American Institute of Mathematical Sciences

Advanced
April  2018, 15(2): 485-505. doi: 10.3934/mbe.2018022

Optimal vaccination strategies for an SEIR model of infectious diseases with logistic growth

Markus Thäter , Kurt Chudej and  Hans Josef Pesch ,

Chair of Mathematics in Engineering Sciences, University of Bayreuth, Bayreuth, D 95440, Germany

* Corresponding author: Hans Josef Pesch

Received  July 30, 2016 Accepted  May 15, 2017 Published  June 2017

In this paper an improved SEIR model for an infectious disease is presented which includes logistic growth for the total population. The aim is to develop optimal vaccination strategies against the spread of a generic disease. These vaccination strategies arise from the study of optimal control problems with various kinds of constraints including mixed control-state and state constraints. After presenting the new model and implementing the optimal control problems by means of a first-discretize-then-optimize method, numerical results for six scenarios are discussed and compared to an analytical optimal control law based on Pontrygin's minimum principle that allows to verify these results as approximations of candidate optimal solutions.

Keywords: SEIR model, logistic growth, infectious diseases, optimal control, vaccination strategy, verification of numerical results.
Mathematics Subject Classification: Primary: 49K15, 49M25, 90C90, 92D30; Secondary: 34A34.
Citation: Markus Thäter, Kurt Chudej, Hans Josef Pesch. Optimal vaccination strategies for an SEIR model of infectious diseases with logistic growth. Mathematical Biosciences & Engineering, 2018, 15 (2) : 485-505. doi: 10.3934/mbe.2018022
References:
[1]

F. Bauer and C. Castillo-Chavez, Mathematical Models in Population Biology and Epidemiology, Part IV 2nd edition, Springer-Verlag, New York, 2012. doi: 10.1007/978-1-4614-1686-9.  Google Scholar

[2]

M. H. A. Biswas, L. T. Paiva and MdR. de Pinho, A SEIR model for control of infectious diseases with constraints, Mathematical Biosciences and Engineering, 11 (2014), 761-784.  doi: 10.3934/mbe.2014.11.761.  Google Scholar

[3]

A. E. Bryson, W. F. Denham and S. E. Dreyfus, Optimal Programming Problems with Inequality Constraints I, AIAA Journal, 1 (1963), 2544-2550.  doi: 10.2514/3.2107.  Google Scholar

[4] O. Diekmann, H. Heesterbeek and T. Britton, Mathematical Tools for Understanding Infectious Disease Dynamics, Princton University Press, Princton, 2013.   Google Scholar
[5]

R. Fourer, D. Gay and B. Kernighan, AMPL: A Modeling Language for Mathematical Programming Duxbury Press, Pacific Grove, 2002. Google Scholar

[6]

W. E. Hamilton, On nonexistence of boundary arcs in control problems with bounded state variables, IEEE Transactions on Automatic Control, AC-17 (1972), 338-343.   Google Scholar

[7]

R. F. Hartl, S. P. Sethi and R. G. Vickson, A survey of the maximum principles for optimal control problems with state constraints, SIAM Review, 37 (1995), 181-218.  doi: 10.1137/1037043.  Google Scholar

[8]

K. Ito and K. Kunisch, Asymptotic properties of receding horizont optimal control problems, SIAM J. Control Optim., 40 (2002), 1585-1610.  doi: 10.1137/S0363012900369423.  Google Scholar

[9]

D. H. Jacobson, M. M. Lele and J. L.. Speyer, New necessary conditions of optimality for control problems with state variable inequality constraints, Journal of Mathematical Analysis and Applications, 35 (1971), 255-284.  doi: 10.1016/0022-247X(71)90219-8.  Google Scholar

[10]

I. Kornienko, L. T. Paiva and MdR. de Pinho, Introducing state constraints in optimal control for health problems, Procedia Technology, 17 (2014), 415-422.  doi: 10.1016/j.protcy.2014.10.249.  Google Scholar

[11]

V. Lykina, Beiträge zur Theorie der Optimalsteuerungsprobleme mit unendlichem Zeithorizont, Dissertation, Brandenburgische Technische Universität Cottbus, Germany, 2010, http://opus.kobv.de/btu/volltexte/2010/1861/pdf/dissertationLykina.pdf. Google Scholar

[12]

V. Lykina, S. Pickenhain and M. Wagner, On a resource allocation model with infinite horizon, Applied Mathematics and Computation, 204 (2008), 595-601.  doi: 10.1016/j.amc.2008.05.041.  Google Scholar

[13]

H. Maurer and H. J Pesch, Direct optimization methods for solving a complex state-constrained optimal control problem in microeconomics, Applied Mathematics and Computation, 204 (2008), 568-579.  doi: 10.1016/j.amc.2008.05.035.  Google Scholar

[14]

H. Maurer and MdR. de Pinho, Optimal control of epidemiological SEIR models with L1-objectives and control-state constraints, Pac. J. Optim., 12 (2016), 415-436.   Google Scholar

[15]

J. D. Murray, Mathematical Biology: Ⅰ An Introduction 3rd edition, Springer-Verlag, New York, 2002.  Google Scholar

[16]

J. D. Murray, Mathematical Biology: Ⅱ Spatial Models and Biomedical Applications 3rd edition, Springer-Verlag, New York, 2003.  Google Scholar

[17]

R. M. Neilan and S. Lenhart, An introduction to optimal control with an application in disease modeling, DIMACS Series in Discrete Mathematics, 75 (2010), 67-81.   Google Scholar

[18]

H. J. Pesch, A practical guide to the solution of real-life optimal control problems, Control and Cybernetics, 23 (1994), 7-60.   Google Scholar

[19]

S. Pickenhain, Infinite horizon optimal control problems in the light of convex analysis in Hilbert Spaces, Set-Valued and Variational Analysis, 23 (2015), 169-189.  doi: 10.1007/s11228-014-0304-5.  Google Scholar

[20]

M. Plail and H. J. Pesch, The Cold War and the maximum principle of optimal control, Doc. Math. , 2012, Extra vol. : Optimization stories, 331–343.  Google Scholar

[21]

H. Schättler, U. Ledzewicz and H. Maurer, Sufficient conditions for strong local optimality in optimal control problems of L:2-type objectives and control constraints, Dicrete and Continuous Dynamical Systems Series B, 19 (2014), 2657-2679.  doi: 10.3934/dcdsb.2014.19.2657.  Google Scholar

[22]

M. Thäter, Restringierte Optimalsteuerungsprobleme bei Epidemiemodellen Master Thesis, Department of Mathematics, University of Bayreuth in Bayreuth, 2014. Google Scholar

[23]

P.-F. Verhulst, Notice sur la loi que la population suit dans son accroissement, Correspondance Mathématique et Physique, 10 (1838), 113-121.   Google Scholar

[24]

A. Wächter, An Interior Point Algorithm for Large-Scale Nonlinear Optimization with Applications in Process Engineering PhD Thesis, Carnegie Mellon University in Pittsburgh, 2002. Google Scholar

[25]

A. Wächter and L.T. Biegler, On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming, Mathematical Programming, 106 (2006), 25-57.  doi: 10.1007/s10107-004-0559-y.  Google Scholar

[26]

D. Wenzke, V. Lykina and S. Pickenhain, State and time transformations of infinite horizon optimal control problems, Contemporary Mathematics Series of The AMS, 619 (2014), 189-208.  doi: 10.1090/conm/619/12391.  Google Scholar

show all references

References:
[1]

F. Bauer and C. Castillo-Chavez, Mathematical Models in Population Biology and Epidemiology, Part IV 2nd edition, Springer-Verlag, New York, 2012. doi: 10.1007/978-1-4614-1686-9.  Google Scholar

[2]

M. H. A. Biswas, L. T. Paiva and MdR. de Pinho, A SEIR model for control of infectious diseases with constraints, Mathematical Biosciences and Engineering, 11 (2014), 761-784.  doi: 10.3934/mbe.2014.11.761.  Google Scholar

[3]

A. E. Bryson, W. F. Denham and S. E. Dreyfus, Optimal Programming Problems with Inequality Constraints I, AIAA Journal, 1 (1963), 2544-2550.  doi: 10.2514/3.2107.  Google Scholar

[4] O. Diekmann, H. Heesterbeek and T. Britton, Mathematical Tools for Understanding Infectious Disease Dynamics, Princton University Press, Princton, 2013.   Google Scholar
[5]

R. Fourer, D. Gay and B. Kernighan, AMPL: A Modeling Language for Mathematical Programming Duxbury Press, Pacific Grove, 2002. Google Scholar

[6]

W. E. Hamilton, On nonexistence of boundary arcs in control problems with bounded state variables, IEEE Transactions on Automatic Control, AC-17 (1972), 338-343.   Google Scholar

[7]

R. F. Hartl, S. P. Sethi and R. G. Vickson, A survey of the maximum principles for optimal control problems with state constraints, SIAM Review, 37 (1995), 181-218.  doi: 10.1137/1037043.  Google Scholar

[8]

K. Ito and K. Kunisch, Asymptotic properties of receding horizont optimal control problems, SIAM J. Control Optim., 40 (2002), 1585-1610.  doi: 10.1137/S0363012900369423.  Google Scholar

[9]

D. H. Jacobson, M. M. Lele and J. L.. Speyer, New necessary conditions of optimality for control problems with state variable inequality constraints, Journal of Mathematical Analysis and Applications, 35 (1971), 255-284.  doi: 10.1016/0022-247X(71)90219-8.  Google Scholar

[10]

I. Kornienko, L. T. Paiva and MdR. de Pinho, Introducing state constraints in optimal control for health problems, Procedia Technology, 17 (2014), 415-422.  doi: 10.1016/j.protcy.2014.10.249.  Google Scholar

[11]

V. Lykina, Beiträge zur Theorie der Optimalsteuerungsprobleme mit unendlichem Zeithorizont, Dissertation, Brandenburgische Technische Universität Cottbus, Germany, 2010, http://opus.kobv.de/btu/volltexte/2010/1861/pdf/dissertationLykina.pdf. Google Scholar

[12]

V. Lykina, S. Pickenhain and M. Wagner, On a resource allocation model with infinite horizon, Applied Mathematics and Computation, 204 (2008), 595-601.  doi: 10.1016/j.amc.2008.05.041.  Google Scholar

[13]

H. Maurer and H. J Pesch, Direct optimization methods for solving a complex state-constrained optimal control problem in microeconomics, Applied Mathematics and Computation, 204 (2008), 568-579.  doi: 10.1016/j.amc.2008.05.035.  Google Scholar

[14]

H. Maurer and MdR. de Pinho, Optimal control of epidemiological SEIR models with L1-objectives and control-state constraints, Pac. J. Optim., 12 (2016), 415-436.   Google Scholar

[15]

J. D. Murray, Mathematical Biology: Ⅰ An Introduction 3rd edition, Springer-Verlag, New York, 2002.  Google Scholar

[16]

J. D. Murray, Mathematical Biology: Ⅱ Spatial Models and Biomedical Applications 3rd edition, Springer-Verlag, New York, 2003.  Google Scholar

[17]

R. M. Neilan and S. Lenhart, An introduction to optimal control with an application in disease modeling, DIMACS Series in Discrete Mathematics, 75 (2010), 67-81.   Google Scholar

[18]

H. J. Pesch, A practical guide to the solution of real-life optimal control problems, Control and Cybernetics, 23 (1994), 7-60.   Google Scholar

[19]

S. Pickenhain, Infinite horizon optimal control problems in the light of convex analysis in Hilbert Spaces, Set-Valued and Variational Analysis, 23 (2015), 169-189.  doi: 10.1007/s11228-014-0304-5.  Google Scholar

[20]

M. Plail and H. J. Pesch, The Cold War and the maximum principle of optimal control, Doc. Math. , 2012, Extra vol. : Optimization stories, 331–343.  Google Scholar

[21]

H. Schättler, U. Ledzewicz and H. Maurer, Sufficient conditions for strong local optimality in optimal control problems of L:2-type objectives and control constraints, Dicrete and Continuous Dynamical Systems Series B, 19 (2014), 2657-2679.  doi: 10.3934/dcdsb.2014.19.2657.  Google Scholar

[22]

M. Thäter, Restringierte Optimalsteuerungsprobleme bei Epidemiemodellen Master Thesis, Department of Mathematics, University of Bayreuth in Bayreuth, 2014. Google Scholar

[23]

P.-F. Verhulst, Notice sur la loi que la population suit dans son accroissement, Correspondance Mathématique et Physique, 10 (1838), 113-121.   Google Scholar

[24]

A. Wächter, An Interior Point Algorithm for Large-Scale Nonlinear Optimization with Applications in Process Engineering PhD Thesis, Carnegie Mellon University in Pittsburgh, 2002. Google Scholar

[25]

A. Wächter and L.T. Biegler, On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming, Mathematical Programming, 106 (2006), 25-57.  doi: 10.1007/s10107-004-0559-y.  Google Scholar

[26]

D. Wenzke, V. Lykina and S. Pickenhain, State and time transformations of infinite horizon optimal control problems, Contemporary Mathematics Series of The AMS, 619 (2014), 189-208.  doi: 10.1090/conm/619/12391.  Google Scholar

Figure 1.  The SEIR model with vaccination; cp. [2]
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 2.  Scenario 0: The progress of the population without control
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 3.  cenario 1: The progress of the population for WM = ∞
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 4.  Scenario 1: Discrete and verified optimal control for WM = ∞
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 5.  Scenario 2: "Almost bang-bang" control for WM = 2 500 when vaccination is stopped
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 6.  Scenario 2: The infected and the recovered population. The latter exhibits an "almost kink" when vaccination is stopped
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 7.  Scenario 3: The upper picture shows the two competing control candidates according to (33) and (36), the latter for V0 = 125. The optimal control is the boundary control (36) of the mixed control-state constraint (34) almost over the entire time interval. Only at the end, the control (32) in the interior of the admissble set becomes active. In the lower picture the discrete and the verified control values are compared showing again a perfect coincidence. The upright bar marks the switching time.
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 8.  Scenario 3: Time behaviour of the infected and recovered population. The terminal value of the recovered population is a bit higher than in the model with a finite amount of vaccines
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 9.  Scenario 4: Susceptible population and optimal control for $S_{\max} = 1\,200$. The upright lines mark the switching points from unconstrained boundary arcs on state-constrained ones or vice versa. Three boundary arcs occur here; the last one is extremely short; see the zoom. Note that a touch point cannot exist here [6]. The optimal control is discontinuous at entry and/or exit points due to the jump discontinuities of $\lambda_S$ at these junction points; see [7]
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 11.  Scenario 5: Susceptible and infected population: entry and exit point to the state constrained arc are marked in green resp.red. The maximal allowed values $ S_{\max} $ and $ V_0/u(t) $ are marked in blue and purple respectively
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 10.  Scenario 5: The approximate candidate optimal control: the verification test yields $ 8.5 \cdot 10^{-8} $, hence indicating again a perfect coincidence
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 12.  Scenario 6: Discounted functional. The progress of the population for $ W_M = \infty $
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 13.  Scenario 6: Discounted functional. Discrete and verified optimal control for $ W_M = \infty $
Figure Options

Download full-size image

Download as PowerPoint slide

Table 1.  Values from [17], also chosen in [2]1
Parameters Definitions Units Values
$ b $natural birth rateunit of time$^{-1}$0.525
$ d $natural death rateunit of time$^{-1}$0.5
$ c $incidence coefficient $\frac{1}{\mbox{ unit of capita}\,\cdot\,\mbox{unit of time}}$0.001
$ e $exposed to infectious rateunit of time$^{-1}$0.5
$ g $natural recovery rateunit of time$^{-1}$0.1
$ a $disease induced death rateunit of time$^{-1}$0.1
$ u_{\max} $maximum vaccination rateunit of time$^{-1}$1
$ W_M $maximum available vaccinesunit of capitavarious
$ V_0 $upper bound in Eq. 34 $\frac{\mbox{ unit of capita}}{\mbox{ unit of time}}$various
$ S_{\max} $upper bound in Eq. 38unit of capitavarious
$ A_1 $weight parameter $\frac{\mbox{ unit of money}}{\mbox{ unit of capita}\, \cdot\,\mbox{ unit of time}}$0.1
$ A_2 $weight parameterunit of money$\,\cdot\,$unit of time1
$ t_0 $initial timeunit of time (years)0
$ T $final timeunit of time (years)20
$ S_0 $initial susceptible populationunit of capita1000
$ E_0 $initial exposed populationunit of capita100
$ I_0 $initial infected populationunit of capita50
$ R_0 $initial recovered populationunit of capita15
$ N_0 $initial total populationunit of capita1165
$ W_0 $initial vaccinated populationunit of capita0
Table Options

Download as excel

Parameters Definitions Units Values
$ b $natural birth rateunit of time$^{-1}$0.525
$ d $natural death rateunit of time$^{-1}$0.5
$ c $incidence coefficient $\frac{1}{\mbox{ unit of capita}\,\cdot\,\mbox{unit of time}}$0.001
$ e $exposed to infectious rateunit of time$^{-1}$0.5
$ g $natural recovery rateunit of time$^{-1}$0.1
$ a $disease induced death rateunit of time$^{-1}$0.1
$ u_{\max} $maximum vaccination rateunit of time$^{-1}$1
$ W_M $maximum available vaccinesunit of capitavarious
$ V_0 $upper bound in Eq. 34 $\frac{\mbox{ unit of capita}}{\mbox{ unit of time}}$various
$ S_{\max} $upper bound in Eq. 38unit of capitavarious
$ A_1 $weight parameter $\frac{\mbox{ unit of money}}{\mbox{ unit of capita}\, \cdot\,\mbox{ unit of time}}$0.1
$ A_2 $weight parameterunit of money$\,\cdot\,$unit of time1
$ t_0 $initial timeunit of time (years)0
$ T $final timeunit of time (years)20
$ S_0 $initial susceptible populationunit of capita1000
$ E_0 $initial exposed populationunit of capita100
$ I_0 $initial infected populationunit of capita50
$ R_0 $initial recovered populationunit of capita15
$ N_0 $initial total populationunit of capita1165
$ W_0 $initial vaccinated populationunit of capita0
[1]

M. H. A. Biswas, L. T. Paiva, MdR de Pinho. A SEIR model for control of infectious diseases with constraints. Mathematical Biosciences & Engineering, 2014, 11 (4) : 761-784. doi: 10.3934/mbe.2014.11.761
[2]

Martin Luther Mann Manyombe, Joseph Mbang, Jean Lubuma, Berge Tsanou. Global dynamics of a vaccination model for infectious diseases with asymptomatic carriers. Mathematical Biosciences & Engineering, 2016, 13 (4) : 813-840. doi: 10.3934/mbe.2016019
[3]

IvÁn Area, FaÏÇal NdaÏrou, Juan J. Nieto, Cristiana J. Silva, Delfim F. M. Torres. Ebola model and optimal control with vaccination constraints. Journal of Industrial & Management Optimization, 2018, 14 (2) : 427-446. doi: 10.3934/jimo.2017054
[4]

Hongbin Guo, Michael Yi Li. Global dynamics of a staged progression model for infectious diseases. Mathematical Biosciences & Engineering, 2006, 3 (3) : 513-525. doi: 10.3934/mbe.2006.3.513
[5]

Arvind Kumar Misra, Rajanish Kumar Rai, Yasuhiro Takeuchi. Modeling the control of infectious diseases: Effects of TV and social media advertisements. Mathematical Biosciences & Engineering, 2018, 15 (6) : 1315-1343. doi: 10.3934/mbe.2018061
[6]

Yinggao Zhou, Jianhong Wu, Min Wu. Optimal isolation strategies of emerging infectious diseases with limited resources. Mathematical Biosciences & Engineering, 2013, 10 (5&6) : 1691-1701. doi: 10.3934/mbe.2013.10.1691
[7]

Yali Yang, Sanyi Tang, Xiaohong Ren, Huiwen Zhao, Chenping Guo. Global stability and optimal control for a tuberculosis model with vaccination and treatment. Discrete & Continuous Dynamical Systems - B, 2016, 21 (3) : 1009-1022. doi: 10.3934/dcdsb.2016.21.1009
[8]

Edoardo Beretta, Dimitri Breda. An SEIR epidemic model with constant latency time and infectious period. Mathematical Biosciences & Engineering, 2011, 8 (4) : 931-952. doi: 10.3934/mbe.2011.8.931
[9]

Enrique Fernández-Cara, Irene Marín-Gayte. Theoretical and numerical results for some bi-objective optimal control problems. Communications on Pure & Applied Analysis, 2020, 19 (4) : 2101-2126. doi: 10.3934/cpaa.2020093
[10]

Ellina Grigorieva, Evgenii Khailov. Optimal control of a nonlinear model of economic growth. Conference Publications, 2007, 2007 (Special) : 456-466. doi: 10.3934/proc.2007.2007.456
[11]

Siyu Liu, Yong Li, Yingjie Bi, Qingdao Huang. Mixed vaccination strategy for the control of tuberculosis: A case study in China. Mathematical Biosciences & Engineering, 2017, 14 (3) : 695-708. doi: 10.3934/mbe.2017039
[12]

Siyu Liu, Xue Yang, Yingjie Bi, Yong Li. Dynamic behavior and optimal scheduling for mixed vaccination strategy with temporary immunity. Discrete & Continuous Dynamical Systems - B, 2019, 24 (4) : 1469-1483. doi: 10.3934/dcdsb.2018216
[13]

Shujing Gao, Dehui Xie, Lansun Chen. Pulse vaccination strategy in a delayed sir epidemic model with vertical transmission. Discrete & Continuous Dynamical Systems - B, 2007, 7 (1) : 77-86. doi: 10.3934/dcdsb.2007.7.77
[14]

Aili Wang, Yanni Xiao, Robert A. Cheke. Global dynamics of a piece-wise epidemic model with switching vaccination strategy. Discrete & Continuous Dynamical Systems - B, 2014, 19 (9) : 2915-2940. doi: 10.3934/dcdsb.2014.19.2915
[15]

Urszula Ledzewicz, Omeiza Olumoye, Heinz Schättler. On optimal chemotherapy with a strongly targeted agent for a model of tumor-immune system interactions with generalized logistic growth. Mathematical Biosciences & Engineering, 2013, 10 (3) : 787-802. doi: 10.3934/mbe.2013.10.787
[16]

Zhilan Feng, Carlos Castillo-Chavez. The influence of infectious diseases on population genetics. Mathematical Biosciences & Engineering, 2006, 3 (3) : 467-483. doi: 10.3934/mbe.2006.3.467
[17]

Majid Jaberi-Douraki, Seyed M. Moghadas. Optimal control of vaccination dynamics during an influenza epidemic. Mathematical Biosciences & Engineering, 2014, 11 (5) : 1045-1063. doi: 10.3934/mbe.2014.11.1045
[18]

Maria do Rosário de Pinho, Helmut Maurer, Hasnaa Zidani. Optimal control of normalized SIMR models with vaccination and treatment. Discrete & Continuous Dynamical Systems - B, 2018, 23 (1) : 79-99. doi: 10.3934/dcdsb.2018006
[19]

Yoshiaki Muroya, Yoichi Enatsu, Huaixing Li. A note on the global stability of an SEIR epidemic model with constant latency time and infectious period. Discrete & Continuous Dynamical Systems - B, 2013, 18 (1) : 173-183. doi: 10.3934/dcdsb.2013.18.173
[20]

Maria do Rosário de Pinho, Filipa Nunes Nogueira. On application of optimal control to SEIR normalized models: Pros and cons. Mathematical Biosciences & Engineering, 2017, 14 (1) : 111-126. doi: 10.3934/mbe.2017008

2018 Impact Factor: 1.313

Tools

Metrics

  • PDF downloads (107)
  • HTML views (164)
  • Cited by (0)

Other articles
by authors

[Back to Top]

Copyright © 2020 American Institute of Mathematical Sciences