Table Of ContentGLOBAL HEALTHCARE DISASTERS
Predicting the Unpredictable with
Emerging Technologies
GLOBAL HEALTHCARE DISASTERS
Predicting the Unpredictable with
Emerging Technologies
Edited by
Adarsh Garg, PhD
D. P. Goyal, PhD
First edition published 2023
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Library and Archives Canada Cataloguing in Publication
Title: Global healthcare disasters : predicting the unpredictable with emerging technologies / edited by Adarsh Garg,
PhD, D.P. Goyal, PhD.
Names: Garg, Adarsh, editor. | Goyal, D. P., editor.
Description: First edition. | Includes bibliographical references and index.
Identifiers: Canadiana (print) 20220162980 | Canadiana (ebook) 2022016312X | ISBN 9781774910047 (hardcover)
| ISBN 9781774910054 (softcover) | ISBN 9781003282020 (ebook)
Subjects: LCSH: Epidemiology—Forecasting—Data processing—Technological innovations. | LCSH: Emergency
management—Data processing—Technological innovations. | LCSH: Health services administration—Data
processing—Technological innovations.
Classification: LCC RA652.2.D38 G56 2022 | DDC 614.40285—dc23
Library of Congress Cataloging-in-Publication Data
Names: Garg, Adarsh, editor. | Goyal, D. P., editor.
Title: Global healthcare disasters : predicting the unpredictable with emerging technologies / edited by Adarsh Garg, D. P. Goyal.
Description: First edition. | Palm Bay, FL : Apple Academic Press, 2022. | Includes bibliographical references and index. |
Summary: “The recent COVID-19 global pandemic exemplifies the need for efficient, reliable, and real-time tools and
technology for forecasting and predicting healthcare disasters as well as for helping to restrict subsequent spread and
fatality of deadly diseases. This new book discusses many of the innovative and state-of-the-art tools and technology
that can help meet the challenges of predicting such disasters. The chapters offer a plethora of useful information
for designing healthcare disaster management systems that can be dynamically configurable with implementation of
today’s modern technology, such as cloud computing, artificial intelligence, IoT, data analytics, and machine learning.
These can increase effectiveness in remote sensing technologies, data analytics, data storage, communication networks,
geographic information system (GIS), and global positioning System (GPS), to name a few. The book discusses
mathematical models using graph-based approaches for analyzing dynamic, heterogeneous, and unstructured data for
applications in epidemiology. The authors also address the use of mobile applications for communication efforts and
remote monitoring for gauging health and the effectiveness of preventive healthcare measures. The chapters discuss
influencing factors that directly or indirectly target public health infrastructure that can lead to or exacerbate global
health crises, such as extreme climate changes, refugee health crises, terrorism and cyberterrorism, and technology-
related incidents. The book further looks at efficient methods to analyze disasters and how to deliver healthcare in
areas of conflict and crisis. This important volume, Global Healthcare Disasters: Predicting the Unpredictable with
Emerging Technologies, provides a bounty of useful information for health professionals, academicians, researchers,
governmental agencies, and policymakers across the world to predict, mitigate, and manage global health disaster with
emerging technologies”-- Provided by publisher.
Identifiers: LCCN 2022004846 (print) | LCCN 2022004847 (ebook) | ISBN 9781774910047 (hardback) | ISBN
9781774910054 (paperback) | ISBN 9781003282020 (ebook)
Subjects: MESH: Medical Informatics | Disease Outbreaks | COVID-19 | Disasters | Models, Theoretical | Forecasting
Classification: LCC R859 (print) | LCC R859 (ebook) | NLM WA 105 | DDC 362.10285--dc23/eng/20220307
LC record available at https://lccn.loc.gov/2022004846
LC ebook record available at https://lccn.loc.gov/2022004847
ISBN: 978-1-77491-004-7 (hbk)
ISBN: 978-1-77491-005-4 (pbk)
ISBN: 978-1-00328-202-0 (ebk)
Dedicated to the Victims of COVID-19
—Editors
About the Editors
Adarsh Garg, PhD
Adarsh Garg, PhD, is currently Professor (IT and Analytics), GL Bajaj
Institute of Management and Research, Greater Noida, India. She was
formerly Professor at Galgotias University, Gautam Buddh Nagar, Greater
Noida, India, and Visiting Professor at Delhi Technical University, Delhi,
India. Prior to joining Galgotias University, she worked with organizations
including WIPRO Tech, GE, IMT Ghaziabad, Punjabi University, and
Patiala. She is currently supervising several PhD candidates. Dr. Garg has
published over 50 research papers in refereed international/national journals
and conference proceedings. She is a member of various professional
bodies, including the Computer Society of India and the ACM-Computer
Science Teachers’ Association. She has 20 years of teaching, corporate,
and research within the areas of her interest, which include business
analytics, data mining, business intelligence, MIS, e-learning, and project
management.
D. P. Goyal, PhD
D. P. Goyal, PhD, Director and Professor at the Indian Institute of
Management, Shillong, India, and is also a Visiting Professor at Aarhus
University, Denmark. Prior to joining IIM Shillong, he was Professor at
MDI¸ Gurgaon, India.. He has more than 32 years of corporate, teaching,
and research experience to his credit. His teaching and research interest
areas include IS value, IS strategy, MIS, e-government, and business
process management. He has published over 100 research papers in
refereed international/national journals and conference proceedings. He
is a member of various professional bodies and is associated with many
business schools and universities in the capacity of member of board of
governors, academic council, board of studies, academic advisory board,
etc. He has been a consultant to the government of India for various
assignments. He has authored almost 20 books, including textbooks on
management information systems, IT project management, and enterprise
resource planning.
Contents
Contributors ......................................................................................................... xi
Abbreviations .....................................................................................................xiii
Acknowledgments .............................................................................................. xvii
Preface ............................................................................................................... xix
1. Role of Knowledge Graphs in Analyzing Epidemics and
Health Disasters .......................................................................................... 1
Sushmita Singh and Manvi Siwach
2. Forecasting of COVID-19 with the ARIMA Model in India
as a Preventive Measure of Healthcare Catastrophe .............................19
Sanjeev Kr. Jain and Kapil Mohan Garg
3. Role of Artificial Intelligence in the Era of COVID-19 to
Improve Hospital Management ...............................................................37
Ruqaiya Khanam
4. Challenges of Global Healthcare Disasters ............................................. 49
Deepika Sherawat, Sonia, and Priyanka Shukla
5. Healthcare Disaster Prediction with IoT, Data Analytics,
and Machine Learning ............................................................................. 71
Priyanka Shukla, Akanksha Sehgal, Sonia, and Deepika Sherawat
6. Effectiveness of Aarogya Setu Mobile Application
During COVID-19 Healthcare Management:
A Technology Acceptance Model-Based Approach ................................93
Sarika Sharma and D. P. Goyal
7. CoReS-Respiratory Strength Predicting Framework
Using Noninvasive Technology for Remote Monitoring
During Heath Disasters .......................................................................... 109
Adarsh Garg
