Multiobjective approach to optimal control for a tuberculosis model

Mathematical modelling can help to explain the nature and dynamics of infection transmissions, as well as support a policy for implementing those strategies that are most likely to bring public health and economic benefits. The paper addresses the application of optimal control strategies in a tuberculosis model. The model consists of a system of ordinary differential equations, which considers reinfection and post-exposure interventions. We propose a multiobjective optimization approach to find optimal control strategies for the minimization of active infectious and persistent latent individuals, as well as the cost associated to the implementation of the control strategies. Optimal control strategies are investigated for different values of the model parameters. The obtained numerical results cover a whole range of the optimal control strategies, providing valuable information about the tuberculosis dynamics and showing the usefulness of the proposed approach.

[1]  Giulia Marchetti,et al.  Molecular Epidemiology Study of Exogenous Reinfection in an Area with a Low Incidence of Tuberculosis , 2001, Journal of Clinical Microbiology.

[2]  A. Vassall,et al.  How can mathematical models advance tuberculosis control in high HIV prevalence settings? , 2014, The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease.

[3]  J. Ball OPTIMIZATION—THEORY AND APPLICATIONS Problems with Ordinary Differential Equations (Applications of Mathematics, 17) , 1984 .

[4]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[5]  Khalid Hattaf,et al.  Optimal Control of Tuberculosis with Exogenous Reinfection , 2009 .

[6]  Delfim F. M. Torres,et al.  Cost-Effectiveness Analysis of Optimal Control Measures for Tuberculosis , 2014, Bulletin of Mathematical Biology.

[7]  M. L. Chambers The Mathematical Theory of Optimal Processes , 1965 .

[8]  Delfim F. M. Torres,et al.  Optimal control for a tuberculosis model with reinfection and post-exposure interventions. , 2013, Mathematical biosciences.

[9]  J. Janssen,et al.  Deterministic and Stochastic Optimal Control , 2013 .

[10]  Dany Djeudeu,et al.  Optimal Control of the Lost to Follow Up in a Tuberculosis Model , 2011, Comput. Math. Methods Medicine.

[11]  Marc Lipsitch,et al.  Beneficial and perverse effects of isoniazid preventive therapy for latent tuberculosis infection in HIV-tuberculosis coinfected populations. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[12]  G W Comstock,et al.  Epidemiology of tuberculosis. , 1982, The American review of respiratory disease.

[13]  Gabriele Eichfelder,et al.  Adaptive Scalarization Methods in Multiobjective Optimization , 2008, Vector Optimization.

[14]  Singiresu S. Rao,et al.  Optimization Theory and Applications , 1980, IEEE Transactions on Systems, Man, and Cybernetics.

[15]  C. Castillo-Chavez,et al.  To treat or not to treat: the case of tuberculosis , 1997, Journal of mathematical biology.

[16]  Samuel Bowong,et al.  Optimal control of the transmission dynamics of tuberculosis , 2010 .

[17]  C. Castillo-Chavez,et al.  A model for tuberculosis with exogenous reinfection. , 2000, Theoretical population biology.

[18]  L. Lasdon,et al.  On a bicriterion formation of the problems of integrated system identification and system optimization , 1971 .

[19]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[20]  P D van Helden,et al.  Exogenous reinfection as a cause of recurrent tuberculosis after curative treatment. , 1999, The New England journal of medicine.

[21]  M. Thakur,et al.  Global tuberculosis control report. , 2001, The National medical journal of India.

[22]  Marion Muehlen,et al.  Implications of partial immunity on the prospects for tuberculosis control by post-exposure interventions. , 2007, Journal of theoretical biology.

[23]  John T. Workman,et al.  Optimal Control Applied to Biological Models , 2007 .

[24]  Delfim F. M. Torres,et al.  Optimal control strategies for tuberculosis treatment: A case study in Angola , 2012, 1203.3255.

[25]  L. S. Pontryagin,et al.  Mathematical Theory of Optimal Processes , 1962 .

[26]  Margaret E Kruk,et al.  Timing of default from tuberculosis treatment: a systematic review , 2008, Tropical medicine & international health : TM & IH.

[27]  Filip Logist,et al.  Fast Pareto set generation for nonlinear optimal control problems with multiple objectives , 2010 .

[28]  Delfim F. M. Torres,et al.  Dengue disease, basic reproduction number and control , 2011, Int. J. Comput. Math..

[29]  Trina M Girimont,et al.  Dengue disease. , 2010, AAOHN journal : official journal of the American Association of Occupational Health Nurses.

[30]  Zi Sang,et al.  Optimal control of a vector-host epidemics model , 2011 .

[31]  V. Bowman On the Relationship of the Tchebycheff Norm and the Efficient Frontier of Multiple-Criteria Objectives , 1976 .

[32]  C. Yalçin Kaya,et al.  A numerical method for nonconvex multi-objective optimal control problems , 2014, Comput. Optim. Appl..

[33]  Lothar Thiele,et al.  Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study , 1998, PPSN.

[34]  P. Small,et al.  Management of tuberculosis in the United States. , 2001, The New England journal of medicine.

[35]  Suzanne Lenhart,et al.  Optimal control of treatments in a two-strain tuberculosis model , 2002 .

[36]  O Afonso,et al.  Exogenous reinfection with tuberculosis on a European island with a moderate incidence of disease. , 2001, American journal of respiratory and critical care medicine.

[37]  M Elizabeth Halloran,et al.  Epidemiological benefits of more-effective tuberculosis vaccines, drugs, and diagnostics , 2009, Proceedings of the National Academy of Sciences.

[38]  Delfim F. M. Torres,et al.  Dynamics of Dengue epidemics when using optimal control , 2010, Math. Comput. Model..