Data Science Advancements in Pandemic and Outbreak Management

Data Science Advancements in Pandemic and Outbreak Management

Eleana Asimakopoulou (Hellenic National Defence College, Greece) and Nik Bessis (Edge Hill University, UK)
ISBN13: 9781799867364|ISBN10: 1799867366|EISBN13: 9781799867388|DOI: 10.4018/978-1-7998-6736-4


Pandemic is not a term to use lightly or carelessly and, if misused, can cause unreasonable fear, or unjustified acceptance that the challenge is over, potentially leading to unnecessary suffering and loss. However, humans are not always capable of avoiding the risks and consequences of such situations. Thus, there is a need to prepare and plan in advance actions in identifying, assessing and responding to such events in order to manage uncertainty and support sustainable livelihood and wellbeing. A detailed assessment of a continuously evolving situation needs to take place and several aspects have to be brought together and examined before the declaration of a pandemic. Various health organisations, 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 in order to advance the disciplines and 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 learnt and specifically in revisiting past and current pandemic approaches. Such approaches involve the strategies and practices from several disciplines and fields, to name a few, healthcare, management, IT, mathematical modelling and data science. Reviewing these approaches as a means to advance in-situ practices and prompt future directions could alleviate or even prevent human, financial and environmental compromise, loss and social interruption via state-of-the-art technologies and frameworks. Leadership without science cannot support such issues in their full potential and therefore the scope of this book is to bring these aspects together.

The primary goal of the book is to demonstrate 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 achievement of such a goal implies the contribution of various practitioners, scholars in the area and researchers from many disciplines who are willing to offer their expertise and skills in advancing their discipline both as a theory and practice. The book also aims to provide conceptual and practical guidance to relevant stakeholders including managers of relevant organizations. It will help assist in identifying and developing effective and efficient approaches, mechanisms, and systems using emerging technologies to support their effective operation.

The overall mission of the book is to introduce both technical and non-technical details of management strategies and advanced IT, data science and mathematical modelling and demonstrate their application 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. The book aims to collect the vast experience of many leaders and as such, to be a definitive state-of-the-art collection suggesting future directions for healthcare stakeholders, decision makers and crisis management officials to identify applicable theories and practices in order to mitigate, prepare for, respond to and recover from future pandemics and outbreaks.

The projected audience is broad, ranging from those currently working in or those who are interested in joining interdisciplinary, multidisciplinary and transdisciplinary collaborative management of pandemics and outbreaks. Specifically, audiences who are: (1) researchers and practitioners in the areas of pandemic and outbreak detection and management; pandemic assessment; contingency planning and business continuity; emerging technologies and advanced IT; artificial intelligence, data science and mathematical modelling; big data and data management; smart systems, cyber-physical systems and industry 4.0; (2) managers and decision makers in the central and local authorities, research institutes and scientific centers and the industry; (3) academics, instructors, researchers and students in colleges and universities.

Table of Contents and List of Contributors

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Author(s)/Editor(s) Biography

Nik Bessis is a full Professor of Computer Science and the Head of Department of Computing at Edge Hill University, UK. Prior to that, Nik was a full Professor of Computer Science and the Director of Distributed and Intelligent Systems (DISYS) research centre at the University of Derby, UK. He holds a PhD and a MA from De Montfort University, UK and a BA from TEI of Athens, Greece. Professor Bessis is a Fellow of HEA, BCS and a Senior Member of IEEE. His research is on social graphs for network and big data analytics as well as on developing data push and resource provisioning services in IoT, FI and clouds. He has led several projects worth over £3m. He has published over 250 works and won 4 best paper awards. He has chaired over 40 international events, delivered 4 keynote speeches, edited 4 SIs, 8 books and 9 conference proceedings. His latest 2 edited books on IoTs & big data have been ranked as top 25 & top 40 on Amazon AI book lists. He is also the founding editor-in-chief of IJDST. Professor Bessis has served as an expert evaluator for the Hellenic QAA and, as an assessor for more than 10 Professorships conferment worldwide.