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Big Data and Analytics for Infectious Disease Research, Operations, and Policy

Author: National Academies of Sciences, Engineering, and Medicine

Publisher: National Academies Press

Published: 2016-11-30

Total Pages: 99

ISBN-13: 0309450144

With the amount of data in the world exploding, big data could generate significant value in the field of infectious disease. The increased use of social media provides an opportunity to improve public health surveillance systems and to develop predictive models. Advances in machine learning and crowdsourcing may also offer the possibility to gather information about disease dynamics, such as contact patterns and the impact of the social environment. New, rapid, point-of-care diagnostics may make it possible to capture not only diagnostic information but also other potentially epidemiologically relevant information in real time. With a wide range of data available for analysis, decision-making and policy-making processes could be improved. While there are many opportunities for big data to be used for infectious disease research, operations, and policy, many challenges remain before it is possible to capture the full potential of big data. In order to explore some of the opportunities and issues associated with the scientific, policy, and operational aspects of big data in relation to microbial threats and public health, the National Academies of Sciences, Engineering, and Medicine convened a workshop in May 2016. Participants discussed a range of topics including preventing, detecting, and responding to infectious disease threats using big data and related analytics; varieties of data (including demographic, geospatial, behavioral, syndromic, and laboratory) and their broader applications; means to improve their collection, processing, utility, and validation; and approaches that can be learned from other sectors to inform big data strategies for infectious disease research, operations, and policy. This publication summarizes the presentations and discussions from the workshop.

Big Data and Analytics for Infectious Disease Research, Operations, and Policy

Author: National Academies of Sciences, Engineering, and Medicine

Publisher: National Academies Press

Published: 2016-12-30

Total Pages: 99

ISBN-13: 030945011X

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Data Analytics for Pandemics

Author: Gitanjali Rahul Shinde

Publisher: CRC Press

Published: 2020-08-30

Total Pages: 85

ISBN-13: 1000204413

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Epidemic trend analysis, timeline progression, prediction, and recommendation are critical for initiating effective public health control strategies, and AI and data analytics play an important role in epidemiology, diagnostic, and clinical fronts. The focus of this book is data analytics for COVID-19, which includes an overview of COVID-19 in terms of epidemic/pandemic, data processing and knowledge extraction. Data sources, storage and platforms are discussed along with discussions on data models, their performance, different big data techniques, tools and technologies. This book also addresses the challenges in applying analytics to pandemic scenarios, case studies and control strategies. Aimed at Data Analysts, Epidemiologists and associated researchers, this book: discusses challenges of AI model for big data analytics in pandemic scenarios; explains how different big data analytics techniques can be implemented; provides a set of recommendations to minimize infection rate of COVID-19; summarizes various techniques of data processing and knowledge extraction; enables users to understand big data analytics techniques required for prediction purposes.

Applied Big Data Analytics and Its Role in COVID-19 Research

Author: Zhao, Peng

Publisher: IGI Global

Published: 2022-04-29

Total Pages: 349

ISBN-13: 1799887952

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There has been a multitude of studies focused on the COVID-19 pandemic across fields and disciplines as all sectors of life have had to adjust the way things are done and adapt to the constantly shifting environment. These studies are crucial as they provide support and perspectives on how things are changing and what needs to be done to stay afloat. Connecting COVID-19-related studies and big data analytics is crucial for the advancement of industrial applications and research areas. Applied Big Data Analytics and Its Role in COVID-19 Research introduces the most recent industrial applications and research topics on COVID-19 with big data analytics. Featuring coverage on a broad range of big data technologies such as data gathering, artificial intelligence, smart diagnostics, and mining mobility, this publication provides concrete examples and cases of usage of data-driven projects in COVID-19 research. This reference work is a vital resource for data scientists, technical managers, researchers, scholars, practitioners, academicians, instructors, and students.

Big Data Analytics and Intelligence

Author: Poonam Tanwar

Publisher: Emerald Group Publishing

Published: 2020-09-30

Total Pages: 392

ISBN-13: 1839090995

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Big Data Analytics and Intelligence is essential reading for researchers and experts working in the fields of health care, data science, analytics, the internet of things, and information retrieval.

Defense Against Biological Attacks

Author: Sunit K. Singh

Publisher: Springer

Published: 2019-03-30

Total Pages: 327

ISBN-13: 3030030539

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This first volume of a two-volume set describes general aspects, such as the historical view on the topic, the role of information distribution and preparedness of health-care systems and preparedness in emergency cases. Part two describes and discusseses in detail the pathogens and toxins that are potentially used for biological attacks. As such, the book is a valuable resource for faculties engaged in molecular biology, genetic engineering, neurology, biodefense, biosafety & biosecurity, virology, and infectious disease programs, as well as professional medical research organizations.

Institutional Review Board: Management and Function

Author: Public Responsibility in Medicine & Research (PRIM&R),

Publisher: Jones & Bartlett Learning

Published: 2021-03-01

Total Pages: 1113

ISBN-13: 1284235521

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Institutional Review Board (IRB) members and oversight personnel face challenges with research involving new technology, management of big data, globalization of research, and more complex federal regulations. Institutional Review Board: Management and Function, Third Edition provides everything IRBs and administrators need to know about efficiently managing and effectively operating a modern and compliant system of protecting human research subjects. This trusted reference manual has been extensively updated to reflect the 2018 revisions to the Federal Policy for the Protection of Human Subjects (Common Rule). An essential resource for both seasoned and novice IRB administrators and members, Institutional Review Board: Management and Function provides comprehensive and understandable interpretations of the regulations, clear descriptions of the ethical principles on which the regulations are based, and practical step-by-step guidance for effectively implementing regulatory oversight.

Data Science for Healthcare

Author: Sergio Consoli

Publisher: Springer

Published: 2019-02-23

Total Pages: 367

ISBN-13: 3030052494

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This book seeks to promote the exploitation of data science in healthcare systems. The focus is on advancing the automated analytical methods used to extract new knowledge from data for healthcare applications. To do so, the book draws on several interrelated disciplines, including machine learning, big data analytics, statistics, pattern recognition, computer vision, and Semantic Web technologies, and focuses on their direct application to healthcare. Building on three tutorial-like chapters on data science in healthcare, the following eleven chapters highlight success stories on the application of data science in healthcare, where data science and artificial intelligence technologies have proven to be very promising. This book is primarily intended for data scientists involved in the healthcare or medical sector. By reading this book, they will gain essential insights into the modern data science technologies needed to advance innovation for both healthcare businesses and patients. A basic grasp of data science is recommended in order to fully benefit from this book.

Leveraging Data Science for Global Health

Author: Leo Anthony Celi

Publisher: Springer Nature

Published: 2020-07-31

Total Pages: 471

ISBN-13: 3030479943

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This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.

Disease Surveillance

Author: Joseph S. Lombardo

Publisher: John Wiley & Sons

Published: 2012-11-09

Total Pages: 326

ISBN-13: 1118569059

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An up-to-date and comprehensive treatment of biosurveillance techniques With the worldwide awareness of bioterrorism and drug-resistant infectious diseases, the need for surveillance systems to accurately detect emerging epidemicsis essential for maintaining global safety. Responding to these issues, Disease Surveillance brings together fifteen eminent researchers in the fields of medicine, epidemiology, biostatistics, and medical informatics to define the necessary elements of an effective disease surveillance program, including research, development, implementation, and operations. The surveillance systems and techniques presented in the text are designed to best utilize modern technology, manage emerging public health threats, and adapt to environmental changes. Following a historical overview detailing the need for disease surveillance systems, the text is divided into the following three parts: Part One sets forth the informatics knowledge needed to implement a disease surveillance system, including a discussion of data sources currently used in syndromic surveillance systems. Part Two provides case studies of modern disease surveillance systems, including cases that highlight implementation and operational difficulties as well as the successes experienced by health departments in the United States, Canada, Europe, and Asia. Part Three addresses practical issues concerning the evaluation of disease surveillance systems and the education of future informatics and disease surveillance practitioners. It also assesses how future technology will shape the field of disease surveillance. This book's multidisciplinary approach is ideal for public health professionals who need to understand all the facets within a disease surveillance program and implement the technology needed to support surveillance activities. An outline of the components needed for a successful disease surveillance system combined with extensive use of case studies makes this book well-suited as a textbook for public health informatics courses