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Epidemic Logistics with Demand Information Updating Model I: Medical Resource Is Enough

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Abstract

In this chapter, we present a discrete time-space network model for allocating medical resource following an epidemic outbreak. It couples a forecasting mechanism for dynamic demand of medical resource based on an epidemic diffusion model and a multi-stage programming model for optimal allocation and transport of such resource. In this chapter, we present a discrete time-space network model for allocating medical resource following an epidemic outbreak. It couples a forecasting mechanism for dynamic demand of medical resource based on an epidemic diffusion model and a multi-stage programming model for optimal allocation and transport of such resource. At each stage, the linear programming solves for a cost minimizing resource allocation solution subject to a time-varying demand that is forecasted by a recursion model. The rationale that the medical resource allocated in early periods will take effect in subduing the spread of epidemic and thus impact the demand in later periods has been incorporated in such recursion model. We compare the proposed medical resource allocation mode with other operation modes in practice, and find that our model is superior to any of them in less waste of resource and less logistic cost. The results may provide some practical guidelines for a decision-maker who is in charge of medical resource allocation in an epidemics control effort.

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References

  1. Hu JX, Zeng AZ, Zhao LD. A comparative study of public-health emergency management. Ind Manag Data Syst. 2009;109(7):976–92.

    Article  Google Scholar 

  2. Zhang J, Ma ZE. Global dynamics of an SEIR epidemic model with saturating contact rate. Math Biosci. 2003;185(1):15–32.

    Article  Google Scholar 

  3. Mishra B, Saini D. SEIRS epidemic model with delay for transmission of malicious objects in computer network. Appl Math Comput. 2007;188(2):1476–82.

    Google Scholar 

  4. Sun C, Hsieh YH. Global analysis of an SEIR model with varying population size and vaccination. Appl Math Model. 2010;34(10):2685–97.

    Article  Google Scholar 

  5. Zhang J, Li J, Ma Z. Global dynamics of an SEIR epidemic model with immigration of different compartments. Acta Math Scientia. 2006;26(3):551–67.

    Article  Google Scholar 

  6. Saramäki J, Kaski K. Modeling development of epidemics with dynamic small-world networks. J Theor Biol. 2005;234(3):413–21.

    Article  Google Scholar 

  7. Xu XJ, Peng HO, Wang XM, et al. Epidemic spreading with time delay in complex networks. Phys A Stat Mech Appl. 2005;367(C):525–30.

    Google Scholar 

  8. Han XP. Disease spreading with epidemic alert on small-world networks. Phys Lett A. 2007;365(1–2):1–5.

    Article  Google Scholar 

  9. Jung E, Iwami S, Takeuchi Y, et al. Optimal control strategy for prevention of avian influenza pandemic. J Theor Biol. 2009;260(2):220–9.

    Article  Google Scholar 

  10. Wang JX, Xu WS, Zhang RQ, Wu N. Epidemic prevention and infection control of a field dressing station in Wenchuan earthquake areas. Int J Infect Dis. 2009;13(1):100.

    Article  Google Scholar 

  11. Halloran ME, Ferguson NM, Eubank S, et al. Modeling targeted layered containment of an influenza pandemic in the United States. Proc Natl Acad Sci. 2008;105(12):4639–44.

    Article  Google Scholar 

  12. Kim KI, Lin Z, Zhang L. Avian-human influenza epidemic model with diffusion. Nonlinear Anal Real World Appl. 2010;11(1):313–22.

    Article  Google Scholar 

  13. Liu J, Zhang T. Epidemic spreading of an SEIRS model in scale-free networks. Commun Nonlinear Sci Numer Simul. 2011;16(8):3375–84.

    Article  Google Scholar 

  14. Samsuzzoha M, Singh M, Lucy D. Numerical study of an influenza epidemic model with diffusion. Appl Math Comput. 2010;217(7):3461–79.

    Google Scholar 

  15. Samsuzzoha M, Singh M, Lucy D. A numerical study on an influenza epidemic model with vaccination and diffusion. Appl Math Comput. 2012;219:122–41.

    Google Scholar 

  16. Yin S, Yang X B, Karimi H R. Data-driven adaptive observer for fault diagnosis. Math Probl Eng 2012; Article ID 832836.

    Google Scholar 

  17. Yin S, Wang G, Karimi HR. Data-driven design of robust fault detection system for wind turbines. Mechatronics. 2014;24(4):298–306.

    Article  Google Scholar 

  18. Yin S, Li X, Gao H, et al. Data-based techniques focused on modern industry: An overview. IEEE Trans Industr Electron. 2015;62(1):657–67.

    Article  Google Scholar 

  19. Yin S, Wang G, Yang X. Robust PLS approach for KPI-related prediction and diagnosis against outliers and missing data. Int J Syst Sci. 2014;45(7):1375–82.

    Article  Google Scholar 

  20. Zaric GS, Brandeau ML. Resource allocation for epidemic control over short time horizons. Math Biosci. 2001;171(1):33–58.

    Article  Google Scholar 

  21. Zaric GS, Brandeau ML. Dynamic resource allocation for epidemic control in multiple populations. IMA J Math Appl Med Biol. 2002;19(4):235–55.

    Article  Google Scholar 

  22. Brandeau ML, Zaric GS, Richter A. Resource allocation for control of infectious diseases in multiple independent populations: beyond cost-effectiveness analysis. J Health Econ. 2003;22(4):575–98.

    Google Scholar 

  23. Zaric GS, Bravata DM, Cleophas Holty JE, et al. Modeling the logistics of response to anthrax bioterrorism. Med Decis Making. 2008;28(3):332–50.

    Article  Google Scholar 

  24. Tebbens RJD, Pallansch MA, Alexander JP, et al. Optimal vaccine stockpile design for an eradicated disease: application to polio. Vaccine. 2010; 28(26):0–4327.

    Google Scholar 

  25. Liu M, Zhao LD. Optimization of the emergency materials distribution network with time windows in anti-bioterrorism system. Int J Innov Comput Inf Control. 2009;5(11A):3615–24.

    Google Scholar 

  26. Wang HY, Wang XP, Zeng AZ. Optimal material distribution decisions based on epidemic diffusion rule and stochastic latent period for emergency rescue. Int J Math Oper Res. 2009;1(1–2):76–96.

    Article  Google Scholar 

  27. Qiang P, Nagurney A. A bi-criteria indicator to assess supply chain network performance for critical needs under capacity and demand disruptions. Transp Res Part A (Policy Pract), 2012;46(5):801–12.

    Google Scholar 

  28. Rachaniotis NP, Dasaklis TK, Pappis CP. A deterministic resource scheduling model in epidemic control: a case study[J]. Eur J Oper Res. 2012;216(1):225–31.

    Article  Google Scholar 

  29. Barbarosoglu G, OZdamar L, Cevik A. An interactive approach for hierarchical analysis of helicopter logistics in disaster relief operations. Eur J Oper Res. 2002; 140(1):118–33.

    Google Scholar 

  30. Yan S, Shih YL. Optimal scheduling of emergency roadway repair and subsequent relief distribution. Comput Oper Res. 2009;36(6):2049–65.

    Article  Google Scholar 

  31. Liu M, Zhao LD. Analysis for epidemic diffusion and emergency demand in an anti-bioterrorism system. Int J Math Model Numer Optim. 2011;2(1):51–68.

    Google Scholar 

  32. Liu M, Zhao LD, Sebastian HJ. Mixed-collaborative distribution mode of the emergency resource in anti-bioterrorism system. Int J Math Oper Res. 2011;3(2):148–69.

    Article  Google Scholar 

  33. Tham KY (2004) An emergency department response to severe acute respiratory syndrome: a prototype response to bioterrorism. Ann Emerg Med 2004;43(1):6–14.

    Google Scholar 

  34. Sheu JB. An emergency logistics distribution approach for quick response to urgent relief demand in disasters. Transp Res Part E Logis Transp Rev. 2007;43(6):687–709.

    Article  Google Scholar 

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Correspondence to Ming Liu .

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Liu, M., Cao, J., Liang, J., Chen, M. (2020). Epidemic Logistics with Demand Information Updating Model I: Medical Resource Is Enough. In: Epidemic-logistics Modeling: A New Perspective on Operations Research. Springer, Singapore. https://doi.org/10.1007/978-981-13-9353-2_4

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