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Using a Stochastic SIR Model to Design Optimal Vaccination Campaigns via Multiobjective Optimization

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Trends in Biomathematics: Modeling Cells, Flows, Epidemics, and the Environment (BIOMAT 2019)

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Abstract

The design of optimal vaccination campaigns using mathematical and computational models has given concrete suggestions to politicians and other ones responsible on how it should be implemented in a better way, given a certain allowed level of infected persons within the whole population. In this paper, a multiobjective impulsive control scheme in an open-loop continuous-variable dynamic optimization procedure is proposed to cope with this problem, having the NSGA-II (Non-dominated Sorting Genetic Algorithm) as an optimization machinery and the SIR (Susceptible–Infectious–Recovered) model describing the behavior of a disease in a population, extended to analyze the effects of impulsive vaccination on the population. Furthermore, a stochastic SIR model is adapted in order to calculate the probability of eradication to each non-dominated vaccination policy came from the NSGA-II, as decision criteria. The target of the analysis is to give concrete suggestions to politicians or decision-makers how optimal vaccination campaigns should be implemented, given a certain probability of eradication or an allowed level of infected persons within the whole population.

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References

  1. R.M. Anderson, R.M. May, Infectious Diseases of Humans: Dynamics and Control (Oxford University Press, Oxford, 1992)

    Google Scholar 

  2. N.T. Bailey, The Mathematical Theory of Infectious Diseases and its Applications, 2nd edn. (Heffner Press/MacMillan, New York, 1975)

    MATH  Google Scholar 

  3. T. Britton, Math. Biosci. 225, 24–35 (2010)

    Article  MathSciNet  Google Scholar 

  4. R.T.N. Cardoso, R.H.C. Takahashi, C.M. Fonseca, IFAC Proc. Vol. 42, 283–288 (2009)

    Article  Google Scholar 

  5. M.C. Chubb, K.H. Jacobsen, Eur. J. Epidemiol. 25, 13–19 (2010)

    Article  Google Scholar 

  6. D. Clancy, J. Math. Biol. 39, 309–351 (1999)

    Google Scholar 

  7. A.R. da Cruz, R.T.N. Cardoso, R.H.C. Takahashi, IFAC Proce. Vol. 42, 289–294 (2009)

    Article  Google Scholar 

  8. A.R. da Cruz, R.T.N. Cardoso, R.H.C. Takahashi, Lect. Notes Comput. Sci. 6576, 404–417 (2011)

    Article  Google Scholar 

  9. A.R. da Cruz, R.T.N. Cardoso, R.H.C. Takahashi, Appl. Soft Comput. J. 50, 34–47 (2017)

    Article  Google Scholar 

  10. K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, IEEE Trans. Evol. Comput. 6, 182–197 (2002)

    Article  Google Scholar 

  11. A. d’Onofrio, Appl. Math. Lett. 18, 729–732 (2005)

    Article  MathSciNet  Google Scholar 

  12. H.W. Hethcote, SIAM Rev. 42, 599–653 (2000)

    Article  MathSciNet  Google Scholar 

  13. D.J. Higham, SIAM Rev. 43, 525–546 (2001)

    Article  MathSciNet  Google Scholar 

  14. W. Kermack, A. McKendrick, Proc. R. Soc. Lond. A Math. Phys. Sci. 115, 700–721 (1927)

    Google Scholar 

  15. R. Kuske, L.F. Gordillo, P. Greenwood, J. Theor. Biol. 245, 459–469 (2007)

    Google Scholar 

  16. E.G. Nepomuceno, R.H.C. Takahashi, L.A. Aguirre, Revista Brasileira de Biometria 34, 133–162 (2016)

    Google Scholar 

  17. R. Regoes, Stochastic simulation of epidemics. Theoretical Biology (2006). https://tb.ethz.ch

  18. B. Shulgin, L. Stone, Z. Agur, Bull. Math. Biol. 60, 1123–1148 (1998)

    Article  Google Scholar 

  19. E. Tornatore, S.M. Buccellato, P. Vetro, Rendiconti del Circolo Matematico de Palermo 55(2), 223–240 (2006)

    Article  Google Scholar 

  20. Y. Yang, Y. Xiao, Math. Comput. Model. 52, 1591–1604 (2010)

    Article  Google Scholar 

Download references

Acknowledgements

R. T. N. Cardoso thanks the International Union of Biological Sciences (IUBS) for partial support of living expenses in Szeged, during the 19th BIOMAT International Symposium, October 20–26, 2019.

The authors would thank to the Brazilian Agencies CAPES, CNPq, FAPEMIG, and to CEFET-MG for financial support.

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Dusse, A.C.S., Cardoso, R.T.N. (2020). Using a Stochastic SIR Model to Design Optimal Vaccination Campaigns via Multiobjective Optimization. In: Mondaini, R.P. (eds) Trends in Biomathematics: Modeling Cells, Flows, Epidemics, and the Environment. BIOMAT 2019. Springer, Cham. https://doi.org/10.1007/978-3-030-46306-9_16

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