bluuebunny/crossref_metadata_2025_split huggingface dataset:
Characterizing super-spreaders using population-level weighted social networks in rural communities
Authors: Shridhar Shivkumar Vishnempet , Alexander Marcus , Christakis Nicholas A. 
Published: November 2021

Sociocentric network maps of entire populations, when combined with data on the nature of constituent dyadic relationships, offer the dual promise of advancing understanding of the relevance of networks for disease transmission and of improving epidemic forecasts. Here, using detailed sociocentric data collected over 4 years in a population of 24 702 people in 176 villages in Honduras, along with diarrhoeal and respiratory disease prevalence, we create a social-network-powered transmission model and identify super-spreading nodes as well as the nodes most vulnerable to infection, using agent-based Monte Carlo network simulations. We predict the extent of outbreaks for communicable diseases based on detailed social interaction patterns. Evidence from three waves of population-level surveys of diarrhoeal and respiratory illness indicates a meaningful positive correlation with the computed super-spreading capability and relative vulnerability of individual nodes. Previous research has identified super-spreaders through retrospective contact tracing or simulated networks. By contrast, our simulations predict that a node’s super-spreading capability and its vulnerability in real communities are significantly affected by their connections, the nature of the interaction across these connections, individual characteristics (e.g. age and sex) that affect a person’s ability to disperse a pathogen, and also the intrinsic characteristics of the pathogen (e.g. infectious period and latency).
This article is part of the theme issue ‘Data science approach to infectious disease surveillance’.
A global assessment of the impact of school closure in reducing COVID-19 spread
Authors: Wu Joseph T. , Mei Shujiang , Luo Sihui , Leung Kathy , Liu Di , Lv Qiuying , Liu Jian , Li Yuan , Prem Kiesha , Jit Mark , Weng Jianping , Feng Tiejian , Zheng Xueying , Leung Gabriel M. 
Published: November 2021

Prolonged school closure has been adopted worldwide to control COVID-19. Indeed, UN Educational, Scientific and Cultural Organization figures show that two-thirds of an academic year was lost on average worldwide due to COVID-19 school closures. Such pre-emptive implementation was predicated on the premise that school children are a core group for COVID-19 transmission. Using surveillance data from the Chinese cities of Shenzhen and Anqing together, we inferred that compared with the elderly aged 60 and over, children aged 18 and under and adults aged 19–59 were 75% and 32% less susceptible to infection, respectively. Using transmission models parametrized with synthetic contact matrices for 177 jurisdictions around the world, we showed that the lower susceptibility of school children substantially limited the effectiveness of school closure in reducing COVID-19 transmissibility. Our results, together with recent findings that clinical severity of COVID-19 in children is lower, suggest that school closure may not be ideal as a sustained, primary intervention for controlling COVID-19.
This article is part of the theme issue ‘Data science approach to infectious disease surveillance’.
COVID-19 Surveiller: toward a robust and effective pandemic surveillance system based on social media mining
Authors: Jiang Jyun-Yu , Zhou Yichao , Chen Xiusi , Jhou Yan-Ru , Zhao Liqi , Liu Sabrina , Yang Po-Chun , Ahmar Jule , Wang Wei 
Published: November 2021

The outbreak of the novel coronavirus, COVID-19, has become one of the most severe pandemics in human history. In this paper, we propose to leverage social media users as social sensors to simultaneously predict the pandemic trends and suggest potential risk factors for public health experts to understand spread situations and recommend proper interventions. More precisely, we develop novel deep learning models to recognize important entities and their relations over time, thereby establishing dynamic heterogeneous graphs to describe the observations of social media users. A dynamic graph neural network model can then forecast the trends (e.g. newly diagnosed cases and death rates) and identify high-risk events from social media. Based on the proposed computational method, we also develop a web-based system for domain experts without any computer science background to easily interact with. We conduct extensive experiments on large-scale datasets of COVID-19 related tweets provided by Twitter, which show that our method can precisely predict the new cases and death rates. We also demonstrate the robustness of our web-based pandemic surveillance system and its ability to retrieve essential knowledge and derive accurate predictions across a variety of circumstances. Our system is also available athttp://scaiweb.cs.ucla.edu/covidsurveiller/.This article is part of the theme issue ‘Data science approachs to infectious disease surveillance’.
Modelling the geographical spread of HIV among MSM in Guangdong, China: a metapopulation model considering the impact of pre-exposure prophylaxis
Authors: Jing Fengshi , Ye Yang , Zhou Yi , Zhou Hanchu , Xu Zhongzhi , Lu Ying , Tao Xiaoyu , Yang Shujuan , Cheng Weibin , Tian Junzhang , Tang Weiming , Wu Dan 
Published: November 2021

Men who have sex with men (MSM) make up the majority of new human immunodeficiency virus (HIV) diagnoses among young people in China. Understanding HIV transmission dynamics among the MSM population is, therefore, crucial for the control and prevention of HIV infections, especially for some newly reported genotypes of HIV. This study presents a metapopulation model considering the impact of pre-exposure prophylaxis (PrEP) to investigate the geographical spread of a hypothetically new genotype of HIV among MSM in Guangdong, China. We use multiple data sources to construct this model to characterize the behavioural dynamics underlying the spread of HIV within and between 21 prefecture-level cities (i.e. Guangzhou, Shenzhen, Foshan, etc.) in Guangdong province: the online social network via a gay social networking app, the offline human mobility network via the Baidu mobility website, and self-reported sexual behaviours among MSM. Results show that PrEP initiation exponentially delays the occurrence of the virus for the rest of the cities transmitted from the initial outbreak city; hubs on the movement network, such as Guangzhou, Shenzhen, and Foshan are at a higher risk of ‘earliest’ exposure to the new HIV genotype; most cities acquire the virus directly from the initial outbreak city while others acquire the virus from cities that are not initial outbreak locations and have relatively high betweenness centralities, such as Guangzhou, Shenzhen and Shantou. This study provides insights in predicting the geographical spread of a new genotype of HIV among an MSM population from different regions and assessing the importance of prefecture-level cities in the control and prevention of HIV in Guangdong province.
This article is part of the theme issue ‘Data science approach to infectious disease surveillance’.
Data science approaches to confronting the COVID-19 pandemic: a narrative review
Authors: Zhang Qingpeng , Gao Jianxi , Wu Joseph T. , Cao Zhidong , Dajun Zeng Daniel 
Published: November 2021

During the COVID-19 pandemic, more than ever, data science has become a powerful weapon in combating an infectious disease epidemic and arguably any future infectious disease epidemic. Computer scientists, data scientists, physicists and mathematicians have joined public health professionals and virologists to confront the largest pandemic in the century by capitalizing on the large-scale ‘big data’ generated and harnessed for combating the COVID-19 pandemic. In this paper, we review the newly born data science approaches to confronting COVID-19, including the estimation of epidemiological parameters, digital contact tracing, diagnosis, policy-making, resource allocation, risk assessment, mental health surveillance, social media analytics, drug repurposing and drug development. We compare the new approaches with conventional epidemiological studies, discuss lessons we learned from the COVID-19 pandemic, and highlight opportunities and challenges of data science approaches to confronting future infectious disease epidemics.
This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.
Optimal resource allocation in HIV self-testing secondary distribution among Chinese MSM: data-driven integer programming models
Authors: Jing Fengshi , Zhang Qingpeng , Ong Jason J. , Xie Yewei , Ni Yuxin , Cheng Mengyuan , Huang Shanzi , Zhou Yi , Tang Weiming 
Published: November 2021

Human immunodeficiency virus self-testing (HIVST) is an innovative and effective strategy important to the expansion of HIV testing coverage. Several innovative implementations of HIVST have been developed and piloted among some HIV high-risk populations like men who have sex with men (MSM) to meet the global testing target. One innovative strategy is the secondary distribution of HIVST, in which individuals (defined as indexes) were given multiple testing kits for both self-use (i.e.self-testing) and distribution to other people in their MSM social network (defined as alters). Studies about secondary HIVST distribution have mainly concentrated on developing new intervention approaches to further increase the effectiveness of this relatively new strategy from the perspective of traditional public health discipline. There are many points of HIVST secondary distribution in which mathematical modelling can play an important role. In this study, we considered secondary HIVST kits distribution in a resource-constrained situation and proposed two data-driven integer linear programming models to maximize the overall economic benefits of secondary HIVST kits distribution based on our present implementation data from Chinese MSM. The objective function took expansion of normal alters and detection of positive and newly-tested ‘alters’ into account. Based on solutions from solvers, we developed greedy algorithms to find final solutions for our linear programming models. Results showed that our proposed data-driven approach could improve the total health economic benefit of HIVST secondary distribution.
This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.
Clinical Outcome of Neurological patients with COVID-19: the impact of Healthcare organization improvement between waves
Authors: Cristillo Viviana , Pilotto Andrea , Benussi Alberto , Libri Ilenia , Giunta Marcello , Morotti Andrea , Gipponi Stefano , Locatelli Martina , Piccinelli Stefano Cotti , Mazzoleni Valentina , Schiano di Cola Francesca , Masciocchi Stefano , Pezzini Debora , Scalvini Andrea , Premi Enrico , Cottini Elisabetta , Gamba Massimo , Magoni Mauro , Fontanella Marco Maria , Padovani Alessandro 
Published: November 2021

AbstractObjectiveThe aim of this study is to evaluate the differences of clinical presentations and the impact of healthcare organization on outcomes of neurological COVID-19 patients admitted during the first and second pandemic waves.MethodsIn this single center cohort study, we included all patients with SARS-CoV-2 infection admitted to a Neuro-COVID Unit. Demographic, clinical and laboratory data were compared between patients admitted during the first and second waves of COVID-19 pandemic.Results223 patients were included, of whom 112 and 111 hospitalized during the first and second pandemic waves, respectively. Patients admitted during the second wave were younger and exhibited pulmonary COVID-19 severity, resulting in less oxygen support (n=41, 36.9% vs n=79, 70.5%, p<0.001) and lower mortality rates (14.4% vs 31.3%, p=0.004). The different healthcare strategies and early steroid treatment emerged as significant predictors of mortality independently from age, premorbid conditions and COVID-19 severity in cox regression analyses.ConclusionsDifferences in healthcare strategies during the second phase of COVID-19 pandemic probably explain the differences in clinical outcomes independently of disease severity, underlying the importance of standardized early management of neurological patients with SARS-CoV-2 infection.
Red backgrounds have different effects on electrophysiological responses to fearful faces in groups with low and high autistic tendency
Authors: Mu Eveline , Hugrass Laila , Crewther David 
Published: November 2021

AbstractVisual processing differences in the magnocellular pathway have been reported across the autistic spectrum. On the basis that the firing of primate Type IV magnocellular cells is suppressed by diffuse red backgrounds, several groups have used red backgrounds as a means to investigate magnocellular contributions to visual processing in humans. Here, we measured emotional identification accuracy, and compared the P100 and N170 responses from groups with low (n=21; AQ<11) and high (n=22; AQ>22) Autism Spectrum Quotient (AQ) scores, in response to low (LSF) and high (HSF) spatially filtered fearful and neutral face stimuli presented on red and green backgrounds. For the LSF stimuli, the low AQ group correctly identified fearful expressions more often when presented on a red compared to a green background. The low AQ group also showed red backgrounds reduced the effect of LSF fearful expressions on P100 amplitudes. In contrast, the high AQ group showed that background colour did not significantly alter P100 responses to LSF stimuli. Interestingly, red background reduced the effects of HSF stimuli for the high AQ group. The effects of background color on LSF and HSF facial emotion responses were not evident for the N170 component. Our findings suggest that presenting face stimuli on a red background alters both magnocellular and parvocellular contributions to the P100 waveform, and that these effects differ for groups with low and high autistic tendencies. In addition, a theoretical model for explaining the temporal differences in facial emotion processing for low and high AQ groups is proposed.
Supply chain integration and economic performance: empirical evidence from a developing country
Authors: Nguyen Minh Hue , Phan Anh Chi , Matsui Yoshiki 
Published: November 2021

PurposeThis study seeks to investigate the impact of supply chain integration (SCI) practices on economic performance in Vietnamese enterprises.Design/methodology/approachBased on data collected from 144 Vietnamese enterprises, this study employs partial least squares path modeling to analyze the direct and indirect relationships among external integration, information integration, process integration and economic performance.FindingsAnalytical results indicate that of the three SCI practices, external integration has a significant and positive impact on economic performance. Process integration and information integration, on the one hand, reveal insignificant direct impact on economic performance, and on the other, show significant indirect effect on economic performance through external integration.Originality/valueThis study sheds new light on the contribution of SCI to economic performance in developing economies and offers suggestions for not only local management but also foreign investors on the efficient allocation of resources to achieve higher economic performance.
Prepaid debit cards and banking intention
Authors: Anong Sophia T. , Routh Aditi 
Published: November 2021

PurposeThis study examines the relationship between prepaid debit card use and the intention to open a bank account within twelve months. The Transtheoretical Model (TTM) of Behavior Change helped to conceptualize one's stage in the process of changing from unbanked status if desired. The Theory of Planned Behavior (TPB) provided a framework to examine factors that influence banking intention. Prepaid debit card use is considered a social norm as it is a popular alternative to banking, and these accounts have increasingly mimicked bank account features in recent years.Design/methodology/approachThree in-depth focus group interviews with low-income respondents were first conducted in 2012, which revealed a prolific use of prepaid debit cards. Most participants had previous banking history, and despite negative experiences, some requested information about banking terms and “free” banking. These themes and previous studies informed a TPB-based biprobit model, which was estimated using data of an unbanked sample from 2013, 2015 and 2017 waves of the US Survey of Unbanked and Underbanked Households.FindingsThough there was banking interest in the focus groups, no significant empirical association was found between recent prepaid debit card use and banking intention. Going deeper with another sample, we found that current cardholders were equally likely to have become recently banked or to be long-term unbanked but less likely to be long-term banked. Also, factors such as a more recent relationship with banks, use of other alternative financial services for transactions and credit, smartphone ownership, and trust increase banking intention.Research limitations/implicationsThe main limitation of the study is the cross-section quantitative data. Future research may track banking status over time, particularly as financial technology (fintech) evolves with alternatives that may influence banks and customers to adapt.Practical implicationsTo compete with “leapfrog” fintech banking alternatives, bank managers should consider utilizing customer segmentation to target “at-risk” customers and former customers with products and terms tailored to meet their banking needs. Banks can also tailor digital products to capture markets in banking desserts through mobile phones.Originality/valueThis mixed-methods study is unique in that it builds on insights from earlier in-depth interviews with real unbanked groups to examine a trend in prepaid debit card use and the impact on banking interest.
Convex-concave effect of financial flexibility on hospitality performance: quantile regression approach
Authors: Chang Bao-Guang , Wu Kun-Shan 
Published: November 2021

PurposeThe purpose of this paper is to study the influence of financial flexibility (FF) on enterprise performance (EP) within Taiwan’s hospitality industry during the COVID-19 shock and explore whether EP varies with hospitality industry characteristics.Design/methodology/approachSecondary data of 39 Taiwan Stock Exchange-listed hospitality firms were collected from the Taiwan Economic Journal databases. Quantile regression analysis was applied to examine the FF-EP relationshipFindingsThe results evidence that there is a U-shaped (convex) FF-EP relationship for hospitality firms in the 10th, 25th and 50th Tobin’s Q quantiles and in asset-heavy firms. For asset-light firms, FF has an inverted U-shaped (concave) effect on EP in the 90th Tobin’s Q quantilePractical implicationsThe empirical results highlight the need for Taiwan’s hospitality industry as a whole to take rolling adjustment and optimization of FF and concentrate on liquidity risk management after the COVID-19 pandemic and for long-term sustainability.Originality/valueTo the best of the authors’ knowledge, this study is one of the first to examine the nonlinear FF-EP relationship in the hospitality industry of Taiwan, particularly amid the COVID-19 shock. Moreover, this study extends current literature by revealing the hospitality industry’s FF-EP relationship and highlights the importance of the pandemic crisis context.
Insights from sentiment analysis to leverage local tourism business in restaurants
Authors: Yu Ting , Rita Paulo , Moro Sérgio , Oliveira Cristina 
Published: November 2021

Purpose
Social media has become the main venue for users to express their opinions and feelings, generating a vast number of available and valuable data to be scrutinized by researchers and marketers. This paper aims to extend previous studies analyzing social media reviews through text mining and sentiment analysis to provide useful recommendations for management in the restaurant industry.

Design/methodology/approach
The Lexalytics, a text mining artificial intelligence tool, is applied to analyze the text of the online reviews of the restaurants in a touristic Dutch village extracted from the most frequently used social media platforms focusing on the four restaurant quality factors, namely, food and beverage, service, atmosphere and value.

Findings
The findings of this research are presented by the identified key themes with comparisons of the customers’ review sentiment between a selected restaurant, Zwaantje, vis-à-vis its bench-mark restaurants set by a specific approach under the abovementioned quality dimensions, in which the food and beverage and service are the most commented by customers. Results demonstrate that text mining can generate insights from different aspects and that the proposed approach is valuable to restaurant management.

Originality/value
The paper provides a relatively big scale in numbers and resources of social media reviews to further explore the most important service dimensions in the restaurant industry in a specific tourist area. It also offers a useful framework to apply the text mining business intelligence tool by comparison of peers for local small business restaurant practitioners to improve their management skills beyond manually reading social media reviews.
Internal projects as strategic tools: an organisation-level perspective and a research agenda
Authors: Bjorvatn Torbjørn 
Published: November 2021

PurposeThe purpose of this conceptual paper is to describe and explain how organisations use internal projects to implement organisation-level strategy objectives.Design/methodology/approachTheory development with an emphasis on explaining key constructs and their mutual relationships. The theoretical contribution is represented in a diagram along with a detailed verbal account.FindingsThe paper develops a dynamic, cross-level framework to illustrate the organisational processes and outcomes that determine project-based strategy implementation within a single organisation. The interplay between the base organisation and the project, and benefits realisation were singled out as key future research areas. The proposed framework engages with central discourses in the fields of project management, strategic management, innovation studies, knowledge management and organisation studies.Research limitations/implicationsOnly the contours of an organisation-level theory of strategically motivated internal projects are outlined. Future research must elaborate on the complexities, the non-linear relationships and the boundary conditions that follow from the proposed framework.Practical implicationsManagers are alerted to the strategic role of internal projects, how these projects help connect strategy and performance and what the accompanying organisational processes and outcomes look like.Originality/valueThe paper constitutes an early conceptual treatment of strategy-driven internal projects as a distinct project category, thus addressing a major knowledge gap in project studies. Organisational project-management theory is extended with suggestions for future research.