| Title | Venue | Year | Impact | Source |
| 3501 | Covid-19-vaccine/evolocumab: Drug specific antibody leading to treatment failure: case report | N/A | 2022 | | CORD-19 |
| 3502 | Remdesivir: Bradycardia: case report | N/A | 2022 | | CORD-19 |
| 3503 | Tozinameran: Hypersensitivity manifested as erythema multiforme minor reaction and eosinophilia: case report | N/A | 2022 | | CORD-19 |
| 3504 | Ravulizumab: Lack of efficacy: case report | N/A | 2022 | | CORD-19 |
| 3505 | Tozinameran: Fever-related ventricular fibrillation: case report | N/A | 2022 | | CORD-19 |
| 3506 | Ocrelizumab: Prolonged severe acute respiratory syndrome Coronavirus-2 infection: case report | N/A | 2022 | | CORD-19 |
| 3507 | AZD-1222: Pityriasis rubra pilaris: case report | N/A | 2022 | | CORD-19 |
| 3508 | Atorvastatin: Statin induced autoimmune necrotising myopathy: case report | N/A | 2022 | | CORD-19 |
| 3509 | Elasomeran: Myocarditis: case report | N/A | 2022 | | CORD-19 |
| 3510 | AZD-1222: Thrombosis with thrombocytopenia syndrome: case report | N/A | 2022 | | CORD-19 |
| 3511 | Covid-19-vaccine-pfizer-biontech: Recurrent pericarditis: case report | N/A | 2022 | | CORD-19 |
| 3512 | Tozinameran: Myopericarditis: case report | N/A | 2022 | | CORD-19 |
| 3513 | AZD-1222: Macrophage activation syndrome: case report | N/A | 2022 | | CORD-19 |
| 3514 | AZD-1222: Thrombotic thrombocytopenia and diffuse arterial thrombosis: case report | N/A | 2022 | | CORD-19 |
| 3515 | Ad26.cov2-s: Diffuse erythematous dermatitis: case report | N/A | 2022 | | CORD-19 |
| 3516 | Antineoplastics: Gastrointestinal distress, lack of efficacy and systemic inflammatory response syndrome and respiratory with circulatory failure: case report | N/A | 2022 | | CORD-19 |
| 3517 | Elasomeran: Pericarditis: case report | N/A | 2022 | | CORD-19 |
| 3518 | How the nature of behavior change affects the impact of asymptomatic coronavirus transmission SARS-CoV-2 has caused severe respiratory illnesses and deaths since late 2019 and spreads globally. While asymptomatic cases play a crucial role in transmitting COVID-19, they do not contribute to the observed prevalence, which drives behavior change during the pandemic. This study aims to identify the effect of the proportion of asymptomatic infections on the magnitude of an epidemic under behavior change scenarios by developing a compartmental mathematical model. In this interest, we discuss three different behavior change cases separately: constant behavior change, instantaneous behavior change response to the disease’s perceived prevalence, and piecewise constant behavior change response to government policies. Our results imply that the proportion of asymptomatic infections which maximizes the spread of the epidemic depends on the nature of the dominant force driving behavior changes. | N/A | 2022 | | CORD-19 |
| 3519 | China Survey Report on the Online Learning Status of High Schools During the COVID-19 Pandemic We examined the results of a large-scale national survey of online secondary education in China. The online survey of 33,194 high school students and 5,667 teachers provides comprehensive and representative data regarding the quality of online education and its implementation during the pandemic. Questionnaire surveys of different grades and comparative analysis of different cohorts reflect the group heterogeneity of the online learning effect. The findings reveal the situation of emergency teaching in China during the pandemic and provides targeted suggestions for school education following the resumption of classes. | N/A | 2022 | | CORD-19 |
| 3520 | Osimertinib: Pneumonitis and interstitial lung disease: case report | N/A | 2022 | | CORD-19 |
| 3521 | Elasomeran: Haemorrhagic pericardial effusion: case report | N/A | 2022 | | CORD-19 |
| 3522 | Revision of Comirnaty precautions in Japan | N/A | 2022 | | CORD-19 |
| 3523 | Elasomeran: Myocarditis: case report | N/A | 2022 | | CORD-19 |
| 3524 | Tourism, job vulnerability and income inequality during the COVID-19 pandemic: A global perspective The COVID-19 pandemic demonstrated the vulnerability of tourism workers, but no detailed job loss figures are available that links tourism vulnerability with income inequality. This study evaluates how reduced international tourism consumption affects tourism employment and their income loss potential for 132 countries. This analysis shows that higher proportions of female (9.6%) and youth (10.1%) experienced unemployment whilst they were paid significantly less because they worked in tourism (−5%) and if they were women (−23%). Variations in policy support and pre-existing economic condition further created significant disparities on lost-income subsidies across countries. With the unequal financial burden across groups, income and regions, the collapse of international travel exacerbates short-term income inequality within and between countries. | N/A | 2022 | | CORD-19 |
| 3525 | Immunological challenges of the "new" infections: corona viruses The arrival of the most recent coronavirus in 2019, SARS-CoV-2, caught the entire world by surprise, and as a result has caused more anguish due to its rapid spread and serious health consequences for the elderly and those with underlying health conditions, and its ability to generate variants of ever increasing contagiousness. But this was not the first coronavirus to infect humans. This chapter explores the history of this virus family, the emergence of the first serious infection in 2003–04 (SARS-CoV), and the related virus MERS in 2012, and the possible origins of SARS-CoV-2. The lessons of those two outbreaks that never developed into pandemics may not all have been learnt by the world health leaders of today. Nevertheless, the rapidity of vaccine development and the conventional health measure introduced during 2020, not always in good time, has almost certainly led to lower morbidities and mortalities that would otherwise have been the case. This chapter will inevitably be out of date by time this book goes to press. Nevertheless, it is to be hoped that the origin of SARS-CoV-2 will eventually be established, but sadly not without the cooperation of the major countries having the resources to carry out such complex investigations. If such a cooperation did happen, maybe future pandemics of this will be more controllable, and even never progress beyond local outbreaks. | A New History of Vaccines for | 2022 | | CORD-19 |
| 3526 | Vaccines are not always perfect: adverse effects and their clinical impact The nature of vaccines and the dichotomy of public opinion about their efficacy, and often the ethical debates about how they are produced, is not new phenomena. Antivaccination movements were present at the end of the 19th century and have existed in every decade, with every new vaccine, ever since. This chapter explores the origins and reasons for vaccination reluctance, sometimes medically justified, mostly based on fallacious scientific argument, and sometimes as a result of pure prejudice against government intervention in the individual’s freedom. In some cases, vaccine prejudice has been justified where vaccinees have suffered adverse effects (AEs) that have been said to be directly due to the vaccine itself, and rarely, thank goodness, to manufacturing errors that have clearly caused such effects. The analysis of the possible contributions to any observed AE, monitored by clinical trial procedures, due to manufacturing additives and adjuvants present within the vaccine sample itself, are described. The results of expert analysis of the likelihood of “cause and effect” for such AEs is described, and the improvements in vaccine technology today, particularly the immunity enhancing adjuvants, that minimize the possibility of a vaccine component’s contribution to postvaccination ill effects are reviewed. Finally, the issue of vaccine safety, of some of the concerns raised with COVID19 vaccines, and the history of cause and effect arguments for those vaccines are discussed. | A New History of Vaccines for | 2022 | | CORD-19 |
| 3527 | Organizational resilience and internal branding: investigating the effects triggered by self-service technology The majority of studies on internal brand equity examine its various dimensions and relationships between them. While prior research specifies organizational practices relevant for successful internal branding, the insights about the impact of essential organizational factors on internal brand equity are still limited. This study focuses on organizational resilience that is vital for the existence of organizations not only during a crisis, but also during everyday operations. The main purpose of this study is to investigate the impact of organizational resilience on internal brand equity considering the effects triggered by self-service technology (SST) in retailing. Since retailing had been significantly transformed by technological innovations over the past decade, we explore the effects of employees’ perceptions about performance of SST. The results of a survey conducted among retail employees in Sweden demonstrate that organizational resilience and employees’ perceptions about technological innovations are critical for enhancing internal brand equity, which includes brand orientation, internal brand knowledge, internal brand involvement, and internal brand commitment. | N/A | 2022 | | CORD-19 |
| 3528 | COVID-19 and the rare disease organization response during pandemic: the 'Italian model' | Future Rare Dis | 2022 | | CORD-19 |
| 3529 | Investor Diversity and Liquidity in The Secondary Loan Market We find strong evidence that investor diversity is beneficial to loan liquidity: More diverse syndicates, as measured by the number of investor-types or the concentration of loan shares by investor-type, hold loans that have lower quoted bid-ask spreads in the secondary market. These results are robust, and do not appear to be driven by investors’ borrower/loan selection. Further, they are not driven by the presence of any particular type of investors. Our findings are consistent with Goldstein and Yang (J Financ 70:1723–1765 2015) insight that there is a strategic complementarity between different groups in trading on their information and producing information. | N/A | 2022 | | CORD-19 |
| 3530 | ÖGARI Positionspapier zur innerklinischen Akut- und Notfallmedizin | N/A | 2022 | | CORD-19 |
| 3531 | Corona-Update: Aktuelle Studien zu SARS-CoV-2 | N/A | 2022 | | CORD-19 |
| 3532 | COVID-19: in the direction of monitoring the pandemic in India The coronavirus disease 2019 (COVID-19) pandemic has witnessed a total of 2,631,338 infected cases as on April 22, 2020. The first case being recorded in January 2020, followed by more than 20,000 case in a matter of 3 months creating a tremor among the population of India. The United States tops the total number of infected cases in the world with 8,45,822 cases, followed by Spain summing up to 2,08,389 cases. India having 21,370 total infected cases, along with 4370 recoveries and a total death toll of 681, ranks 17th among the other countries. In this study, a detailed summary has been conducted on how India is working together to fight this pandemic. The countrymen, along with following the strict instructions provided by the WHO, have set up new rules to fight this disease. This study includes the various medical procedures adopted by the government of India along with the economic guidelines for its fellow citizens during these hard times; a geospatial approach has also been used to identify the infected regions of the country and their pandemic control methods in the 1-month period (22 March–22 April). To date, being the second most populated developing country in the world, India has managed to control the spread of the virus to a large extent. The WHO has praised India's efforts in monitoring the spread of the virus and has alerted India that a simple lockdown would not stop the spread of the virus. They have recommended India to increase the number of tests to segregate the people showing early/mild symptoms. The outcomes of the precautions taken are also discussed in this study. | Data Science for COVID-19 | 2022 | | CORD-19 |
| 3533 | COVID-19: will it be a game changer in higher education in India? The aim of this chapter is to compare the efficacy of various forms of education that may be imparted to students in the wake of coronavirus disease 2019 (COVID-19) and the consequent lockdown period where traditional modes of education are suspended. Various colleges and universities where the authors are currently teaching have been instructed to teach in online mode using tools like zoom messenger. In this direction, the authors first discuss how outcome-based online learning may be implemented in the Indian education sector. To prove their point, the authors select a set of students from the science and engineering streams and impart education to them using both traditional and online methods. Tests are conducted on them after both forms of learning. Multivariate regression analysis is applied on these test results to derive a model for both forms of learning. Results show that both forms of learning are equally effective on these classes of students. Statistical Package for Social Sciences (SPSS) was used for the simulation purpose. Thus we may conclude that in normal circumstances the online method of teaching may not always be beneficial considering the stages and adaptability among all sections of students in a variety of disciplines, but for the science and engineering discipline, a blended learning composed of both classroom-based teaching and online learning methodology may be used to obtain good results, especially when the former form of learning is not available. | Data Science for COVID-19 | 2022 | | CORD-19 |
| 3534 | Data science: a survey on the statistical analysis of the latest outbreak of the 2019 pandemic novel coronavirus disease using ANOVA Since the outbreak of the coronavirus disease 2019 (COVID-19) in Wuhan, China, in late December 2019, the disease has already affected over 200 countries and territories in less than 4 months. On March 11, 2020, the WHO declared the outbreak as a pandemic. As of April 25, 2020, the contagious disease has already infected over 2,919,404 people and the number of deaths reached nearly 206,482. As the disease is spreading rapidly, very less information is available regarding the spread of the novel virus and its effect over various countries. With the help of data science and its latest applications, this chapter aims to explain the rapid spread and impact of the novel coronavirus infection over individual countries. In this chapter, we have first explained about the evolution and transmission of viral diseases from animals to humans, next discussed about the various statistical methods used for the analysis of the spread of the disease, and finally come up with a comparison of the past 2 months of the pandemic (March and April). This chapter will give an insight of the application of data science in analyzing the latest COVID-19 pandemic and its impact. | Data Science for COVID-19 | 2022 | | CORD-19 |
| 3535 | Potential antiviral therapies for COVID-19 The novel coronavirus disease (COVID-2019) caused by severe acute respiratory syndrome coronavirus (called SARS-CoV-2) emerged in China in December 2019 and then spread rapidly to more than 200 countries around the world, including the United States, Spain, Italy, the United Kingdom, Germany, France, Japan, and South Korea, resulting in more than 208,112 deaths worldwide. As there is no approved vaccine or therapeutic available to control the COVID-2019 pandemic, scientists across the world are trying every possible way to find antivirals specific to this virus. In this urgent situation, parallel to the development of new vaccines and drugs, many previously approved antiviral drugs of broad range such as arbidol, interferon alfa, chloroquine, remdesivir, and favipiravir are presently undergoing clinical trials against COVID-19. So far some positive findings have been obtained, and here we present a thorough overview of all possible antiviral medicines that can control this pandemic of SARS-CoV-2. | Data Science for COVID-19 | 2022 | | CORD-19 |
| 3536 | Bi(III) I-Complexes of Porphyrins for Biomedicine: Synthesis and Spectral-Optical Properties Bismuth complexes of porphyrins are of interest for IR luminescence diagnostics of cancer, since rather intense emission bands in the range of 800–920 nm have been found. In connection with the COVID-19 pandemic, bismuth compounds are also of interest in the treatment of coronavirus infection. Bismuth complexes of porphyrins of various spatial configurations have been synthesized, and several spectral-optical properties have been investigated. The influence of various substituents on the spectral characteristics was evaluated by methods of studying electronic absorption spectra, luminescence spectra, IR-, and (1)H NMR spectroscopy. | N/A | 2022 | | CORD-19 |
| 3537 | Economic Agents' Behaviors During the Coronavirus Pandemic: Theoretical Overview and Prospective Approach In economics, several theories have focused on the study of decisions made by economic agents. Recent branches in economics have even revealed some controversies with the theoretical findings of the basic neoclassical model. Indeed, the growing uncertainty characterizing certain contemporary risks leads to various biases in the economic agents’ behaviors. The relevance of decisions under uncertainty calls into question the assumption of absolute rationality of the neoclassical economics. In this context, the COVID-19 pandemic is causing an unprecedented human crisis. This paper aims to provide a theoretical examination of the behaviors of economic agents faced with uncertainty and catastrophic situations. In its first contribution, this study provides a theoretical overview of behaviors under uncertainty. In its second contribution, the paper highlights the need for a prospective approach in the face of the coronavirus pandemic. Hence, we delve into the theoretical foundations of a prospective approach to health. All around the world, the COVID-19 pandemic approved the need for rethinking the preparation of “public health” systems to deal with pandemics. A good response to the present and a better preparation for the future require a revision of behaviors in terms of international cooperation and monitoring of Sustainable Development Goals. | N/A | 2022 | | CORD-19 |
| 3538 | In search of member needs in coworking spaces Coworking spaces represent a new trend for future workplaces. As more building owners are interested in running coworking space businesses, it becomes important to understand both why a potential member chooses one space over another and how to keep existing members at a coworking site. A sound understanding of member needs can make a difference. Unfortunately, very few studies have been conducted as to understanding a member’s basic needs in coworking spaces. In this paper, we aim to identify member’s needs in three coworking spaces in Sweden. Participant observations, immersion, and interviews were used for data collection. The member needs are categorized and structured through the lens of self-determination theory. In total, we uncovered, formulated, and categorized 21 member needs. We found that the fulfillment of one need may lead to the inhibition of another, thus creating tensions between and within coworking members. This research contributes to the literature by addressing the importance and definition of member needs for coworking as well as the created tensions related to these needs, which have been lacking in coworking studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11846-022-00546-4. | N/A | 2022 | | CORD-19 |
| 3539 | Are the northern and southern regions equally affected by the COVID-19 pandemic? An empirical evidence from Nigeria The new coronavirus infection, being a current global fearful pandemic disease among humans, has spread all over hundreds of countries around the world and has greatly infected millions of people. While the spread of this disease continues among humans, the study aims at determining which part of Nigeria is more affected by coronavirus disease 2019 (COVID-19), as this would assist the national government and other international nongovernmental agencies to plan on the concentration patterns as to which region would require more helping hands in terms of fight against the pandemic disease. Nigeria is stratified into two: southern and northern regions. Data on confirmed cases are sourced from the official website of the Nigeria Centre for Disease Control. These datasets, which cover the periods of February 29 and May 1, 2020 inclusive, are captured on a weekly basis and are grouped according to regional zones, i.e., southern and northern parts. The assumptions of normality and homogeneity of variances are tested via the use of Shapiro-Wilk and Bartlett's statistics. The two tests reveal no violation of any of the assumptions. Thereafter, the t-test is applied with a view to determine the region with a higher chance of being affected by COVID-19. Our results indicate that states in the south have a higher rate of confirmed cases of the pandemic infection than the states in the north. It is therefore concluded that the national government as well as other nongovernmental agencies should focus more on the fight against the pandemic disease in the southern states of Nigeria than in the northern states. | Data Science for COVID-19 | 2022 | | CORD-19 |
| 3540 | The significance of daily incidence and mortality cases due to COVID-19 in some African countries With the current outbreak of COVID-19, the African countries have been on heightened alert to detect and isolate any imported and locally transmitted cases of the disease. It was observed that each of the daily COVID-19 incidence and mortality counts among African countries may not be independent. Result of the Ljung-Box test showed that each of the daily COVID-19 incidence and mortality counts among African countries was not independent, rather both are time-dependent. Analyzing daily COVID-19 incidence and mortality counts over time requires more specialized analytic tools. Trend analysis of daily counts of COVID-19 incidence and deaths is presented over time. Also, generalized estimating equation, a flexible tool for analyzing longitudinal data, is employed to analyze the daily COVID-19 mortality rates in African countries. Findings from this study showed that patterns of incidence cases among African countries are statistically different. There are significant monotone trends in the daily COVID-19 incidence and mortality counts of many countries in Africa. There is a positive weak linear relationship between the daily reported COVID-19 cases and the population of African countries. However, the magnitude of the observed association was particularly small. It was further deduced that the farther the number of days from the day of first incidence if the pandemic is not properly managed, the more the daily COVID-19 mortality rate in Africa. | Data Science for COVID-19 | 2022 | | CORD-19 |
| 3541 | COVID-19 outlook in the United States of America: a data-driven thematic approach At the inception of the coronavirus disease 2019 (COVID-19) pandemic, different categories of planners have emerged around the globe, such as Proactive, Preactive, Inactive, and Reactive planners. They all fall into the groups of early birds and latecomers. America, as a benevolent country, is playing a central role in the ongoing battle against COVID-19. Despite America's leading humanitarian and health assistance response to COVID-19, it did not exempt the country from the deadly coronavirus. Despite the controversies surrounding the virus, the American government has taken several steps to reduce the virus spread and flatten the mortality curve. Studies by the Centers for Disease Control and Prevention (CDC) share new knowledge and enlighten the public on the position of COVID-19 globally, but there is a vacuum in having a deeper understanding of the emerging themes in America concerning COVID-19. This research embarked on a thematic and sentiment analysis via text mining techniques from Twitter data to contribute to the ongoing academic discourse with respect to COVID-19 within the context of the United States of America. The results show relevant and unexpected bigrams. This study clarifies some uncertainty regarding the COVID-19 outlook in America. In addition, researchers can extend these results to other countries that have been dominated by COVID-19. Finally, this research discusses the limitations and gives future policy direction regarding COVID-19. | Data Science for COVID-19 | 2022 | | CORD-19 |
| 3542 | Lexicon-based sentiment analysis using Twitter data: a case of COVID-19 outbreak in India and abroad COVID-19 is a kind of virus of the Corona family originated from Wuhan, China, and spread over more than 215 countries in the world, more than 2.3 lakhs people died, and more than 32 lakhs are affected globally till date and numbers are continuously increasing. Because of this global pandemic, citizens of the country are in a panic situation. Sentiment Analysis (SA) is a prominent field to analyze data available on social media. This research work explores SA using the Lexicon-based approach to analyze the sentiment of six different countries: India, the USA, Spain, Italy, France, and the UK. Data from March 15 to April 15, 2020 extracted from Twitter and used to identify sentiment as Negative, Neutral, or Positive using Lexicon-based and Valence Aware Dictionary for Sentiment Reasoning (VADER)-based approaches. Empirical results show that negativity exists in almost all the countries because of COVID-19. Out of six countries considered for the SA, the UK has the highest negativity of 23.03%, followed by France with 22.71%, the USA with 22.01%, and India is having negativity of 18.39% using Simple Lexicon-based approach. At the same time, it is 35.92% in France, 35.68% in the UK, and 35.38% in the USA, while India has the least negativity of 31.03% based on the VADER-based approach. Both approaches are almost producing negativity in the same order with slight variations. Furthermore, a comparative detail analysis of India has also been done based on Twitter data. The data collected before and after lockdown using a simple Lexicon-based approach, and it has been observed that negativity is increasing after lockdown and slightly decreased during lockdown 2.0. Overall implication of this research work is that however negativity exists but people are more positive toward panic situation because of COVID-19 and also fighting against COVID-19 with restrictions like lockdown, home isolation, quarantine, limited access of resources, etc. | Data Science for COVID-19 | 2021 | | CORD-19 |
| 3543 | Coronavirus: a scientometric study of worldwide research publications Coronaviruses (CoVs) are a large family of viruses and are endemic in humans and animals, causing respiratory and intestinal infections. CoV has become a challenge in China region due to its recent outbreak at the start of the year 2020. The current outbreak of CoV disease has resulted in many fatalities and has forced the people of Wuhan Province in China to remain confined in their homes. Two other two forms of CoVs were epidemic in 2003 when the severe acute respiratory syndrome coronavirus (SARS-CoV) spread in Hong Kong and the Middle East respiratory syndrome coronavirus (MERS-CoV) spread in the Middle East region. This scientometric study is an attempt to trace the trends of research associated with “Coronavirus” for a period of 32 years using the Web of Science citation database. The database was searched on February 26, 2020, for CoV publications published from 1989 to 2020. Identified and analyzed parameters include year of publication, publication type, patterns of international collaboration, research institutions, journals, impact factor, h-index, language, and the number of times cited. Most of the research publications were from the United States (35,871), and the University of Hong Kong was the most productive institute (517, 4.10% publications). The Journal of Virology has published the most number of articles on CoV. | Data Science for COVID-19 | 2022 | | CORD-19 |
| 3544 | Big data processing and analysis on the impact of COVID-19 on public transport delay The coronavirus disease 2019 (COVID-19) pandemic that started at the beginning of the year 2020 has significantly disrupted people's daily life around the world. Understanding and quantifying the impact of such a large-scale disruption will help people mitigate the pandemic and enhance the resilience for future preparation of similar events. In this chapter, we present a research work studying the impact of COVID-19 on public transport in terms of bus delay, which involves big data processing and analysis on multisource datasets containing COVID-19 case data, bus GTFS (General Transit Feed Specification) data, and LGA (Local Government Area) boundary data. The datasets in use are heterogeneous, arrive in large volumes and in real time, and have a spatiotemporal distribution, which brings true challenges to this research. To quantify the bus delay changes, we propose a methodology consisting of real-time data crawling, map-matching, arrival time estimation, and bus delay calculation and aggregation. The methodology is applied to a case study focusing on the Sydney metropolitan region across different stages of the COVID-19 pandemic from February to March 2020. The case study shows that during March 2020, the COVID-19 pandemic has significantly impacted people's travel behaviors in Sydney, but the influence varies in different areas. The most affected areas are the central and eastern suburbs, which recorded a drop of 9.5 min of bus delay during afternoon peak hours. The findings are helpful to understand and mitigate the restriction impact in different city areas with different conditions. The quantified delay reduction also reveals the potential of better transport performance, which could be used as a benchmark of transport performance improvement after the pandemic. The main contributions of this work include the methodology to quantify travel behavior changes under large disruptions such as COVID-19 pandemic and the case study on large-scale and long-period travel behavior shift that seldom happened before. | Data Science for COVID-19 | 2022 | | CORD-19 |
| 3545 | COVID-19 lethality reduction using artificial intelligence solutions derived from telecommunications systems In this chapter, we propose to bridge two worlds as we suggest to repurpose artificial intelligence solutions originally developed for telecommunications systems in the field of fighting against a pandemic. The objective is to provide solutions supporting the global effort to fight the coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The European Telecommunications Standards Institute has published an architecture group specification that introduces a number of building blocks and a general high-level approach enabling automated data analysis and related decision-making in the context of large-scale communication systems. We illustrate how the available structure can be adapted to the needs and analysis of patient allocation in the context of a pandemic and scarce resources. Thus learning and decision-making processes may be applied for the extraction of information related to the pandemic evolution and forecast, as well as the health system optimization. An artificial intelligence (AI) system is indeed ideally suited to extract relevant information from a data pool and to train decision-making algorithms for modeling the spread of a pandemic. Instead of using AI tools in medical diagnostics for a single patient, we propose to extend the approach to a country-wide optimization to optimally allocate patients and available medical resources and show how discrete optimization tools may provide an optimum mapping under the given constraints such as limited medical resources. As a consequence, the analysis and processing of such data by public services is optimized considerably building on state-of-the-art data analytics as originally developed in a different field. | Data Science for COVID-19 | 2022 | | CORD-19 |
| 3546 | "Quarantined within a quarantine": COVID-19 and GIS Dynamic Scenario Modeling in Tasmania, Australia When the Australian state and lone isle of Tasmania went into coronavirus disease 2019 (COVID-19) quarantine lockdown in March, within a quarantine-imposed Australian continent, thinking it was being very prudent, unforeseen was the lurking virus. Australia across January had been watching the global northern hemisphere scenario occurring and by February was preparing to quarantine itself, echoing its existing and long-term biosecurity exclusion regime. On a much grander scale, following through on a previously trialed national pandemic training exercise, no one had factored in the Ruby Princess variable and its major consequences that would require unprecedented pandemic response. The concentrated impact of cruise ship virus dissemination and escalation has been palpable across the world, but the Ruby Princess will remain a disaster in Australia's history. For Tasmania, several elderly passengers retraveled from Sydney to Tasmania, and a minor cluster has occurred. This chapter contextualizes what has been transpiring in Australia with the pandemic, with particular attention upon Tasmania, including discussion about the new COVIDSafe.App, and then explains the potential application of a Systems Dynamics Modeling exercise of the COVID-19 spread, in collaboration with a custom-built 2D/3D geographic information system (GIS) Dynamic Scenario Planning Model to spatially visualize potential “what-if” scenarios of COVID-19 spread (and other future pandemics) to identify high-risk areas and vulnerable communities in the northern areas of Tasmania that is aiding real-time pattern mapping and preparation work and to further consider and enable the most effective emergency response and recovery scenarios. | Data Science for COVID-19 | 2022 | | CORD-19 |
| 3547 | Applications of Building Information Modeling for COVID-19 spread assessment due to the organization of building artifacts Quick and accurate building-level infectious disease transmission evaluation is a key concern that gains traction nowadays. This chapter summarizes Building Information Modeling (BIM) applications for COVID-19 Spread Assessment due to the Organization of Building Artifacts (CSAOBA). Development of BIM-based inbuilt and add-in tools for CSAOBA offer a faster approach for data gathering and sharing. A Geographic Information System (GIS) and BIM-integrated CSAOBA at the provincial level information system is a suitable platform for retrieving and processing huge data automatically and accurately. BIM delivers project-level information and GIS stores and manipulates the regional-level information for district-level assessments. Future BIM standards are to incorporate CSAOBA-related modeling rules and regulations, which may ease handling emergency situations. BIM-based reliable CSAOBA tools require efficient ontologies and related algorithms to increase the accuracy and industrial deployment. | Data Science for COVID-19 | 2022 | | CORD-19 |
| 3548 | Docking study of transmembrane serine protease type 2 inhibitors for the treatment of COVID-19 The recent pandemic development of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its quick national and international spread present a global health emergency. Entry of coronaviruses into the cell depends on binding of the viral spike (S) proteins to host cells receptors, which rely on cell proteases for activation. One of the proteases, transmembrane serine protease type 2 (TMPRSS2) was proven to be crucial for S protein priming. Our research emphasizes on identifying presupposing drug candidates for the TMPRSS2 inhibitors to combat coronavirus disease 2019 (COVID-19). TMPRSS2 homology model is generated by utilizing Modeller9.22, whereas we perform molecular docking with AutoDock Vina. Docking of peptidomimetic inhibitors (inhibitor “92” and inhibitor “50”) and allosteric inhibitors (nafamostat and camostat mesilate) in this study is carried out at the active site of the TMPRSS2 homology model. Known active ligands have low docking score energies varying from −7.6 to −8.7 kcal/mol. The docking study has confirmed peptidomimetic inhibitors bind with the catalytic triad HIS 41 and ASP 90 by strong hydrogen bonding. Allosteric inhibitors block access to the catalytic triad (HIS 41, ASP 90, and SER 186) by forming hydrogen bonds with ASP 180, GLN 183, and GLY 209 in the S1 pocket. This investigation gives an insight into the design and identification of drug repurposing candidates for the management of COVID-19. | Data Science for COVID-19 | 2022 | | CORD-19 |
| 3549 | Modeling and predicting the spread of COVID-19: a continental analysis The world is currently overwhelmed with the perils of the outbreak of the coronavirus disease 2019 (COVID-19) pandemic. As of May 18, 2020, there were 4,819,102 confirmed cases, of which there were 316,959 deaths worldwide. The devastating effects of the COVID-19 pandemic on the world economy are more grievous than many natural disasters like earthquakes and tsunamis in history. Understanding the spread pattern of COVID-19 and predicting the disease dynamics have been essential to assist policymakers and health practitioners in the public and private health sector in providing an efficient way of alleviating the effects of the pandemic across continents. Scholars have steadily worked to provide timely information. Nevertheless, there is a lack of information on which insights can be derived from all these endeavors, especially with regard to modeling and prediction techniques. In this study, we used a literature synthesis approach to provide a narrative review of the current research efforts geared toward predicting the spread of COVID-19 across continents. Such information is useful to provide a global perspective of the virus particularly with regard to modeling and prediction techniques and their outcomes. A total of 69 peer-reviewed articles were reviewed. We found that most articles were from Asia (34.8%) and Europe (23.2%), followed by North America (14.5%), and very few emanated from other continents including Africa and Australia (6.8% each), while no study was reported in Antarctica. Most of the modeling and predictions were based on compartmental epidemiologic models and a few used advanced machine learning techniques. While some models have accurately predicted the end of the epidemic in some countries, other predictions strongly deviate from reality. Interestingly, some studies showed that combining artificial intelligence with classical compartmental models provides a better prediction of the disease spread. Assumptions made when parameterizing the models might be wrong and might not suit the local contexts and might partly explain the observed deviation from the reality on the ground. Furthermore, lack of publicly available key data such as age, gender, comorbidity, and historical medical data of cases and deaths in some continents could limit researchers in addressing some essential aspects of the virus spread and its consequences. | Data Science for COVID-19 | 2022 | | CORD-19 |
| 3550 | Data sharing and privacy issues arising with COVID-19 data and applications The coronavirus disease 2019 (COVID-19) (2019-nCov), which was first detected in Wuhan/China in December 2019 and spread to the whole world in a short time, was explained as a new coronavirus by the World Health Organization on February 11, 2020. Countries are developing various strategies against the spread of epidemic threat. The main ones are to develop web-based or mobile applications to reduce the spread and economic damage of the epidemic by making use of COVID-19 datasets. It is seen that the existing applications developed within the framework of these expectations contain absolute location information (direct), relative location information (indirect), and characteristic data defining people. Even if these data mean a lot to the world's struggle with COVID-19, it is necessary to foresee the risks that may occur after the epidemic when the relations of the information are considered. In order to measure the privacy risk of this kind of applications containing personal data, privacy metrics have been defined in the literature. In this chapter, we give a perspective about the sharing and privacy of medical data within the scope of COVID-19. Within this context, privacy models, metrics, and approaches for selecting the appropriate model are described, in particular for COVID-19 applications, and we also propose a new metric with the entropy approach to metrics defined in the literature and effective in determining the privacy score. | Data Science for COVID-19 | 2022 | | CORD-19 |
