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

Big Data and Analytics for Infectious Disease Research, Operations, and Policy
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
Total Pages: 99
Release: 2016-11-30
Genre: Medical
ISBN: 0309450144

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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

Big Data and Analytics for Infectious Disease Research, Operations, and Policy
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
Total Pages: 99
Release: 2016-12-30
Genre: Medical
ISBN: 030945011X

Download Big Data and Analytics for Infectious Disease Research, Operations, and Policy Book in PDF, ePub and Kindle

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.


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

Applied Big Data Analytics and Its Role in COVID-19 Research
Author: Zhao, Peng
Publisher: IGI Global
Total Pages: 349
Release: 2022-04-29
Genre: Computers
ISBN: 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.


Data Analytics for Pandemics

Data Analytics for Pandemics
Author: Gitanjali Rahul Shinde
Publisher: CRC Press
Total Pages: 85
Release: 2020-08-30
Genre: Computers
ISBN: 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.


Big Data Analytics and Intelligence

Big Data Analytics and Intelligence
Author: Poonam Tanwar
Publisher: Emerald Group Publishing
Total Pages: 392
Release: 2020-09-30
Genre: Business & Economics
ISBN: 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

Defense Against Biological Attacks
Author: Sunit K. Singh
Publisher: Springer
Total Pages: 327
Release: 2019-03-30
Genre: Medical
ISBN: 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

Institutional Review Board: Management and Function
Author: Public Responsibility in Medicine & Research (PRIM&R),
Publisher: Jones & Bartlett Learning
Total Pages: 1113
Release: 2021-03-01
Genre: Medical
ISBN: 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.


Leveraging Data Science for Global Health

Leveraging Data Science for Global Health
Author: Leo Anthony Celi
Publisher: Springer Nature
Total Pages: 471
Release: 2020-07-31
Genre: Medical
ISBN: 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.


Leveraging Data Science for Global Health

Leveraging Data Science for Global Health
Author: Leo Anthony Celi
Publisher: Springer
Total Pages: 475
Release: 2020-09-18
Genre: Medical
ISBN: 9783030479961

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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.


Data Science Advancements in Pandemic and Outbreak Management

Data Science Advancements in Pandemic and Outbreak Management
Author: Asimakopoulou, Eleana
Publisher: IGI Global
Total Pages: 255
Release: 2021-04-09
Genre: Computers
ISBN: 1799867382

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Pandemics are disruptive. Thus, there is a need to prepare and plan actions in advance for identifying, assessing, and responding to such events to manage uncertainty and support sustainable livelihood and wellbeing. A detailed assessment of a continuously evolving situation needs to take place, and several aspects must be brought together and examined before the declaration of a pandemic even happens. Various health organizations; crisis management bodies; and authorities at local, national, and international levels are involved in the management of pandemics. There is no better time to revisit current approaches to cope with these new and unforeseen threats. As countries must strike a fine balance between protecting health, minimizing economic and social disruption, and respecting human rights, there has been an emerging interest in lessons learned and specifically in revisiting past and current pandemic approaches. Such approaches involve strategies and practices from several disciplines and fields including healthcare, management, IT, mathematical modeling, and data science. Using data science to advance in-situ practices and prompt future directions could help alleviate or even prevent human, financial, and environmental compromise, and loss and social interruption via state-of-the-art technologies and frameworks. Data Science Advancements in Pandemic and Outbreak Management demonstrates how strategies and state-of-the-art IT have and/or could be applied to serve as the vehicle to advance pandemic and outbreak management. The chapters will introduce both technical and non-technical details of management strategies and advanced IT, data science, and mathematical modelling and demonstrate their applications and their potential utilization within the identification and management of pandemics and outbreaks. It also prompts revisiting and critically reviewing past and current approaches, identifying good and bad practices, and further developing the area for future adaptation. This book is ideal for data scientists, data analysts, infectious disease experts, researchers studying pandemics and outbreaks, IT, crisis and disaster management, academics, practitioners, government officials, and students interested in applicable theories and practices in data science to mitigate, prepare for, respond to, and recover from future pandemics and outbreaks.