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Parallel Simulation Decision-Making Method for a Response to Unconventional Public Health Emergencies Based on the Scenario–Response Paradigm and Discrete Event System Theory

Published online by Cambridge University Press:  18 July 2019

Tian Xie Open the ORCID record for Tian Xie [Opens in a new window] ,
Mengna Ni ,
Zhaoyun Zhang  and
Yaoyao Wei
Tian Xie
Affiliation:
Department of Management Science and Engineering, School of Economics, Management and Law, University of South China, Hengyang, China
Mengna Ni
Affiliation:
Department of Management Science and Engineering, School of Economics, Management and Law, University of South China, Hengyang, China
Zhaoyun Zhang
Affiliation:
Department of Management Science and Engineering, School of Economics, Management and Law, University of South China, Hengyang, China
Yaoyao Wei*
Affiliation:
Department of Management Science and Engineering, School of Economics, Management and Law, University of South China, Hengyang, China School of Marxism, University of South China, Hengyang, China
*
Correspondence and reprint requests to Yaoyao Wei, No. 28, West Changsheng Road, Hengyang City, Hunan Province, PR China 421001 (e-mail: 24359048@qq.com).
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Abstract

Given the non-repeatability, complexity, and unpredictability of unconventional public health emergencies, building accurate models and making effective response decisions based only on traditional prediction–response decision-making methods are difficult. To solve this problem, under the scenario–response paradigm and theories on parallel emergency management and discrete event system (DES), the parallel simulation decision-making framework (PSDF), which includes the methods of abstract modeling, simulation operation, decision-making optimization, and parallel control, is proposed for unconventional public health emergency response processes. Furthermore, with the example of the severe acute respiratory syndrome (SARS) response process, the evolutionary scenarios that include infected patients and diagnostic processes are transformed into simulation processes. Then, the validity and operability of the DES–PSDF method proposed in this paper are verified by the results of a simulation experiment. The results demonstrated that, in the case of insufficient prior knowledge, effective parallel simulation models can be constructed and improved dynamically by multi-stage parallel controlling. Public health system bottlenecks and relevant effective response solutions can also be obtained by iterative simulation and optimizing decisions. To meet the urgent requirements of emergency response, the DES–PSDF method introduces a new response decision-making concept for unconventional public health emergencies.

Keywords

discrete event systemparallel emergency managementsimulation decisionunconventional public health emergency
Type
Concepts in Disaster Medicine
Information
Disaster Medicine and Public Health Preparedness , Volume 13 , Issue 5-6 , December 2019 , pp. 1017 - 1027
Copyright
Copyright © 2019 Society for Disaster Medicine and Public Health, Inc. 

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References

REFERENCES

Zhong, YG, Mao, ZG, Weng, WG, et al. Progress of “study on unconventional emergencies management”. Syst Eng Theory Prac. 2012;32(5):911–918.Google Scholar
Yang, Q, Yang, F. Multi-agents simulation on unconventional emergencies evolution mechanism in public health. In: Jin, D, Lin, S, eds. Advances in Multimedia, Software Engineering and Computing. Berlin Heidelberg, Germany: Springer; 2011:509–514.CrossRefGoogle Scholar
Bostick, N, Subbarao, I, Burkle, F, et al. Disaster triage systems for large-scale catastrophic events. Disaster Med Public Health Prep. 2008;2(S1):S35–S39.CrossRefGoogle ScholarPubMed
Wang, FY, Qiu, XG, Zeng, DJ, et al. A computational experimental platform for emergency response based on parallel systems. Complex Systems and Complexity Science. 2010;7(4):1–10.Google Scholar
McCarthy, ML, Aronsky, D, Kelen, GD. The measurement of daily surge and its relevance to disaster preparedness. Acad Emerg Med. 2006;13(11):1138–1141.CrossRefGoogle ScholarPubMed
McCarthy, ML, Zeger, SL, Ding, R, et al. The challenge of predicting demand for emergency department services. Acad Emerg Med. 2008;15(4):337–346.CrossRefGoogle ScholarPubMed
Bernstein, SL, Aronsky, D, Duseja, R, et al. The effect of emergency department crowding on clinically oriented outcomes. Acad Emerg Med. 2009;16(1):1–10.CrossRefGoogle ScholarPubMed
Forster, AJ, Stiell, I, Wells, G, et al. The effect of hospital occupancy on emergency department length of stay and patient disposition. Acad Emerg Med. 2003;10(2):127–133.CrossRefGoogle ScholarPubMed
Zhong, MH, Shi, CL, Fu, TR. Study in performance analysis of China Urban Emergency Response System based on Petri Net. Safety Sci. 2010;48(6):755–762.CrossRefGoogle Scholar
Zhou, JF. Petri Net modeling for the emergency response to chemical accidents. J Loss Prev Process Ind. 2013;26(4):766–770.CrossRefGoogle Scholar
Helbing, D, Farkas, I, Vicsek, T. Simulating dynamical features of escape panic. Nature. 2000;407(9):487–490.CrossRefGoogle ScholarPubMed
Valiani, A, Caleffi, V, Zanni, A. Case study: Malpasset dam-break simulation using a two-dimensional finite volume method. J Hydraul Eng. 2002;128(5):460–472.CrossRefGoogle Scholar
Ni, Z, Rong, L, Wang, N, et al. Knowledge model for emergency response based on contingency planning system of China. IJIM. 2019;46:10–22.Google Scholar
Bo-Hu, LI, Chai, XD, Hou, BC, et al. Networked modeling & simulation platform based on concept of cloud computing– cloud simulation platform. J Syst Simul. 2009;21(17):5292–5299.Google Scholar
Wang, FY. Parallel control and management for intelligent transportation systems: concepts, architectures, and applications. IEEE Trans Intell Transp Syst. 2010;11(3):630–638.CrossRefGoogle Scholar
Kolker, A. Queuing theory and discrete events simulation for health care: from basic processes to complex systems with interdependencies. In: Rodrigues, J, ed. Health Information Systems: Concepts, Methodologies, Tools, and Applications. Hershey, PA: IGI-press Global; 2009:331–343.Google Scholar
Jacobson, SH, Hall, SN, Swisher, JR. Discrete-event simulation of health care systems. Patient Flow: Reducing Delay in Healthcare Delivery. New York: Springer; 2006:211–252.CrossRefGoogle Scholar
Budgaga, W, Malensek, M, Pallickara, S, et al. Predictive analytics using statistical, learning, and ensemble methods to support real-time exploration of discrete event simulations. Future Gener Comput Syst. 2016;56:360–374.CrossRefGoogle Scholar
Furian, N, O’sullivan, M, Walker, C, et al. A conceptual modeling framework for discrete event simulation using hierarchical control structures. Simul Model Pract Theory. 2015;56:82–96.CrossRefGoogle ScholarPubMed
Ni, Z, Wang, Y, Yin, Z. Relative risk model for assessing domino effect in chemical process industry. Safety Sci. 2016;87:156–166.CrossRefGoogle Scholar
Yingying, YU. Research on emergency resource demand based on emergency management process. Ind Safety Environ Protect. 2014;9:47–50.Google Scholar
Lavery, E, Beaverstock, M, Greenwood, A. Applied simulation: modeling and analysis using FlexSim. Orem, UT: FlexSim Software Products Inc.; 2011.Google Scholar
Kelton, DW. Simulation with Arena. New York: McGraw-Hill Education; 2014.Google Scholar
Pokraev, SV. Model-Driven Semantic Integration of Service-Oriented Applications. Enschede: University of Twente; 2009.Google Scholar
Fang, J, Qu, T, Li, Z, et al. Huang. Agent-based Gateway Operating System for RFID-enabled ubiquitous manufacturing enterprise. Robot Comput Integr Manuf. 2013;29(4):222–231.CrossRefGoogle Scholar
Kumar, S, McCreary, ML, Nottestad, DA. Quantifying supply chain trade-offs using six sigma, simulation, and designed experiments to develop a flexible distribution network. Qual Eng. 2011;23(2):180–203.CrossRefGoogle Scholar
Xie, T, Li, CD, Wei, YY, et al. Cross-domain integrating and reasoning spaces for offsite nuclear emergency response. Safety Sci. 2016;85:99–116.CrossRefGoogle Scholar
Viswanathan, K, Bass, R, Wijetunge, G, et al. Rural mass casualty preparedness and response: The Institute of Medicine’s Forum on Medical and Public Health Preparedness for Catastrophic Events. Disaster Med Public Health Prep. 2012;6(3):297–302.CrossRefGoogle ScholarPubMed
Fiedrich, F, Gehbauer, F, Rickers, U. Optimized resource allocation for emergency response after earthquake disasters. Safety Sci. 2000;35(1):41–57.CrossRefGoogle Scholar
Wang, H, Kang, Z, Zhou, N, et al. A model checker for WS-CDL. J Syst Softw. 2010;83(10):1651–1661.CrossRefGoogle Scholar
Wang, XH, Wong, TN, Wang, G. Service-oriented architecture for ontologies supporting multi-agent system negotiations in virtual enterprise. J Intell Manuf. 2012;23(4):1331–1349.CrossRefGoogle Scholar
Zeng, L. A study about the simulation of emergency response on public incidents in metropolis– with the example of SARS patients response flow. Dissertation. Shanghai: Tongji University; 2007.Google Scholar
Han, S. Emergency medical service system simulation based on emergency scenes. Dissertation. Shanghai: Shanghai Jiao Tong University; 2014.Google Scholar