International Journal of Production Research

Production of Healthcare under

Epidemic Outbreaks

Guest editors:

  • Professor Desheng Wu, University of Chinese Academy of Sciences and Stockholm University, China/Sweden, Email  (Managing guest editor)

  • Professor Alexandre Dolgui, Head of Automation, Production and Computer Sciences Dept., IMT Atlantique, Nantes, France,  Email:

  • Professor David L. Olson, University of Nebraska, USA, E-mail:

  • Professor Xiaolan Xie, Heads of the Research department on Healthcare Engineering at Mines Saint-Etienne, France, Email:


Submission deadline:​

Deadline of Manuscript Submission: 31st August 2021 (Extended)

Submit here:


About the special issue

A recent outbreak of coronavirus (2019-nCoV) occurred in China, reminding us of the horror of countrywide epidemics such as SARS, MERS, Ebola (de Wit et al. 2016). Communicable or infectious diseases are also a major cause of mortality in the aftermath of natural or man-made disasters. Epidemic sickness can rapidly spread by a group of infectious agents through several methods of interactions, threatening the health of a large number of people in a very short time (Medina 2018).


The threat of emerging and re-emerging infectious diseases to global healthcare remains critical, and the capacity of pandemic preparedness to confront such threats is of great importance. Effective responses of an epidemic outbreak would help stabilize economic activities and reduce systematic risks. Available resources such as essential medical supplies and well-trained personnel need to be deployed rapidly in an optimized manner. In order to contain epidemics before irreversible consequences, information channels must be transparently managed in conjunction with financial resources from both government and social support. Therefore, quick responses of an emergency healthcare systems during containment efforts are of paramount importance.


In light of the early efforts of Ginsberg et al. (2009), Data Analytics and Artificial Intelligence (AI) has proven to have significant potential in risk identification and assessment: effectively pre-empting, preventing and combating the threats of infectious disease epidemics; facilitating understanding of health-seeking behaviors and public emotions during epidemics.


Today’s world of seamless boundaries and global interconnectivity has created an explosion of health data increasing from 500 petabytes in 2012 to 25,000 petabytes in 2020 (Feldman, Martin, & Skotnes 2012). From a systems-thinking perspective, AI offers new tools for public health practitioners and policy makers to revolutionize healthcare and population health through focused, context-specific interventions, expanding access to health information and services (Kao et al. 2014, Li et al. 2017, Anparasan and Miguel 2018, Wirz et al. 2018, Cai et al. 2019, Chen et al. 2019, Wen et al. 2019, Wang et al. 2020, Ganasegeran and Surajudeen 2020,).

This call for papers for the International Journal of Production Research on the theme of “Production of Healthcare” is intended to obtain insights and viewpoints from scholars regarding risk and analytics in healthcare production. Authors are encouraged to submit their articles addressing the theme of this special issue which are main focus on healthcare production.

Topics of interest:


The special issue aims to address the following, but not limited to, potential topics in healthcare risk modelling and their applications:

  • Innovative strategies to limit risk of epidemic disease propagation

  • Risk mitigation in healthcare with advanced analytics

  • Queuing modelling in healthcare

  • Simulation of outbreak events

  • Big data-driven health risk identification

  • AI-based epidemic network analysis

  • Estimating the risk of global economic costs of Coronavirus

  • MCDM models in field of healthcare risk management

  • How to manage risk of future outbreaks (prevention, control and treatment)

  • Response models during epidemic outbreaks

  • IoT application in healthcare

  • Interdisciplinary approaches and decision-making tools in healthcare risk analysis

  • Cloud-based framework for social media analysis

  • Emergency management of resource allocation

  • Humanitarian logistics dealing with uncertainties

  • Other topics related to healthcare risk analytics


  1. de Wit, Emmie, et al. "SARS and MERS: recent insights into emerging coronaviruses." Nature Reviews Microbiology 14.8 (2016): 523.

  2. Medina, Rafael A. "1918 influenza virus: 100 years on, are we prepared against the next influenza pandemic?" Nature Reviews Microbiology 16.2 (2018): 61.

  3. Ginsberg, Jeremy, et al. "Detecting influenza epidemics using search engine query data." Nature 457.7232 (2009): 1012-1014.

  4. Feldman, B., Martin, E. M., & Skotnes, T. (2012). Big data in healthcare hype and hope. Dr. Bonnie, 360, 122–125.

  5. Kao, Rowland R., et al. "Supersize me: how whole-genome sequencing and big data are transforming epidemiology." Trends in microbiology 22.5 (2014): 282-291.

  6. Li, Na, et al. "Evaluation of reverse referral partnership in a tiered hospital system–A queuing-based approach." International Journal of Production Research 55.19 (2017): 5647-5663.

  7. Anparasan, Azrah A., and Miguel A. Lejeune. "Data laboratory for supply chain response models during epidemic outbreaks." Annals of Operations Research 270.1-2 (2018): 53-64.

  8. Wirz, Christopher D., et al. "Rethinking social amplification of risk: Social media and Zika in three languages." Risk Analysis 38.12 (2018): 2599-2624.

  9. Cai, Guofa, et al. "QoS-Aware Buffer-Aided Relaying Implant WBAN for Healthcare IoT: Opportunities and Challenges." IEEE Network 33.4 (2019): 96-103.

  10. Chen, Wuhua, Zhe George Zhang, and Xiaohong Chen. "On two-tier healthcare system under capacity constraint." International Journal of Production Research (2019): 1-21..

  11. Wen, Jing, Na Geng, and Xiaolan Xie. "Real-time scheduling of semi-urgent patients under waiting time targets." International Journal of Production Research (2019): 1-17.

  12. Wang, Z., et al. "Epidemic Propagation with Positive and Negative Preventive Information in Multiplex Networks." IEEE transactions on cybernetics (2020).

  13. Ganasegeran, Kurubaran, and Surajudeen Abiola Abdulrahman. "Artificial Intelligence Applications in Tracking Health Behaviors During Disease Epidemics." Human Behaviour Analysis Using Intelligent Systems. Springer, Cham, 2020. 141-155.