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
Topics of interest
- 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
References
- de Wit, Emmie, et al. "SARS and MERS: recent insights into emerging coronaviruses." Nature Reviews Microbiology 14.8 (2016): 523.
- Medina, Rafael 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.
- Feldman, B., Martin, E. M., & Skotnes, T. (2012). Big data in healthcare hype and hope. Dr. Bonnie, 360, 122–125.
- 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.
- 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.
- 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.
- Wirz, Christopher D., et al. "Rethinking social amplification of risk: Social media and Zika in three languages." Risk Analysis 38.12 (2018): 2599-2624.
- Cai, Guofa, et al. "QoS-Aware Buffer-Aided Relaying Implant WBAN for Healthcare IoT: Opportunities and Challenges." IEEE Network 33.4 (2019): 96-103.
- Chen, Wuhua, Zhe George Zhang, and Xiaohong Chen. "On two-tier healthcare system under capacity constraint." International Journal of Production Research (2019): 1-21..
- 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.
- Wang, Z., et al. "Epidemic Propagation with Positive and Negative Preventive Information in Multiplex Networks." IEEE transactions on cybernetics (2020).
- 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：疫情爆發(fā)下的醫(yī)療保健生產(chǎn)
特邀編輯
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
關(guān)鍵日期
稿件提交截止日期：2020年11月30日
最終決定截止日期：2021年5月31日
暫定出版日期：2021年10月31日
在此處提交：https://mc.forttlecentral.com/tprs
關(guān)于特刊
最近在中國爆發(fā)的冠狀病毒（2019 nCoV）使我們想起了非典、MERS、埃博拉等全國性流行病的恐怖（de Wit et al。2016年）。傳染病是自然災(zāi)害或人為災(zāi)害后死亡的主要原因。傳染病通過一組傳染媒介經(jīng)多種相互作用的方式迅速傳播，在很短的時間內(nèi)威脅許多人的健康（Medina 2018）。
新出現(xiàn)和重新出現(xiàn)的傳染病對全球醫(yī)療保健的威脅仍然十分嚴重，應(yīng)對這種威脅的大流行防備能力非常重要。對疫情的有效應(yīng)對將有助于穩(wěn)定經(jīng)濟活動和減少系統(tǒng)性風險。必要的醫(yī)療用品和訓(xùn)練有素的人員等現(xiàn)有資源需要以最佳方式迅速部署。為了在發(fā)生不可逆轉(zhuǎn)的后果之前控制流行病，必須結(jié)合政府和社會支持的財政資源，對信息渠道進行透明管理。因此，緊急醫(yī)療系統(tǒng)在遏制工作中的快速反應(yīng)至關(guān)重要。
鑒于Ginsberg等人的早期努力（2009），數(shù)據(jù)分析和人工智能（AI）已被證明在風險識別和評估方面具有巨大潛力：有效地預(yù)防、阻止和應(yīng)對傳染病流行的威脅；促進對流行病期間求醫(yī)行為和公眾情緒的理解。
當今世界的無縫邊界和全球互聯(lián)已經(jīng)造成了健康數(shù)據(jù)的爆炸式增長，從2012年的500 PB增加到2020年的25000 PB（Feldman、Martin和Skotnes 2012）。從系統(tǒng)思維的角度來看，人工智能為公共衛(wèi)生從業(yè)者和政策制定者提供了新的工具，通過有針對性的、針對具體情況的干預(yù)措施，擴大獲取健康信息和服務(wù)的機會（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）。
為《國際生產(chǎn)研究雜志》征集以“醫(yī)療保健生產(chǎn)”為主題的論文，旨在獲得學(xué)者們對醫(yī)療保健生產(chǎn)中的風險和分析的見解和觀點。鼓勵作者提交他們論文，以探討這一主要集中在醫(yī)療保健生產(chǎn)的特刊主題。
感興趣的主題
本特刊旨在探討以下但不限于在醫(yī)療風險建模及其應(yīng)用的潛在主題：
- 限制流行病傳播風險創(chuàng)新策略
- 通過先進的分析降低醫(yī)療風險
- 醫(yī)療保健中的排隊模型
- 爆發(fā)事件仿真
- 大數(shù)據(jù)驅(qū)動健康風險識別
- 基于人工智能的流行病網(wǎng)絡(luò)分析
- 估計冠狀病毒的全球經(jīng)濟成本風險
- 醫(yī)療風險管理領(lǐng)域的MCDM模型
- 如何管理未來疫情的風險（預(yù)防、控制和治療）
- 疫情爆發(fā)期間的應(yīng)對模型
- 物聯(lián)網(wǎng)在醫(yī)療領(lǐng)域的應(yīng)用
- 醫(yī)療風險分析中的跨學(xué)科方法和決策工具
- 基于云的社交媒體分析框架
- 資源配置應(yīng)急管理
- 應(yīng)對不確定性的人道主義物流
- 其他與醫(yī)療風險分析相關(guān)的主題
參考文獻
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.