Special Issue: Production of Healthcare under Epidemic Outbreaks

Guest editors

• Professor Desheng Wu, University of Chinese Academy of Sciences and Stockholm University, China/Sweden, Email dash@risklab.ac, dash.wu@gmail.com  (Managing guest editor)
• Professor Alexandre Dolgui, Head of Automation, Production and Computer Sciences Dept., IMT Atlantique, Nantes, France,  Email: alexandre.dolgui@imt-atlantique.fr
• Professor David L. Olson, University of Nebraska, USA, E-mail: dolson3@unl.edu
• Professor Xiaolan Xie, Heads of the Research department on Healthcare Engineering at Mines Saint-Etienne, France, Email: xie@emse.fr

Key dates

Deadline of Manuscript Submission: 30 November 2020

Final Decision Due: 31 May 2021

Tentative Publication Date: 31 October 2021

1.  https://mc.manuscriptcentral.com/tprs

Topics of interest

1. Innovative strategies to limit risk of epidemic disease propagation
2. Risk mitigation in healthcare with advanced analytics
3. Queuing modelling in healthcare
4. Simulation of outbreak events
5. Big data-driven health risk identification
6. AI-based epidemic network analysis
7. Estimating the risk of global economic costs of Coronavirus
8. MCDM models in field of healthcare risk management
9. How to manage risk of future outbreaks (prevention, control and treatment)
10. Response models during epidemic outbreaks
11. IoT application in healthcare
12. Interdisciplinary approaches and decision-making tools in healthcare risk analysis
13. Cloud-based framework for social media analysis
14. Emergency management of resource allocation
15. Humanitarian logistics dealing with uncertainties
16. Other topics related to healthcare risk analytics

References

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.

特刊：疫情爆发下的医疗保健生产

特邀编辑

Professor Desheng Wu, University of Chinese Academy of Sciences and Stockholm University, China/Sweden, Email dash@risklab.ac, dash.wu@gmail.com (Managing guest editor)

Professor Alexandre Dolgui, Head of Automation, Production and Computer Sciences Dept., IMT Atlantique, Nantes, France, Email: alexandre.dolgui@imt-atlantique.fr

Professor David L. Olson, University of Nebraska, USA, E-mail: dolson3@unl.edu

Professor Xiaolan Xie, Heads of the Research department on Healthcare Engineering at Mines Saint-Etienne, France, Email: xie@emse.fr

关键日期

稿件提交截止日期：2020年11月30日

最终决定截止日期：2021年5月31日

暂定出版日期：2021年10月31日

在此处提交：https://mc.forttlecentral.com/tprs

关于特刊

最近在中国爆发的冠状病毒（2019 nCoV）使我们想起了非典、MERS、埃博拉等全国性流行病的恐怖（de Wit et al。2016年）。传染病是自然灾害或人为灾害后死亡的主要原因。传染病通过一组传染媒介经多种相互作用的方式迅速传播，在很短的时间内威胁许多人的健康（Medina 2018）。

新出现和重新出现的传染病对全球医疗保健的威胁仍然十分严重，应对这种威胁的大流行防备能力非常重要。对疫情的有效应对将有助于稳定经济活动和减少系统性风险。必要的医疗用品和训练有素的人员等现有资源需要以最佳方式迅速部署。为了在发生不可逆转的后果之前控制流行病，必须结合政府和社会支持的财政资源，对信息渠道进行透明管理。因此，紧急医疗系统在遏制工作中的快速反应至关重要。

鉴于Ginsberg等人的早期努力（2009），数据分析和人工智能（AI）已被证明在风险识别和评估方面具有巨大潜力：有效地预防、阻止和应对传染病流行的威胁；促进对流行病期间求医行为和公众情绪的理解。

当今世界的无缝边界和全球互联已经造成了健康数据的爆炸式增长，从2012年的500 PB增加到2020年的25000 PB（Feldman、Martin和Skotnes 2012）。从系统思维的角度来看，人工智能为公共卫生从业者和政策制定者提供了新的工具，通过有针对性的、针对具体情况的干预措施，扩大获取健康信息和服务的机会（Kao等 2014，Li等 2017，Anparasan和Miguel 2018，Wirz等 2018，Cai等 2019，Chen等 2019，Wen等 2019，Wang等 2020，Ganasegeran和Surajuden，2020）。

为《国际生产研究杂志》征集以“医疗保健生产”为主题的论文，旨在获得学者们对医疗保健生产中的风险和分析的见解和观点。鼓励作者提交他们论文，以探讨这一主要集中在医疗保健生产的特刊主题。

感兴趣的主题

本特刊旨在探讨以下但不限于在医疗风险建模及其应用的潜在主题：

• 限制流行病传播风险创新策略
• 通过先进的分析降低医疗风险
• 医疗保健中的排队模型
• 爆发事件仿真
• 大数据驱动健康风险识别
• 基于人工智能的流行病网络分析
• 估计冠状病毒的全球经济成本风险
• 医疗风险管理领域的MCDM模型
• 如何管理未来疫情的风险（预防、控制和治疗）
• 疫情爆发期间的应对模型
• 物联网在医疗领域的应用
• 医疗风险分析中的跨学科方法和决策工具
• 基于云的社交媒体分析框架
• 资源配置应急管理
• 应对不确定性的人道主义物流
• 其他与医疗风险分析相关的主题

参考文献

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.