Optimal Control of a SEIR Model with Mixed Constraints and L1 Cost

  • Maria do Rosário de Pinho
  • Igor Kornienko
  • Helmut Maurer
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 321)

Abstract

Optimal control can help to determine vaccination policies for infectious diseases. For diseases transmitted horizontally, SEIR compartment models have been used. Most of the literature on SEIR models deals with cost functions that are quadratic with respect to the control variable, the rate of vaccination. Here, we propose the introduction of a cost of L 1 type which is linear with respect to the control variable. Our starting point is the recent work [1], where the number of vaccines at each time is assumed to be limited. This yields an optimal control problem with a mixed control-state constraint. We discuss the necessary optimality conditions of the Maximum Principle and present numerical solutions that precisely satisfy the necessary conditions.

Keywords

optimal control epidemiology mixed constraints numerical solutions bang-bang control 

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References

  1. 1.
    Biswas, M.H.A., Paiva, L.T., de Pinho, M.D.R.: A SEIR model for control of infectious diseases with constraints. Mathematical Biosciences and Engineering (to appear, 2014)Google Scholar
  2. 2.
    Brauer, F., Castillo-Chavez, C.: Mathematical Models in Population Biology and Epidemiology. Springer, New York (2001)CrossRefMATHGoogle Scholar
  3. 3.
    Clarke, F.: Optimization and Nonsmooth Analysis. John Wiley, New York (1983)MATHGoogle Scholar
  4. 4.
    Clarke, F.: Functional Analysis, Calculus of Variations and Optimal Control. Springer, London (2013)CrossRefMATHGoogle Scholar
  5. 5.
    Clarke, F., de Pinho, M.D.R.: Optimal control problems with mixed constraints. SIAM J. Control Optim. 48, 4500–4524 (2010)CrossRefMATHMathSciNetGoogle Scholar
  6. 6.
    Falugi, P., Kerrigan, E., van Wyk, E.: Imperial College London Optimal Control Software User Guide (ICLOCS). Department of Electrical and Electronic Engineering. Imperial College London, London (2010)Google Scholar
  7. 7.
    Fourer, R., Gay, D.M., Kernighan, B.W.: AMPL: A Modeling Language for Mathematical Programming. Duxbury Press, Brooks–Cole Publishing Company (1993)Google Scholar
  8. 8.
    Hestenes, M.R.: Calculus of Variations and Optimal Control Theory, 2nd edn., 405 pages. John Wiley, New York (1980)Google Scholar
  9. 9.
    Hethcote, H.W.: The basic epidemiology models: models, expressions for R 0, parameter estimation, and applications. In: Ma, S., Xia, Y. (eds.) Mathematical Understanding of Infectious Disease Dynamics, vol. 16, ch. 1, pp. 1–61. World Scientific Publishing Co. Pte. Ltd., Singapore (2008)Google Scholar
  10. 10.
    Maurer, H., Osmolovskii, N.P.: Second-order conditions for optimal control problems with mixed control-state constraints and control appearing linearly. In: Proceedings of the 52nd IEEE Conference on Control and Design (CDC 2013), Firenze, pp. 514–519 (2013)Google Scholar
  11. 11.
    Maurer, H., Pickenhain, S.: Second order sufficient conditions for optimal control problems with mixed control-state constraints. J. Optimization Theory and Applications 86, 649–667 (1995)CrossRefMATHMathSciNetGoogle Scholar
  12. 12.
    Osmolovskii, N.P., Maurer, H.: Applications to Regular and Bang-Bang Control: Second-Order Necessary and Sufficient Optimality Conditions in Calculus of Variations and Optimal Control. SIAM Advances in Design and Control 24 (2013)Google Scholar
  13. 13.
    Neilan, R.M., Lenhart, S.: An Introduction to Optimal Control with an Application in Disease Modeling. DIMACS Series in Discrete Mathematics 75, 67–81 (2010)Google Scholar
  14. 14.
    Paiva, L.T.: Optimal Control in Constrained and Hybrid Nonlinear Systems. Project Report (2013), http://paginas.fe.up.pt/~faf/ProjectFCT2009/report.pdf
  15. 15.
    Schaettler, H., Ledzewicz, U.: Geometric Optimal Control. Theory, Methods and Examples. Springer, New York (2012)CrossRefMATHGoogle Scholar
  16. 16.
    Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106, 25–57 (2006)CrossRefMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Maria do Rosário de Pinho
    • 1
  • Igor Kornienko
    • 1
  • Helmut Maurer
    • 2
  1. 1.Faculdade de Engenharia, ISRUniversity of PortoPortoPortugal
  2. 2.Institut für Numerische und Angewandte MathematikUniversität MünsterMünsterGermany

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