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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
R.M. Anderson, R.M. May, Infectious Diseases of Humans: Dynamics and Control (Oxford University Press, Oxford, 1992)
N.T. Bailey, The Mathematical Theory of Infectious Diseases and its Applications, 2nd edn. (Heffner Press/MacMillan, New York, 1975)
T. Britton, Math. Biosci. 225, 24–35 (2010)
R.T.N. Cardoso, R.H.C. Takahashi, C.M. Fonseca, IFAC Proc. Vol. 42, 283–288 (2009)
M.C. Chubb, K.H. Jacobsen, Eur. J. Epidemiol. 25, 13–19 (2010)
D. Clancy, J. Math. Biol. 39, 309–351 (1999)
A.R. da Cruz, R.T.N. Cardoso, R.H.C. Takahashi, IFAC Proce. Vol. 42, 289–294 (2009)
A.R. da Cruz, R.T.N. Cardoso, R.H.C. Takahashi, Lect. Notes Comput. Sci. 6576, 404–417 (2011)
A.R. da Cruz, R.T.N. Cardoso, R.H.C. Takahashi, Appl. Soft Comput. J. 50, 34–47 (2017)
K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, IEEE Trans. Evol. Comput. 6, 182–197 (2002)
A. d’Onofrio, Appl. Math. Lett. 18, 729–732 (2005)
H.W. Hethcote, SIAM Rev. 42, 599–653 (2000)
D.J. Higham, SIAM Rev. 43, 525–546 (2001)
W. Kermack, A. McKendrick, Proc. R. Soc. Lond. A Math. Phys. Sci. 115, 700–721 (1927)
R. Kuske, L.F. Gordillo, P. Greenwood, J. Theor. Biol. 245, 459–469 (2007)
E.G. Nepomuceno, R.H.C. Takahashi, L.A. Aguirre, Revista Brasileira de Biometria 34, 133–162 (2016)
R. Regoes, Stochastic simulation of epidemics. Theoretical Biology (2006). https://tb.ethz.ch
B. Shulgin, L. Stone, Z. Agur, Bull. Math. Biol. 60, 1123–1148 (1998)
E. Tornatore, S.M. Buccellato, P. Vetro, Rendiconti del Circolo Matematico de Palermo 55(2), 223–240 (2006)
Y. Yang, Y. Xiao, Math. Comput. Model. 52, 1591–1604 (2010)
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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-46306-9_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-46305-2
Online ISBN: 978-3-030-46306-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)