IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v172y2023ics0960077923004617.html
   My bibliography  Save this article

A statistical moment-based spectral approach to the chance-constrained stochastic optimal control of epidemic models

Author

Listed:
  • Olivares, Alberto
  • Staffetti, Ernesto

Abstract

This paper presents a spectral approach to the uncertainty management in epidemic models through the formulation of chance-constrained stochastic optimal control problems. Specifically, a statistical moment-based polynomial expansion is used to calculate surrogate models of the stochastic state variables of the problem that allow for the efficient computation of their main statistics as well as their marginal and joint probability density functions at each time instant, which enable the uncertainty management in the epidemic model. Moreover, the surrogate models are employed to perform the corresponding sensitivity and risk analyses. The proposed methodology provides the designers of the optimal control policies with the capability to increase the predictability of the outcomes by adding suitable chance constraints to the epidemic model and formulating a proper cost functional. The chance-constrained optimal control of a COVID-19 epidemic model is considered in order to illustrate the practical application of the proposed methodology.

Suggested Citation

  • Olivares, Alberto & Staffetti, Ernesto, 2023. "A statistical moment-based spectral approach to the chance-constrained stochastic optimal control of epidemic models," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    • Handle: RePEc:eee:chsofr:v:172:y:2023:i:c:s0960077923004617
    DOI: 10.1016/j.chaos.2023.113560
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077923004617
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2023.113560?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Alberto Olivares & Ernesto Staffetti, 2021. "Optimal Control Applied to Vaccination and Testing Policies for COVID-19," Mathematics, MDPI, vol. 9(23), pages 1-22, December.
    2. Judd, Kenneth L. & Maliar, Lilia & Maliar, Serguei & Valero, Rafael, 2014. "Smolyak method for solving dynamic economic models: Lagrange interpolation, anisotropic grid and adaptive domain," Journal of Economic Dynamics and Control, Elsevier, vol. 44(C), pages 92-123.
    3. Ali, Hegagi Mohamed & Ameen, Ismail Gad, 2021. "Optimal control strategies of a fractional order model for Zika virus infection involving various transmissions," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    4. G. C. Calafiore & L. El Ghaoui, 2006. "On Distributionally Robust Chance-Constrained Linear Programs," Journal of Optimization Theory and Applications, Springer, vol. 130(1), pages 1-22, July.
    5. Brugnago, Eduardo L. & da Silva, Rafael M. & Manchein, Cesar & Beims, Marcus W., 2020. "How relevant is the decision of containment measures against COVID-19 applied ahead of time?," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    6. Peijiang Liu & Abdullahi Yusuf & Ting Cui & Anwarud Din, 2022. "Stochastic Optimal Control Analysis For The Covid-19 Epidemic Model Under Real Statistics," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 30(08), pages 1-24, December.
    7. Ruoyu Wang & Qihua Wang, 2022. "Determination and estimation of optimal quarantine duration for infectious diseases with application to data analysis of COVID‐19," Biometrics, The International Biometric Society, vol. 78(2), pages 691-700, June.
    8. Lv, Wei & He, Hanfei & Li, Kezan, 2022. "Robust optimal control of a network-based SIVS epidemic model with time delay," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    9. Olivares, Alberto & Staffetti, Ernesto, 2021. "Uncertainty quantification of a mathematical model of COVID-19 transmission dynamics with mass vaccination strategy," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    10. Saha, Sangeeta & Samanta, Guruprasad, 2022. "Analysis of a host–vector dynamics of a dengue disease model with optimal vector control strategy," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 195(C), pages 31-55.
    11. Thul, Lawrence & Powell, Warren, 2023. "Stochastic optimization for vaccine and testing kit allocation for the COVID-19 pandemic," European Journal of Operational Research, Elsevier, vol. 304(1), pages 325-338.
    12. Oladyshkin, S. & Nowak, W., 2012. "Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 179-190.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. James, Nick & Menzies, Max, 2023. "Collective infectivity of the pandemic over time and association with vaccine coverage and economic development," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Olivares, Alberto & Staffetti, Ernesto, 2021. "Uncertainty quantification of a mathematical model of COVID-19 transmission dynamics with mass vaccination strategy," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    2. Wenqing Chen & Melvyn Sim & Jie Sun & Chung-Piaw Teo, 2010. "From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization," Operations Research, INFORMS, vol. 58(2), pages 470-485, April.
    3. S. Bogan Aruoba & Pablo Cuba-Borda & Kenji Higa-Flores & Frank Schorfheide & Sergio Villalvazo, 2021. "Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 41, pages 96-120, July.
    4. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    5. Howard Kung & Gonzalo Morales & Alexandre Corhay, 2017. "Fiscal Discount Rates and Debt Maturity," 2017 Meeting Papers 840, Society for Economic Dynamics.
    6. Calafiore, Giuseppe Carlo & Parino, Francesco & Zino, Lorenzo & Rizzo, Alessandro, 2023. "Dynamic planning of a two-dose vaccination campaign with uncertain supplies," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1269-1278.
    7. Erdoğan, Güneş & Yücel, Eda & Kiavash, Parinaz & Salman, F. Sibel, 2024. "Fair and effective vaccine allocation during a pandemic," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).
    8. Aldrich Eric Mark & Kung Howard, 2021. "Computational Methods for Production-Based Asset Pricing Models with Recursive Utility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(1), pages 1-26, February.
    9. Lilia Maliar & Serguei Maliar & John B. Taylor & Inna Tsener, 2020. "A tractable framework for analyzing a class of nonstationary Markov models," Quantitative Economics, Econometric Society, vol. 11(4), pages 1289-1323, November.
    10. Victor Duarte & Diogo Duarte & Dejanir H. Silva, 2024. "Machine Learning for Continuous-Time Finance," CESifo Working Paper Series 10909, CESifo.
    11. Kristensen, Dennis & Mogensen, Patrick K. & Moon, Jong Myun & Schjerning, Bertel, 2021. "Solving dynamic discrete choice models using smoothing and sieve methods," Journal of Econometrics, Elsevier, vol. 223(2), pages 328-360.
    12. Krishnamurthy, Arvind & Li, Wenhao, 2020. "Dissecting Mechanisms of Financial Crises: Intermediation and Sentiment," Research Papers 3874, Stanford University, Graduate School of Business.
    13. Shang, Xiaobing & Su, Li & Fang, Hai & Zeng, Bowen & Zhang, Zhi, 2023. "An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    14. Rongju Zhang & Nicolas Langren'e & Yu Tian & Zili Zhu & Fima Klebaner & Kais Hamza, 2018. "Local Control Regression: Improving the Least Squares Monte Carlo Method for Portfolio Optimization," Papers 1803.11467, arXiv.org, revised Sep 2018.
    15. Bilsel, R. Ufuk & Ravindran, A., 2011. "A multiobjective chance constrained programming model for supplier selection under uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 45(8), pages 1284-1300, September.
    16. Jesús Fernández‐Villaverde & Oren Levintal, 2018. "Solution methods for models with rare disasters," Quantitative Economics, Econometric Society, vol. 9(2), pages 903-944, July.
    17. Rashed Khanjani-Shiraz & Ali Babapour-Azar & Zohreh Hosseini-Noudeh & Panos M. Pardalos, 2022. "Distributionally robust maximum probability shortest path problem," Journal of Combinatorial Optimization, Springer, vol. 43(1), pages 140-167, January.
    18. Gary S. Anderson, 2018. "Reliably Computing Nonlinear Dynamic Stochastic Model Solutions: An Algorithm with Error Formulas," Finance and Economics Discussion Series 2018-070, Board of Governors of the Federal Reserve System (U.S.).
    19. L. Jeff Hong & Zhiyuan Huang & Henry Lam, 2021. "Learning-Based Robust Optimization: Procedures and Statistical Guarantees," Management Science, INFORMS, vol. 67(6), pages 3447-3467, June.
    20. Xufeng Tang & Chang Liu & Xinqi Li & Ying Ji, 2023. "Distributionally Robust Programming of Berth-Allocation-with-Crane-Allocation Problem with Uncertain Quay-Crane-Handling Efficiency," Sustainability, MDPI, vol. 15(18), pages 1-27, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:172:y:2023:i:c:s0960077923004617. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.