicting confirmed patients is typically an imbalanced dataset consisting mostly of normal data; this imbalance negatively affects the accuracy of prediction. Hence, the purposes of this study are to extract rules for selecting appropriate prediction algorithms and data imbalance resolution methods according to the characteristics of the datasets available for each country or region, and to predict the number of COVID-19 patients based on these algorithms. To this end, a decision tree-type rule was extracted to identify 13 data characteristics and a discrimination algorithm was selected based on those characteristics. With this system, we predicted the COVID-19 situation in four regions: Africa, China, Korea, and the United States. The proposed method has higher prediction accuracy than the random selection method, the ensemble method, or the greedy method of discriminant analysis, and prediction takes very little time. Full article

(This article belongs to the Special Issue Big Data Analytics amid COVID-19: Toward Sustainable Society)

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19 pages, 3282 KiB

Open AccessArticle

Deep Learning-Based Knowledge Graph Generation for COVID-19

by Taejin Kim, Yeoil Yun and Namgyu Kim

Sustainability 2021, 13(4), 2276; https://doi.org/10.3390/su13042276 - 19 Feb 2021

Cited by 22 | Viewed by 7448

Abstract

Many attempts have been made to construct new domain-specific knowledge graphs using the existing knowledge base of various domains. However, traditional “dictionary-based” or “supervised” knowledge graph building methods rely on predefined human-annotated resources of entities and their relationships. The cost of creating human-annotated resources is high in terms of both time and effort. This means that relying on human-annotated resources will not allow rapid adaptability in describing new knowledge when domain-specific information is added or updated very frequently, such as with the recent coronavirus disease-19 (COVID-19) pandemic situation. Therefore, in this study, we propose an Open Information Extraction (OpenIE) system based on unsupervised learning without a pre-built dataset. The proposed method obtains knowledge from a vast amount of text documents about COVID-19 rather than a general knowledge base and add this to the existing knowledge graph. First, we constructed a COVID-19 entity dictionary, and then we scraped a large text dataset related to COVID-19. Next, we constructed a COVID-19 perspective language model by fine-tuning the bidirectional encoder representations from transformer (BERT) pre-trained language model. Finally, we defined a new COVID-19-specific knowledge base by extracting connecting words between COVID-19 entities using the BERT self-attention weight from COVID-19 sentences. Experimental results demonstrated that the proposed Co-BERT model outperforms the original BERT in terms of mask prediction accuracy and metric for evaluation of translation with explicit ordering (METEOR) score. Full article

(This article belongs to the Special Issue Big Data Analytics amid COVID-19: Toward Sustainable Society)

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16 pages, 341 KiB

Open AccessArticle

Value Relevance of Accounts Receivable Factoring and Its Impact on Financing Strategy under the K-IFRS after COVID-19 from the Perspective of Accounting Big Data

by Jung Min Park, Hyoung Yong Lee, Sang Hyun Park and Ingoo Han

Sustainability 2020, 12(24), 10287; https://doi.org/10.3390/su122410287 - 09 Dec 2020

Cited by 6 | Viewed by 4466

Abstract

This study investigates whether recognized accounts receivable (AR) factoring is more value relevant than disclosed AR factoring. After the adoption of the Korean International Financial Reporting Standards (K-IFRS), AR factoring is recognized as short-term debt, thus increasing firms’ leverage ratio. Using cross-sectional equity valuation regressions, we find that recognized AR factoring is value relevant, unlike disclosed AR factoring. Moreover, the market value of equity and AR factoring are more significantly correlated in highly leveraged firms than in less-leveraged ones. Accounting data are important from the perspective of big data. In the accounting industry as well, professionals started realizing the implications of big data. The COVID-19 pandemic has created a health crisis and wreaked havoc in an already-fragile global economy. Although there is no way to predict exactly what the economic damage from the COVID-19 pandemic will be, there must be widespread agreement that it will have severe financial impact on every company. Global financial markets have suffered dramatic falls due to the pandemic, and highly leveraged companies are in serious need of financing. While diving deeper, sound debt management and debt transparency are critical to ensure debt sustainability. Thus, companies would be willing to use AR factoring in order to overcome this financial status. This study also shows that highly leveraged firms decrease AR factoring after K-IFRS adoption. Full article

(This article belongs to the Special Issue Big Data Analytics amid COVID-19: Toward Sustainable Society)

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