scholarly journals Big data driven COVID-19 pandemic crisis management: Potential approach for global health

2021 ◽  
Author(s):  
Yang Lv ◽  
Chenwei Ma ◽  
Xiaohan Li ◽  
Min Wu
Keyword(s):  
Systematic Review ◽  
Big Data ◽  
Virus Infection ◽  
Global Health ◽  
Qualitative Analysis ◽  
Crisis Management ◽  
Data Driven ◽  
Potential Approach ◽  
Genetic Modifications ◽  
Big Data Technologies

IntroductionThis study aims to explore the potential of big data technologies for controlling COVID-19 transmission and managing effectively.Material and methodsA systematic review guided by PRISMA guidelines has been followed to obtain the key elements.ResultsThis study identified the most relevant 32 documents for qualitative analysis. It also reveals 10 possible sources and 8 key applications of big data for analyzing the virus infection trend, transmission pattern, virus association, and differences of genetic modifications.ConclusionsThe findings will provide new insight and help policymakers, and administrators to develop data-driven initiatives to tackle and manage the COVID-19 crisis.

scholarly journals A Systematic Review on Papers That Study on SNPs That Affect SARS-CoV-2 Infection & COVID-19 Severity

2021 ◽  
Author(s):  
Siyeon Suh ◽  
Sol Lee ◽  
Ho Gym ◽  
Sun Ha Jee ◽  
Sanghyuk Yoon ◽  
...  
Keyword(s):  
Systematic Review ◽  
Virus Infection ◽  
Global Health ◽  
Full Text ◽  
Clinical Studies ◽  
Time History ◽  
The World ◽  
History Of ◽  
Severity Of The Disease

Abstract Background:COVID-19, caused by SARS-CoV-2 has become the most threatening issue to all populations around the world. It is directly and indirectly affecting all of us and thus, is a emergence topic dealt in global health. In order to avoid the infection, various studies have been done and still ongoing. Now having over 141 million cases of COVID19 and causing over 3 million deaths around the world, the tendency of infection and degree severity of the disease shown in different groups of people came up as an issue. Here, we reviewed 21 papers on SNPs related to SARS-CoV-2 infection severity and analyzed the results of them.Methods:The PubMed databases were searched for papers discussing SNPs associated with SARS-CoV-2 infection severity. Clinical studies with human patients and statistically showing relevance of the SNP with virus infection were included. Quality Assessment of all papers were done with Newcastle Ottawa Scale.Results:In the analysis, 21 full-text literatures out of 2956 screened titles and abstracts, including 63496 cases, were included. All were human based clinical studies, some based on certain regions gathered patient data and some based on big databases obtained online. ACE2, TMPRSS2, IFITM3 are the genes mentioned most frequently that are related with SARS-CoV-2 infection. 20 out of 21 studies mentioned one of more of those genes. The relevant genes according to SNPs were also analyzed. rs12252-C, rs143936283, rs2285666, rs41303171, and rs35803318 are the SNPs that were mentioned at least twice in two different studies.Conclusions: We found that ACE2, TMPRSS2, IFITM3 are the major genes that are involved in SARS-CoV-2 infection. The mentioned SNPs were all related to one or more of the above mentioned genes. There were discussions on certain SNPs that increased the infection severity to certain ethinic groups more than the others. However, as there is limited follow up and data due to shortage of time history of the disease, studies may be limited.

scholarly journals Urban Crime and Security

2021 ◽  
pp. 213-228
Author(s):  
Tao Cheng ◽  
Tongxin Chen
Keyword(s):  
Resource Allocation ◽  
Big Data ◽  
Crime Prevention ◽  
Urban Crime ◽  
Data Driven ◽  
Big Data Technologies ◽  
Urban Security ◽  
Made In

AbstractScientists have an enduring interest in understanding urban crime and developing security strategies for mitigating this problem. This chapter reviews the progress made in this topic from historic criminology to data-driven policing. It first reviews the broad implications of urban security and its implementation in practice. Next, it focuses on the tools to prevent urban crime and improve security, from analytical crime hotspot mapping to police resource allocation. Finally, a manifesto of data-driven policing is proposed, with its practical demand for efficient security strategies and the development of big data technologies. It emphasizes that data-driven strategies could be applied in cities due to their promising effectiveness for crime prevention and security improvement.

scholarly journals Why Personal Dreams Matter: How professionals affectively engage with the promises surrounding data-driven healthcare in Europe

2022 ◽  
Vol 9 (1) ◽  
pp. 205395172110706
Author(s):  
Marthe Stevens ◽  
Rik Wehrens ◽  
Johanna Kostenzer ◽  
Anne Marie Weggelaar-Jansen ◽  
Antoinette de Bont
Keyword(s):  
Big Data ◽  
Science And Technology Studies ◽  
Data Science ◽  
Data Driven ◽  
Grand Narrative ◽  
Healthcare Staff ◽  
Critical Data ◽  
Key Players ◽  
Big Data Technologies ◽  
Critical Policy

Recent buzzes around big data, data science and artificial intelligence portray a data-driven future for healthcare. As a response, Europe's key players have stimulated the use of big data technologies to make healthcare more efficient and effective. Critical Data Studies and Science and Technology Studies have developed many concepts to reflect on such overly positive narratives and conduct critical policy evaluations. In this study, we argue that there is also much to be learned from studying how professionals in the healthcare field affectively engage with this strong European narrative in concrete big data projects. We followed twelve hospital-based big data pilots in eight European countries and interviewed 145 professionals (including legal, governance and ethical experts, healthcare staff and data scientists) between 2018 and 2020. In this study, we introduce the metaphor of dreams to describe how professionals link the big data promises to their own frustrations, ideas, values and experiences with healthcare. Our research answers the question: how do professionals in concrete data-driven initiatives affectively engage with European Union's data hopes in their ‘dreams’ – and with what consequences? We describe the dreams of being seen, of timeliness, of connectedness and of being in control. Each of these dreams emphasizes certain aspects of the grand narrative of big data in Europe, makes particular assumptions and has different consequences. We argue that including attention to these dreams in our work could help shine an additional critical light on the big data developments and stimulate the development of responsible data-driven healthcare.

scholarly journals Data-driven smart eco-cities and sustainable integrated districts: A best-evidence synthesis approach to an extensive literature review

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Simon Elias Bibri
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Urban Ecology ◽  
Smart Cities ◽  
Evidence Synthesis ◽  
Data Driven ◽  
Extensive Literature ◽  
Best Evidence Synthesis ◽  
Big Data Technologies ◽  
Smart Urbanism

AbstractAs materializations of trends toward developing and implementing urban socio-technical and enviro-economic experiments for transition, eco-cities have recently received strong government and institutional support in many countries around the world due to their ability to function as an innovative strategic niche where to test and introduce various  reforms. There are many models of the eco-city based mainly on either following the principles of urban ecology or combining the strategies of sustainable cities and the solutions of smart cities. The most prominent among these models are sustainable integrated districts and data-driven smart eco-cities. The latter model represents the unprecedented transformative changes the eco-city is currently undergoing in light of the recent paradigm shift in science and technology brought on by big data science and analytics.  This is motivated by the growing need to tackle the problematicity surrounding eco-cities in terms of their planning, development, and governance approaches and practices. Employing a combination of both best-evidence synthesis and narrative approaches, this paper provides a comprehensive state-of-the-art and thematic literature review on sustainable integrated districts and data-driven smart eco-cities. The latter new area is a significant gap in and of itself that this paper seeks to fill together with to what extent the integration of eco-urbanism and smart urbanism is addressed in the era of big data, what driving factors are behind it, and what forms and directions it takes. This study reveals that eco-city district developments are increasingly embracing compact city strategies and becoming a common expansion route for growing cities to achieve urban ecology or urban sustainability. It also shows that the new eco-city projects are increasingly capitalizing on data-driven smart technologies to implement environmental, economic, and social reforms. This is being accomplished by combining the strengths of eco-cities and smart cities and harnessing the synergies of their strategies and solutions in ways that enable eco-cities to improve their performance with respect to sustainability as to its tripartite composition. This in turn means that big data technologies will change eco-urbanism in fundamental and irreversible ways in terms of how eco-cities will be monitored, understood, analyzed, planned, designed, and governed. However, smart urbanism poses significant risks and drawbacks that need to be addressed and overcome in order to achieve the desired outcomes of ecological sustainability in its broader sense. One of the key critical questions raised in this regard pertains to the very potentiality of the technocratic governance of data-driven smart eco-cities and the associated negative implications and hidden pitfalls. In addition, by shedding light on the increasing adoption and uptake of big data technologies in eco-urbanism, this study seeks to assist policymakers and planners in assessing the pros and cons of smart urbanism when effectuating ecologically sustainable urban transformations in the era of big data, as well as to stimulate prospective research and further critical debates on this topic.

scholarly journals Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review

2022 ◽  
Vol 16 (1) ◽  
pp. e0010056
Author(s):  
Emmanuelle Sylvestre ◽  
Clarisse Joachim ◽  
Elsa Cécilia-Joseph ◽  
Guillaume Bouzillé ◽  
Boris Campillo-Gimenez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Big Data ◽  
Real World ◽  
Data Sources ◽  
Data Driven ◽  
Real World Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Dengue Prediction

Background Traditionally, dengue surveillance is based on case reporting to a central health agency. However, the delay between a case and its notification can limit the system responsiveness. Machine learning methods have been developed to reduce the reporting delays and to predict outbreaks, based on non-traditional and non-clinical data sources. The aim of this systematic review was to identify studies that used real-world data, Big Data and/or machine learning methods to monitor and predict dengue-related outcomes. Methodology/Principal findings We performed a search in PubMed, Scopus, Web of Science and grey literature between January 1, 2000 and August 31, 2020. The review (ID: CRD42020172472) focused on data-driven studies. Reviews, randomized control trials and descriptive studies were not included. Among the 119 studies included, 67% were published between 2016 and 2020, and 39% used at least one novel data stream. The aim of the included studies was to predict a dengue-related outcome (55%), assess the validity of data sources for dengue surveillance (23%), or both (22%). Most studies (60%) used a machine learning approach. Studies on dengue prediction compared different prediction models, or identified significant predictors among several covariates in a model. The most significant predictors were rainfall (43%), temperature (41%), and humidity (25%). The two models with the highest performances were Neural Networks and Decision Trees (52%), followed by Support Vector Machine (17%). We cannot rule out a selection bias in our study because of our two main limitations: we did not include preprints and could not obtain the opinion of other international experts. Conclusions/Significance Combining real-world data and Big Data with machine learning methods is a promising approach to improve dengue prediction and monitoring. Future studies should focus on how to better integrate all available data sources and methods to improve the response and dengue management by stakeholders.

scholarly journals Defining global health: findings from a systematic review and thematic analysis of the literature

2021 ◽  
Vol 6 (6) ◽  
pp. e005292
Author(s):  
Melissa Salm ◽  
Mahima Ali ◽  
Mairead Minihane ◽  
Patricia Conrad
Keyword(s):  
Systematic Review ◽  
Global Health ◽  
Qualitative Analysis ◽  
Health Improvement ◽  
Qualitative Synthesis ◽  
Common Definition ◽  
Multiple Meanings ◽  
Definition Of ◽  
Key Aspects ◽  
Historical Antecedents

IntroductionDebate around a common definition of global health has seen extensive scholarly interest within the last two decades; however, consensus around a precise definition remains elusive. The objective of this study was to systematically review definitions of global health in the literature and offer grounded theoretical insights into what might be seen as relevant for establishing a common definition of global health.MethodA systematic review was conducted with qualitative synthesis of findings using peer-reviewed literature from key databases. Publications were identified by the keywords of ‘global health’ and ‘define’ or ‘definition’ or ‘defining’. Coding methods were used for qualitative analysis to identify recurring themes in definitions of global health published between 2009 and 2019.ResultsThe search resulted in 1363 publications, of which 78 were included. Qualitative analysis of the data generated four theoretical categories and associated subthemes delineating key aspects of global health. These included: (1) global health is a multiplex approach to worldwide health improvement taught and pursued at research institutions; (2) global health is an ethically oriented initiative that is guided by justice principles; (3) global health is a mode of governance that yields influence through problem identification, political decision-making, as well as the allocation and exchange of resources across borders and (4) global health is a vague yet versatile concept with multiple meanings, historical antecedents and an emergent future.ConclusionExtant definitions of global health can be categorised thematically to designate areas of importance for stakeholders and to organise future debates on its definition. Future contributions to this debate may consider shifting from questioning the abstract ‘what’ of global health towards more pragmatic and reflexive questions about ‘who’ defines global health and towards what ends.

scholarly journals Business Models and Ecosystem for Big Data

2021 ◽  
pp. 269-288
Author(s):  
Sonja Zillner
Keyword(s):  
Big Data ◽  
Virtual Worlds ◽  
Business Models ◽  
Value Added ◽  
Systematic Investigation ◽  
Data Driven ◽  
Strategic Partnerships ◽  
New Business ◽  
Big Data Technologies ◽  
Value Propositions

AbstractWith the recent technical advances in digitalisation and big data, the real and the virtual worlds are continuously merging, which, again, leads to entire value-added chains being digitalised and integrated. The increase in industrial data combined with big data technologies triggers a wide range of new technical applications with new forms of value propositions that shift the logic of how business is done. To capture these new types of value, data-driven solutions for the industry will require new business models. The design of data-driven AI-based business models needs to incorporate various perspectives ranging from customer and user needs and their willingness to pay for new data-driven solutions to data access and the optimal use of technologies, while taking into account the currently established relationships with customers and partners. Successful data-driven business models are often based on strategic partnerships, with two or more players establishing the basis for sustainable win-win situations through transparent resource-, investment-, risk-, data- and value-sharing. This chapter will explore the different data-driven business approaches and highlight in this context the importance of functioning ecosystems on the various levels. The chapter will conclude with an introduction to the data-driven innovation framework, a proven methodology to guide the systematic investigation of data-driven business opportunities while incorporating the dynamics of the underlying ecosystems.

scholarly journals Big Data in Manufacturing

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Tolulope J. Ashaolu ◽  
Abayomi Ajayi-Majebi ◽  
Sarhan M. Musa
Keyword(s):  
Big Data ◽  
Interdisciplinary Research ◽  
Data Analytics ◽  
Manufacturing Sector ◽  
Research Area ◽  
Data Driven ◽  
Manufacturing Processes ◽  
Big Data Technologies

In the data-driven economy, turning data into actionable analytics is the best way to boost efficiency, quality, and productivity. The manufacturing processes are getting more and more complex due to increasing demands. Manufacturers of all types of products are finding significant value in big data. The application of big data technologies in manufacturing sector is a relatively new interdisciplinary research area which incorporates automation, engineering, and data analytics. This paper provides an introduction on the use of big data in manufacturing.

scholarly journals Equilibrium of Labor Market: New Security Instruments in the Context of Digitalization

2021 ◽  
Vol 93 ◽  
pp. 03017
Author(s):  
Alena Vankevich ◽  
Iryna Kalinouskaya ◽  
Olga Zaitseva ◽  
Alena Korabava
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Labor Market ◽  
Qualitative Analysis ◽  
Market Analysis ◽  
Economic Activities ◽  
Current Information ◽  
Market Requirements ◽  
Big Data Technologies

The actual methods of labor market analysis are based on outdated technologies for collecting information and do not consider the competencies available in the CV and in demand by vacancies. In order to obtain reliable current information on the balance of the workforce quality, as the carrier of certain competencies, and market requirements, the method is proposed for determining the degree of their consistency through the ratio of competencies available to applicants and those requested by employers. The proposed methodology, based on big data technologies, uses artificial intelligence as the main toolkit which makes it possible to quickly and efficiently collect, process and visualize the obtained data, which makes it possible to conduct its further qualitative analysis in the context of the proposed / demanded competencies, professions, regions and types of economic activities. As a practical interpretation of the proposed methodology, the paper analyzes the degree of consistency of existing / demanded competencies in the context of the regions of Belarus.

scholarly journals Big Data Technologies in Political Processes: Risks and Opportunities

2020 ◽  
Vol 9 (6) ◽  
pp. 143-149 ◽  
Author(s):  
D. R. Mukhametov
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Political Institutions ◽  
Personal Data ◽  
Data Driven ◽  
Grey Zone ◽  
Political Processes ◽  
Personal Data Protection ◽  
Big Data Technologies ◽  
Political Agents

The article deals with various aspects related to the use of Big Data technologies in political processes. Digital technologies have an ambivalent impact on the social and political processes, creating the “grey zone” of opportunities and resources that are the subject of conflicts and competition among various political agents. This statement is equally true concerning election campaigns. Firstly, the author describes the concept of data-driven campaign, which is rapidly spreading due to the demand for flexible management mechanisms and the formation of the “attention economy”. The implementation of the concept includes processes of data mining and analysis, microtargeting — the article reveals the content of each stage on the example of recent cases. The essential advantage of using big data analysis in political processes is concluded not only in the scale of the data mining but also in the possibility to examine deep causal relationships and dependencies, which extends the range of opportunities to influence political agents behaviour. Secondly, it is possible to extrapolate mechanisms of data-driven campaign to the level of data-driven politics. The author formulates the major risks and threats associated with the use of Big Data in political processes: funnel of mistrust in political institutions and technologies, blurring political institutions and plebiscite democracy, the preservation and confidentiality of personal data, the consequences of algorithms cognitive restrictions. As a result, in the short term it will be relevant to provide institutional regulation of data using, as well as to support the development of human capital as the basic skills of personal data protection and the use of modern technologies.

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