The recent outbreak of coronavirus (Covid-19) reminded the world of the devastating impact of epidemic and pandemic outbreaks. The outbreak hit China hard, and continues to spread around the globe. Epidemics can be rapidly spread by a group of infectious agents through several methods, threatening the health of a large number of people in a very short time (Medina 2018). The threat to global healthcare from emerging and reemerging epidemics remains critical, and the capacity of pandemic preparedness to confront such threats needs to be strengthened. There is a need for research in the effectiveness of preparedness systems, and in epidemic monitoring to help stabilize economic activities and reduce systematic risks. This would be greatly aided by high-performance decision support systems to keep track of verified events with known or possible impacts on public health or financial market, providing useful data analytic capacities and suggesting proper and efficient reactions. Data Analytics and Artificial Intelligence (AI) based decision support technologies has also shown its potential in the analysis of epidemic diseases, including effectively pre-empting, preventing and combating the threats of infectious disease epidemic; facilitating understanding of health-seeking behaviors and control of public emotions during epidemics (Ginsberg et al., 2009). Today we have a great deal of health data, but utilizing this data in an effective manner is highly challenging. AI offers new tools for public health practitioners and policy makers to revolutionize healthcare and population health through focused, context-specific interventions (Wu et al. 2016, Nam and Seong. 2019, Wu et al. 2019, Chaudhuri and Bose, 2020, Müller-Peltzer et al. 2020, Liu et al. 2020).
This call for papers of Decision Support Systems on the theme of “Data Analytics and Decision-Making Systems” is intended to publish insights and viewpoints from scholars regarding risk and data analytics in healthcare decision systems. Authors are encouraged to submit their articles addressing the theme of this special issue which are main focus on decision support system in epidemic and pandemic healthcare systems and cases.
Topics of Interest: The special issue aims to address the following, but not limited to, potential topics in decision support system and data analytics and their applications:
AI-based behaviors pattern cognition of epidemic outbreak
Social-networks analysis applied to healthcare systems
Data Analytics and recommendation system
Decision making in emergence healthcare
Covid-19 effect on operations and financial information systems
Text and sentiment analysis of pandemic
Innovative decision-making approaches/methods and technologies applied to epidemic control
Information systems impact on health risk propagation control
Intelligent system for emergence resource allocation of healthcare systems
Emergency workflow management during epidemics and pandemics
Other topics related to data analytics and decision support as applied to epidemics and pandemics
Submitted articles must not have been previously published or currently submitted for journal publication elsewhere. As an author, you are responsible for understanding and adhering to our submission guidelines. You can access them from https://www.journals.elsevier.com/decision-support-systems/.
Please read these before submitting your manuscript. Each paper will go through a rigorous review process.
Deadline of Manuscript Submission: 30 December 2020
Final Decision Due: 31 July 2021
Tentative Publication Date: 30 November 2021
Desheng Dash Wu, University of Chinese Academy of Sciences, Stockholm University, Email email@example.com, firstname.lastname@example.org (Managing Guest Editor)
David L. Olson, University of Nebraska, USA, E-mail: email@example.com
James H. Lambert, University of Virginia, USA, E-mail: firstname.lastname@example.org
Guest Associate Editors:
Professor Asil Oztekin，Professor ，Email: email@example.com
Professor Bart Baesens, Professor ，Email: firstname.lastname@example.org
Professor Bhavik Pathak, Professor ，Email: email@example.com
Professor David C. Novak, Professor ，Email:David.Novak@uvm.edu
Professor Dursun Delen, Patterson Foundation Chair, Email: firstname.lastname@example.org
Professor David Sundaram, Professor ，Email: email@example.com
Professor Georgios Sermpinis, Professor ，Email: Georgios.Sermpinis@glasgow.ac.uk
Professor Indranil Bose, Professor ，Email: firstname.lastname@example.org
Professor J. Izquierdo，Professor ，Email: email@example.com
Professor Jiamin Wang, Professor, Email: firstname.lastname@example.org
Professor Jianjun Wu, Professor, Email: email@example.com
Professor Kristof Coussement, Professor ，Email: firstname.lastname@example.org
Professor Liu Dengpan, Professor, Email: email@example.com
Professor Mark Cecchini，Professor ，Email: firstname.lastname@example.org
Professor Maryam Ghasemaghaei，Professor ，Email: email@example.com
Professor Minqiang Li，Professor ，Email: firstname.lastname@example.org
Professor Marijn Janssen ，Professor ，Email: M.F.W.H.A.Janssen@tudelft.nl
Professor Mohamed Wahab Mohamed Ismail, Professor, Email: email@example.com
Professor Paulo Cortez，Professor ，Email: firstname.lastname@example.org
Professor Shiji Song, Professor, Email: email@example.com
Professor Steven M. Thompson, Professor ，Email:firstname.lastname@example.org
Professor Selwyn Piramuthu, Professor ，Email: selwyn. piramuthu @warrington.ufl.edu
Professor Thomas Chesney，Professor ，Email: Thomas.Chesney@nottingham.ac.uk
Professor Vincenzo Piuri, Professor ，Email: email@example.com
Professor Weiguo Zhang，Professor ，Email: firstname.lastname@example.org
Professor Zheng Xiaolong, Professor, Email: email@example.com
Dr. Amanda Luo, Associate Professor, Email: firstname.lastname@example.org;
Dr. Dexter Wu, Associate Professor, Email: email@example.com
Dr. Shuzhen Chen, Assistant Professor, Email: firstname.lastname@example.org
Dr. Tianyu Wang, Research Associate, Email: email@example.com
Dr. Yuan Bian, Research Associate, Email: firstname.lastname@example.org
Medina, R.A. “1918 influenza virus: 100 years on, are we prepared against the next influenza pandemic?” Nature Reviews Microbiology 16.2 (2018): 61.
Ginsberg, Jeremy, et al. “Detecting influenza epidemics using search engine query data.” Nature 457.7232 (2009): 1012-1014.
Wu, Desheng Dash, et al. “Bi‐level programing merger evaluation and application to banking operations.” Production and Operations Management 25.3 (2016): 498-515.
Nam, KiHwan, and NohYoon Seong. “Financial news-based stock movement prediction using causality analysis of influence in the Korean stock market.” Decision Support Systems 117 (2019): 100-112.
Wu, D., Zhang, B., & Baron, O. A trade credit model with asymmetric competing retailers. Production and Operations Management, 28.3 (2019): 206-231.
Müller-Peltzer, Michael, et al. “Longitudinal Healthcare Analytics for Disease Management: Empirical Demonstration for Low Back Pain.” Decision Support Systems (2020).
Chaudhuri, Neha, and Indranil Bose. “Exploring the role of deep neural networks for post-disaster decision support.” Decision Support Systems (2020): 113234.
Liu, Xinying, and Upkar Varshney. “Mobile health: A carrot and stick intervention to improve medication adherence.” Decision Support Systems 128 (2020): 113165.