# THE SIMULATION OF CONTROLING THE SPREAD OF HIV USING ANALYTICAL AND NUMERICAL APPROACH

Gratis

Bulletin of Mathematics

ISSN Printed: 2087-5126; Online: 2355-8202 Vol. 07, No. 02 (2015), pp. 13–33 http://jurnal.bull-math.org

THE SIMULATION OF CONTROLING THE

SPREAD OF HIV USING ANALYTICAL

AND NUMERICAL APPROACH

Miksalmina, Said Munzir and Tarmizi

Abstract.This research is related to the optimal control problem in controlling the spread of HIV (Human Immunodeficiency Virus) to minimize the amount of HIV virus and treatment costs. The solution of the problem in this is conducted by the analytical and numerical approaches. The solution is initially conducted analytically through linearization of state equations around equilibrium point, while the second solution is conducted numerically using the direct discretization method with pseu- dospectral approach. The analytical solution procedure using Pontryagin Minimum Principle with the help of MATLAB symbolic features, while numerical solution us- ing Gaus Pseudospectral Method (GPM) with the help of TOMLAB/PROPT. The results of numerical simulations for controlling the spread of the HIV virus to the initial time t = 0 and the final time t f = 500 successfully minimize mutant HIV strain, proviral Th cells infected by the mutant strain , and Th cells productively infected by the new strain as well as minimizing the cost of therapy is needed. The optimal solution indicates an increase in uninfected Th cells and a decrease in wild- type HIV, proviral Th cells (wild type), productively infected Th cells (wild type), mutant HIV strain, proviral Th cells infected by the mutant strain and Th cells pro- ductively infected by the new strain. These results showed a significant difference with the analytical solution. There are many potential differences in the results, including as a result of linearization at the equilibrium point and the condition number for the matrix involved in the analytic solution.

Received 11-12-2014, Accepted 12-05-2015. 2010 Mathematics Subject Classification: 30C45 Key words and Phrases: HIV, Optimal Control, Pontryagin Minimum Principle, Gauss Pseu- dospectral Method (GPM)

## 1. INTRODUCTION

Acquired Immuno Deficiency Syndrome (AIDS) is an infectious disease caused by a virus called Human Immuno Deficiency Virus (HIV). HIV (Hu- man Immunodeficiency Virus) is a virus that attacks the human immune system. This disease is a dangerous disease and should be aware because it spread rapidly around the world [9]. One approach for explaining the solution of the problems of the spread of the HIV virus that occur in the real world is to translate the problem into mathematical language

According to Setiawan [9], by translating problems into mathematical language it will obtain a mathematical models, which describe the results of the formulation of the problem to be solved. Mathematical models can be applied to determine the spread of the HIV virus. The purpose of modeling is to control the spread of the HIV virus where its complexity tends to increase.

Various attempts were made to control the spread of the HIV virus. One of them is the solution of drug therapy. Fariana [4] have conducted an analysis of stability and optimal control of drug therapy in the treatment of HIV with Highly Active Anti-Retroviral Therapy (HAART). HAART is a method of treatment which is performed in patients with HIV that aims to slow the progres of HIV infection to AIDS.

Research results show the effectiveness of drug control to suppress the development of the HIV virus with the provision of Anti Retroviral Drugs (ARVs). ARV is a drug that works inhibit HIV replication in CD4 (white blood cells), so as to reduce the amount of virus in the blood.

The results of two studies conducted by Fariana [4] and Sukokarlinda, et al (2013) provide a solution that is not optimal. This is due to HAART and the provision of antiretroviral drugs can not eliminate the life cycle strains (strains) of HIV in a population vary widely and can cause serious side effects. Therefore, it is necessary to control the spread of HIV in other ways.

Stengel [11] has conducted a simulation control of HIV by developing a model of Perelson, et al [1] and Kirschner, et al [3] in the form of four dynamic equations. This model was then developed into seven dynamic equations which describe some therapies as the treatment.

Based on previous studies, the authors would like to continue the re- search in terms of controlling the spread of the HIV virus. This study adopts a model studied by Stengel [11] and Afandi [2]. The model minimizes the The model is solved by analytical and numerical approaches. Analytic ap- proach is solved with the help of MATLAB symbolic, while the numerical approximation is solved with the helping TOMLAB/propt.

## 2. METHODS

Research activities include study design, study subjects, research pro- cedures and research instruments. The design of the research conducted in this research is to collect research materials including sources of informa- tion both journals, books articles and other relevant instructions. In this case study refers to internasionl journal entitled Mutation and control of the human immudeficiency virus written by Robert F. Stengel [11]. In this case study refers to internasionl journal entitled Mutation and control of the human immudeficiency virus written by Robert F. Stengel [11]. The journal reference used is a journal with the title Dynamics of HIV infection of CD4+T Cells by Perelson [1] and Optimal control of the chemotherapy of HIV by Kirschner [3] and Strategy Control the spread of HIV wild type and mutant therapy with inhibitors by Afandi [2]. The subject of research in this paper is to analyze the results Stengel [11] using simulation TOM- LAB/propt. Besides understanding the theories that support the discussion of optimal control problems that include: Pontryagin Minimum Principle, Gauss Pseudospectral Method (GPM), the program TOMLAB/propt and symbolic methods.

Troubleshooting control the spread of HIV in this study through an- alytical and numerical approaches. analytical solution performed with lin- earized equation of state around the equilibrium point with the help of MATLAB symbolic. While the settlement is done numerically by direct discretization using pseudospectral method with the help TOMLAB/propt.

Simulations done with therapy and no therapy for optimal control problems in controlling the spread of HIV is done with the initial time t

= 0 and the final time t f = 500, the value of the control variable for therapy u , u , u , u ranges between 0 and 1. Simulation is done to control

1

2

3

4 the spread of HIV processing time for 500 days.

Simulations are also carried out with a view to minimizing the number of objective function the mutant HIV strain (x ), proviral T cells infected 5 h by the mutant strain (x ), and T h cells productively infected by the new

6 strain (x ) as well as the cost of therapy. While the control variable is

7 therapy *protease inhibitor *(u ), *fussion inhibitor *(u ), T h *cell enhancer *(u )

1

2

3 the parameter initial values of the state variables are different, using the parameter values of the journal Stengel [11].

## 3. RESULTS

Dynamic system used in this study consisted of seven differential equa- tions, each of which express a mathematical model consisting of : wild-type HIV, uninfected T cells, proviral T cells (wild type), productively infected h h

T h cells (wild type), mutant HIV strain, proviral T h cells infected by the mutant strain and T h cells productively infected by the new strain. System of differential equations describe the spread of the HIV virus and the type of therapy as control variables are given by the following dynamical system [11]: x x x a x

˙x = a − a (1 − u ) + a (1 − a )(1 − u )

1

1

1

2

1

2

2

3

4

4

10

1 a

5 x x x a x x

˙x 2 = − a

2

1 2 (1 − u 2 )(1 − u 4 ) − a

6 2 − a

2

11

2

5 1 + x + x

1

5 x

- x + x + x + x

2

3

4

6

7

- a 1 − x (1 + u )

7

2

3 a

8 x x x x

˙x 3 = a

2

1 2 (1 − u 2 )(1 − u 4 ) − a

9 3 − a

6

3 ˙x = a x − a x

4

9

3

4

4 a a x a x x a x x

˙x = a + a − a − a

5

3

4

10

4

3

4

7

1

5

2

11

5

2 ˙x = a a x x − a x − a x

6

2

11

5

2

9

6

6

6 x x

˙x = a − a (1)

7

9

6

4

7 with

2 i dt

(2)

*study, variations in circumstances that meet the type of control system ed-*

*final time and free-final state system*, where x(t ) = x dan x(t f ) = x f free.

Optimal Control Solution Optimal control problems in this research is to minimize the number of ma- lignant and mutant viruses as well as the cost of therapy are formulated in the objective function as follows:

J [x(t), u(t)] =

1

2 h S f

55 x

5 (t f )

2

66 x

6 (t f )

2 i

2 t

4 = Reverse trancription inhibitor

f

Z t h q

55 x

5 (t)

2

66 x

6 (t)

2

77 x

7 (t)

2

System of differential equations (1) above is referred to as equation of state (state equation). Each differential equations dynamical systems above have the initial conditions and final time (t f ) is determined. *Fix In this*

3 = T h cells enhancer u

˙x

2 = Constant average healthy cells become infected a

1 = Wild-type HIV

˙x

2 = Uninfected T h cells

˙x

3 = Proviral T h cells (wild type)

˙x

4 = Productively infected T h cells (wild type)

˙x 5 = Mutant HIV strain ˙x

6 = Proviral T h cells infected by the mutant strain

˙x 7 = T h cells productively infected by the new strain a

1 = The average particle death a

3 = The amount of HIV virus produced a

2 = Fussions inhibitor u

4 = Average mortality ˙x

4 a

5 = Sources of healthy cells a

6 = Average mortality ˙x

1 and ˙x

3 a

7 = The average growth of healthy cells a

8 = The maximum level of healthy cells a

9 = Average of infected cells become productive a

10 = The mutation rate a

11 = The mutant strain has a fitness u

1 = Protease inhibitor u

- S f
- 1
- q
- q
- ru i (t)

## 1. Around Equilibrated point linearization

1 u

1 x

∗

2 x

∗

3 x

∗

4 x

∗

5 x

∗

6 x

∗

7 , u

∗

∗

Taylor series approach the equation system around the equilibrium point is F

2 u

∗

3 u

∗

4 )

∗

∗

∗

∂f ∂x ^{1}

∗

∗

∗

∗

∗

(x, u) = f (x ∗

4 = 0 thus obtained form a linear system. By using the

∗

∗

∗

Where the value of u is taken from the set u, u ∈ U = {u(t)|0 ≤ u ≤ 1, 0 ≤ t ≤ t f

}. In this case, u as (0 ≤ u ≤ 1) is a measure of the medical business (with the upper limit of 1 and the rate of medical efforts made proportionally from the amount of HIV virus). Equation (2) shows r is a variable that is correlated with weight control usage costs (cost control) is a balancing factor of cost control system. whereas S f

55 , S f

66 , S f

77 , q

55 , q

66 , q

77 is a diagonal matrix elements are presented in equation (2) which states that permitted reciprocal relationship between the values of the response to the charges, with the aim of balancing the speed and efficacy of treatment, in this case will look for a minimum function.

Analytical methods Optimal treatment strategy is obtained by solving the optimality system, consisting of fourteen ordinary differential equation of state and costate equations. The settlement resolved by designing a symbolic program MAT- LAB, by first linearized seven state equation.

To liniarized seven state equation then determined equilibrium point, where x ∗

1 = x

∗

3 = x

4 = x

∗

∗

5 = x

∗

6 = x

∗

7 = 0, x

∗

2 = 1000 and assumed u

∗

1 = u

∗

2 = u

∗

3 = u

∗

- (x

4 x

- (x 2 − x
- (x
- (x
- (x 5 − x
- (x

1 x

∗

2 x

∗

3 x

∗

4 x

∗

5 x

∗

6 x

∗

7 , u

∗

∗

1 u

2 u

∗

3 u

∗

4 )

2 − u

∗

2 )

∂f ∂u ^{2}

|(x ∗

1 x

∗

2 x

∗

|(x ∗

∂f ∂u ^{1}

∗

4 x

3 u

∗

4 )

7 − x

∗

7 )

∂f ∂x ^{7}

|(x ∗

1 x

∗

2 x

∗

3 x

∗

∗

1 )

5 x

∗

6 x

∗

7 , u

∗

1 u

∗

2 u

∗

3 u

∗

4 )

∗

3 x

4 x

2 u

4 x

∗

3 u

∗

4 )

∗

4 )

∂f ∂u ^{4}

|(x ∗

1 x

∗

2 x

∗

3 x

∗

∗

∗

5 x

∗

6 x

∗

7 , u

∗

1 u

∗

2 u

∗

3 u

∗

4 ) (3)

Based on the equation (3) above obtained results linearized state equation as follows:

2 u

1 u

∗

∗

5 x

∗

6 x

∗

7 , u

∗

1 u

∗

2 u

∗

3 u

∗

4 )

3 − u

3 )

∗

4 x

7 , u

∗

6 x

∗

5 x

∗

∗

∂f ∂u ^{3}

3 x

∗

2 x

∗

1 x

|(x ∗

∗

1 u

∗

4 x

3 u

∗

4 )

3 − x

∗

3 )

∂f ∂x ^{3}

|(x ∗

1 x

∗

2 x

∗

3 x

∗

∗

2 u

5 x

∗

6 x

∗

7 , u

∗

1 u

∗

2 u

∗

3 u

∗

4 )

4 − x

∗

∗

3 x

∂f ∂x ^{2}

5 x

2 x

6 x

1 x

7 , u

|(x ∗

1 u

1 )

2 u

1 − x

3 u

4 )

∗

2 )

|(x ∗

1 u

1 x

∗

2 x

∗

3 x

∗

4 x

∗

5 x

∗

6 x

∗

7 , u

∗

∗

4 )

∂f ∂x ^{4}

∂f ∂x ^{6}

6 x

∗

7 , u

∗

1 u

∗

2 u

∗

3 u

∗

4 )

6 − x

∗

6 )

|(x ∗

|(x ∗

1 x

∗

2 x

∗

3 x

∗

4 x

∗

5 x

∗

6 x

∗

7 , u

∗

∗

5 x

∗

∗

1 x

∗

2 x

∗

3 x

∗

4 x

∗

5 x

∗

6 x

∗

7 , u

∗

1 u

∗

∂f ∂x ^{5}

3 x

∗

2 x

∗

1 x

|(x ∗

5 )

2 u

∗

4 )

∗

3 u

∗

4 x

- (x
- (u 1 − u
- (u
- (u
- (u 4 − u
- a

- x
- a
- (x
- a
- x
- x
- x
- x
- x
- x
- x
- x
- u

- a
- 0.2837e
- 2.8115 × 10
- 0.3421e
- 4.1356 × 10
- 1.2664 × 10
- 0.2000e
- 2.545 × 10
- 0.2448e
- 2.1825 × 10
- 54.7776e
+ 60.9346e

^{2 .4204t}- 191.9484e
+ 10.8370e

- 9.6902 × 10
- 6.1951 × 10

^{51}e .

7 (a

3 a

4 ) dx

6 dt

= x

5 (a

2 a

11 x

2 ) + x

6 (a

9 − a

6 ) dx

7 dt

= x

6 (a

2 ) + x

11 x

2 a

4 ) dx

6 )) dx

4 dt

= x

3 (a

9 ) + x

4 (−a

5 dt

1 − a

= x

4 (a

3 a

4 a

10 ) + x

5 (−a

9 ) + x

7 (a

4 )

−0.0041t −124.3225e

−34 e

.2745t −2.7789e

−0.2635t

.0041t − 4.78083 × 10 ^{18} e

.0300t −2.3902 × 10 ^{18} e

.0300t

.0079t − 193.4611e

−0.0300t

−0.0079t − 0.2275e

−2.4120t

−0.2745t

−57 e

.2745t

− 3e ^{2}

−2.4120t

∗ _{2 =} 2.3902 × 10 ^{18} e

Results linearization of equation (4) above, then solved using Pontrya- gin minimum principle with the help of MATLAB symbolic program, in order to obtain the general solution of the homogeneous and non- homoge- neous equations. Final solution of this problem is obtained as follows:

−15 e

x ∗ _{1}

= −0.4347e −0.0041t

−13 e

.0041t

.0079t

−0.0079t

−2.4120t

.4204t x

−52 e

.2635t

−0.2745t − 6.7672 × 10

−57 e

.2745t

−34 e ^{2}

9

2 ) + x

3 (−(a

2 − x

8 x

2

1 − (a

5

2 x

2 )

∗

7 1 − x

2 ) (−a

6 ) − a

7 x

2 a

8

7 (1 − x

2 a

2

1 x

2 ) + x

dx

1 dt

= x

1 (−a

1 − a

2 x

4 a

6 x

3 a

4 (1 − a

1 0) dx

2 dt

= a

5 − a

2 a

8 ) + x

3 a

4

3 a

7 x

2 a

7 x

2

3

6

7 x

7 a

8 x

2 dx

3 dt

= x

1 (a

8

2 a

7 x

7 a

2 a

8

4 a

7 x

2 a

8

5 − (a 5 + a

2 a

11 x

2 )

6 a

7 x

2 a

8

.4204t

- 1000
- 1.0277 × 10

13 .0041t ∗ −0.0079t −0.0041t x e _{3 = 0.2200e + 0.1343e}

- 2.0342 × 10

.2635t −0.0079t −2.4120t −52 e −0.3220e + 1.4630 × 10−17e − 6.6541 × 10

.2745t ^{2 .4204t} −0.2745t −57 −36 e

- 0.0017e
- 9.4611 × 10 − 2.5481 × 10 e −

.0041t .0079t

∗ − −0.0041t −0.0079t x_{4}= 13e + 0.0027e + 0.0028e

2.0342 × 10 − 0.0029e .2635t .2745t − −2.4120t

19e + 0.0017e

−1.8932 × 10 − 2.3606 × 10−54e .2745t −59 −39 −2.4204t e e- 2.7964 × 10 + 3.5060 × 10

.0041t .0079t .0079t

∗ −0.0041t −2.4120t

x_{5}= + 0.1865e + 0.0773e

−0.2071e − 0.0671e − 0.0541e

_{52}.2635t −0.2745t −35 −2.4204t

e e

- 0.0645e −1.3330 × 10 − 1.2014 × 10

.0041t .0079t ∗ −0.0041t −0.0079t x _{6} = + 0.1076e + 0.1166e

−0.1415e − 0.0795e .2635t −4 −2.4120t −0.2745t e

- 3.2531 × 10 − 6.5957 × 10−54e − 0.0036e

.2745t −58 −38 −2.4204t e e

- 2.2545 × 10 + 7.1983 × 10

.0041t .0041t .0079t ∗ −4 x e _{7} = 0.0017e + 0.0013e

− 2.1395 × 10 .2635t −4 −0.0079t −7 −2.4120t −55 e e e

- 3.3760 × 10 − 4.4933 × 10 − 8.2012 × 10
- 3.1349 × 10 + 1.4838 × 10 − 9.9040 × 10

_{41}.2745t −4 −0.2745t −59 −2.4204t

∗ u _{1} = ∗ u _{2 =}

∗ u _{3 =}

Of the general solution can chart of satay and control variables are presented in Figure 1 and Figure 2 as follows: Figure 1 shows that the number of wild-type HIV, uninfected T h cells, proviral T cells (wild type), productively infected T cells (wild type), mu- h h tant HIV strain, proviral T h cells infected by the mutant strain and T h cells productively infected by the new strain.

Figure 2 shows the therapy of *protease inhibitor, fussion inhibitor*, T h cells enhancer, *reverse trancription inhibitor *is static. It indicates that the four therapeutic fixed and unchanged.

## 2. Linearization point Optimal Numerical Results

In this case, the point of which is used for seven linearized state ∗ equation that the optimal point TOMLAB results, where x = 0.001988,

1 ∗ ∗ ∗ ∗ ∗ x

= 983.68099, x = 0.00568, x = 0.00025, x = 0.007212, x = 0.003196,

2

3

4

5

6 ∗ −4 ∗ ∗ ∗ ∗ x = 0.39 × 10 and u = 0.365782, u = 1, u = 1, u = 1, thus obtained

7

1

2

3

4 form a linear system. By using the Taylor series approach the equation sys- tem around the equilibrium point is:

Figure 2: Analytical Solutions In Control

- (x
- (x
- (x 3 − x
- (x
- (x
- (x 6 − x
- (x

- (u
- (u 2 − u
- (u
- (u
- 1.5855 × 10
- (x
- 1.9674 × 10
- (x
- (x
- a 7 (1−983.6902a
- (x
- (x
- u
- (x
- (x
- 0.39 × 10
- (x
- (x
- (x
- (x

−4 a

1

= −0.001988a

Based on the equation (4) in the above obtained results linearized state equation as follows: dx ^{1} dt

4 ) (4)

∗

3 u

∗

2 u

∗

1 u

∗

7 , u

∗

6 x

∗

5 x

∗

4 x

∗

3 x

∗

2 x

∗

1 x

3 a

4 (1 − a

10 )(1 − u

4 (1 − a

983.6902 a ^{8}

7 1 −

3 a

11

2 a

6 − 7.0943a

984.6830 − 983.68099a

= a ^{5}

1 ) dx ^{2} dt

∗

10 )(1 − u

3 a

∗

4 )a

∗

4 − x

2 ) + (x

∗

2 x

1 − a

1 )(a

∗

1 − x

1 )

|(x ∗

4 )

∂f ∂u ^{4}

∗

3 u

∗

2 u

∗

1 u

∗

7 , u

∗

6 x

∗

5 x

4 x

4 )

∗

3 x

∗

2 x

∗

1 x

|(x ∗

∂f ∂u ^{2}

2 )

∗

4 )

∗

3 − u

∗

6 x

4 − u

4 )

∗

3 u

∗

2 u

∗

1 u

∗

7 , u

∗

∗

∗

5 x

∗

4 x

∗

3 x

∗

2 x

∗

1 x

|(x ∗

∂f ∂u ^{3}

3 )

1 − x

1 ) − a ^{5}

∗

11 )+(a 4 −a ∗

7 − x

11 ) + (x

2 a

1 − 983.68099a

5 )(−a

∗

5 − x

10

4 a

3 a

4 )a

2 a

7 )a

2 )(0.007212a

11 −(x 2 −x ∗

2 a

1 −7.0943a

4 − 0.007212a

3 a

−4 a

1

10 − a

3 a

3 a

∗

3 a

4 ) dx ^{5} dt

6 ) dx ^{7} dt

4 (5)

7 )a

∗

7 − x

9

6 )a

∗

6 − x

−4

9 − 0.39 × 10

= 0.0003196a

9 − a

4 dx ^{6} dt = 7.0943a

6 )(−a

∗

6 − x

11 ) + (x

2 a

2 )(0.007212a

∗

2 − x

6

9 − 0.003196a

11 − 0.003196a

2 a

= 0.00025a

∗

1.0185

8 +(x 4 −x ∗

8

7 a

3 a

6 )−1.9674×10

∗

6 −x

8

7 a

3 a

−1.9674×10

4 )

7 a

∗

3 a

3 −1.9674×10

8 )+x 3 −x ∗

983.6902 a ^{8}

7 1 −

11 ) + a

2 a

6 − 0.007212a

2 )(−a

∗

2 − x

7 −x

7 )

4 − x

3 )a

9 − (x

3 )a

∗

3 − x

4

9 − 0.00025a

4 dx ^{4} dt = 0.00568a

4 )a

∗

4 − x

9 − (x

∗

−1.9674×10

3 − x

6

9 − 0.00568a

= −0.00568a

8 )) dx ^{3} dt

7 a

3 a

7 (−1.9674×10

3 −1983.68099a

8

7 a

3 a

∗

3 u

∗

2 u

2 x

∗

1 x

|(x ∗

∂f ∂x ^{3}

3 )

∗

4 )

∗

3 u

∗

∗

3 x

1 u

∗

7 , u

∗

6 x

∗

5 x

∗

4 x

∗

3 x

∗

∗

2 x

∗

3 x

∗

2 x

∗

1 x

|(x ∗

∂f ∂x ^{4}

4 )

∗

4 − x

4 )

3 u

4 x

∗

2 u

∗

1 u

∗

7 , u

∗

6 x

∗

5 x

∗

∗

∗

4 x

∗

∗

1 − x

4 )

∗

3 u

∗

2 u

∗

1 u

∗

7 , u

6 x

∂f ∂x ^{1}

∗

5 x

∗

4 x

∗

3 x

∗

2 x

∗

1 x

F (x, u) = f (x ∗

1 )

|(x ∗

1 x

1 u

|(x ∗

∂f ∂x ^{2}

2 )

∗

2 − x

4 )

∗

3 u

∗

2 u

∗

∗

1 x

7 , u

∗

6 x

∗

5 x

∗

4 x

∗

3 x

∗

2 x

∗

∗

∗

2 u

∗

7 , u

∗

6 x

∗

5 x

∗

4 x

∗

3 x

∗

2 x

1 x

1 u

|(x ∗

∂f ∂x ^{7}

7 )

∗

7 − x

4 )

∗

3 u

∗

2 u

∗

∗

∗

∗

3 x

∗

1 u

∗

7 , u

∗

6 x

∗

5 x

∗

4 x

∗

∗

2 u

2 x

∗

1 x

|(x ∗

∂f ∂u ^{1}

1 )

∗

1 − u

4 )

∗

3 u

∗

1 u

7 , u

5 x

5 − x

4 x

∗

3 x

∗

2 x

∗

1 x

|(x ∗

∂f ∂x ^{5}

5 )

∗

4 )

5 x

∗

3 u

∗

2 u

∗

1 u

∗

7 , u

∗

6 x

∗

∗

∗

∗

|(x ∗

6 x

∗

5 x

∗

4 x

∗

3 x

∗

2 x

∗

1 x

∂f ∂x ^{6}

6 x

6 )

∗

4 )

∗

3 u

∗

2 u

∗

1 u

∗

7 , u

∗

The results of the system linearized equation (5) above, then solved using Pontryagin Minimum Principle with the help of MATLAB symbolic program, n order to obtain the general solution of the homogeneous and determined value symbolic constants with the help of the program. Final solution of this problem is obtained as follows: −0.0230t −0.2400t

˙x = −0.0322e − 0.2344e + 0.0040

1 −0.2564t 0.2564t 12 −0.0438t

˙x =

- 0.1766e + inf e − 1.0572 × 10 e

2 0.0438t 0.0138t 5 0.0138t e inf e + inf e − 3.1718 × 10 + 1.8817 ×

13 2.3764t

10 e + inf e − inf e − inf e 0.0138t 2.4123t

- −0.2400t −0.0230t 0.2564t

9 −0.2564t e inf e + 0.4699e − 3.3977 × 10 −

0.0438t −2.4123t 0.0438t 0.0138t inf e + inf e + inf e + inf e +

−0.0138t −0.2400t −0.0230t inf e + 58.6435e − 193.3093e −

13 2.3764t

4 e

1.8817 × 10 + 6.5185 × 10 −27 −0.0230t

˙x = 4.7033 × 10 e

3 −0.2400t −4 0.0230t e

˙x = 0.0037e + 4.7004 × 10

4 −9 2.4123t 0.0138t 0.2564t

˙x = 8.4627 × 10 e + inf e + inf e − 1.1103 ×

5 3 0.2564t 0.0438t −2.4123t −0.0438t e 10 + inf e + inf e + inf e −

− 0.2400t −0.0230t inf e 0.0138t + 0.0523e + 0.0014e + 3.3933 ×

5 2.3764t e 10 − 0.7324

0.0138t 0.2564t −8 2.4123t ˙x = inf e + inf e + 1.9988 × 10 e + 1.5792 ×

6 3 0.0438t 0.0438t

10 e −0.2564t − inf e − inf e − inf e −0.0138t −0.2400t −0.0230t inf e − 0.0034e + 0.0031e − 8.1245 ×

- −2.4123t

5 2.3764t 10 e − 0.1612

0.2564t 0.0138t 2.4123t 0.2564t ˙x = 9.5439e − inf e + 0.2760e + + inf e

7 0.0438t 0.0438t

−0.0138t inf e + inf e − inf e + 2.1493 ×

−5 −0.2400t −5 e e 10 − 3.5308 × 10 − 0.0230t − 1.1409 ×

3 2.3764t 10 e + 0.0022

˙u =

1 ˙u =

2 3 0.2564t 0.0138t 4 2.4123t

˙u = 1.0158×10

- ( inf e +inf e +1.1076×10 e

3 4 −0.2564t 0.0438t −2.4123t e

7.7844 × 10 − inf e − inf e − 1.5671 × −7 −0.0438t −0.0138t −0.2400t

10 e + inf e − 0.0015e + 2.5702 × 2.3764t

−5 −0.0230t 10 e + 348.9492e + 0.1105)

˙u 4 = Of the general solution can chart of state and control variables are presented in Figure 3 and Figure 4 as follows: Figure 3 shows that the proviral T h cells and productively infected T h cells (wild type) decreased. While Figure 4 shows the protease inhibitor, fussion inhibitor, and reverse trancription inhibitor therapy is static. This Figure 3: Analytical Solutions At state variables On Optimal Point Numer- ical Results Figure 4: Analytical Solutions In Control

Table 1: Initial Condition Variable State [11] Parameters Value Estimates x

5

HIV virus using the direct method is based on the transformation of the optimal control problem into a problem of Non Linear Programming (NLP) with the state equation and the equation discretization control using pseu- dospectral method. Problem solving optimal control the spread of the HIV virus is done by using propt-MATLAB Optimal Control Software is TOM- LAB/propt Optimization, which run on MATLAB software 7.10.0 (R2010a) is a toolbox that implements the method parameterization approach control variables that can be used to solve the NLP problem.

Numerical Simulation and Analysis of Results Completion of optimal control problems controlling the spread of the

0.1 a 1 1 0,6

1

9 0,003 a

8 1500 a

7 0,03 a

6 0,02 a

10 a

4 0,24 a

1 0.049 x

3 1200 a

2 0,000024 a

1 2,4 a

7 a

6 x

5 x

4 0,0042 x

3 0,034 x

2 904 x

To be able to perform simulations of optimal control problems control- ling the spread of the HIV virus by using TOMLAB/propt, required values of the parameters of the dynamic system models are presented in Table 1. into the program TOMLAB/propt thus obtained simulation. Simulation for optimal control problems spread of the HIV virus carried by the initial time t

− 0 = 0 and final time t f = 500 , the value of the control variable for therapy u , u , u , u ranges between 0 and 1 Simulations performed with

1

2

3

4 the initial value = u = u = u = 1 and the level of resistance mutants

1

2

3

4 a 11 = 0.6.

Figure 5. (i) indicates that the population of malignant viral particles decreases from the maximum point of 0.0253 to the minimum point 0 so achieve stability until day 500. Figure 5. (ii) shows that the number of healthy cells / uninfected Increased to 998.6436. Figure 5. (iii) indicates that the number of malignant virus-infected cell population plummeted to about 200 days, and then tended to be stable until day 500. Figure 5. (iv) shows that the number of malignant viruses and productive population also decreased to about 100 the first day, then tended to be stable until day 500. Figure 5. (v) shows that the population of mutant viral particles tended to decrease from the maximum point of 0.0228 towards 0.0055 minimum point. Figure 5. (vi) shows that the number of mutant virus-infected cell popula- tion experienced a steady increase until the first 100 days, then decreased

3 gradually until day 500 by 2.6 × 10 .

Figure 6. (i) indicates that the protease inhibitor therapy in the form of bang-bang (control function experienced a jump between 0 and 1) and fluctuated from 1 to the maximum point of the minimum point 0 and back again to the point of maximum 1. This indicates the power of therapy in suppressing and controlling cell growth of the HIV virus, thus toward equilibrium. While Figure 6. (ii), 6. (Iii) and 6 (iv) shows in a static state, meaning fussion inhibitor therapy, therapy riser healthy cells and reverse trancription inhibitor therapy fixed and unchanged. This indicates the power of therapy in controlling the growth of the HIV virus so that in a static state.

## 4. CONCLUSIONS

Simulation results Simulation analytic linearization equilibrium point is not defined on the results wild-type HIV, uninfected T cells , proviral h

T h cells (wild type), productively infected T h cells (wild type), mutant HIV strain, proviral T cells infected by the mutant strain and T cells produc- h h tively infected by the new strain. While the optimal linearization point numerical results also provide results not defined, except in proviral T h cells

2 h _{1} (ii) uninfected T cells (x ) (i) wild-type HIV (x ) _{3} (iv) Productively infected T h cells (wild (iii) Proviral T h cells (wild type) (x ) ^{4} type) (x )

Figure 5: The case of the HIV virus ppopulation with an initial value u =

1 u = u = u = 1 and the level of resistance mutans a = 0.6

2

3

4

11

(i) *Protease inhibitor *therapy (u 1 ) (ii) *Fussion inhibitor *therapy (u

2 )

(iii) T h cell enhancer therapy (u

3 )

(iv) *Reverse trancription inhibitor* therapy (u

4 )

Figure 6: Treatment of HIV with the initial value u

1 = u

2 = u

3 = u

4 = 1 and the level of resistance mutants a

11 = 0.6 Table 2: Comparison of Simulation Results Without Therapy and the Treat- ment

With Therapy No Parameters Analytic Numerical Linearized at Optimal point the equilibrium linearization on point numerical results

1 The final number of Not defined Not Defined wild-type HIV

2 The final number of un- Not Defined Not Defined 998.6436

infected T h cells −73 The final number of Not Defined Not Defined 5.9548 × 10 proviral T h cells (wild type)

−9

4 The final number of Not Defined Not Defined 8.253 × 10 h productively infected T cells (wild type)

5 The final number of mu- Not Defined Not Defined 0.0055

tant HIV strain

6 The final number of Not Defined Not Defined 0.0026

provial T h cells infected by the mutant strain−6

7 The final number of Not Defined Not Defined 3.2614 × 10 T h cells productively in- fected by the new strain of numerical simulations for controlling the spread of the HIV virus to the initial time t = 0 and the final time t = 500 successfully minimize mutant f

HIV strain (x ), proviral Th cells infected by the mutant strain (x ), and

5

6 T cells productively infected by the new strain (x ) as well as minimizing h

7 the cost of therapy is needed. The optimal solution indicates an increase in uninfected T h cells and a decrease in wild-type HIV, proviral T h cells (wild type), productively infected T h cells (wild type), mutant HIV strain, proviral T h cells infected by the mutant strain and T h cells productively in- fected by the new strain. These results showed a significant difference with the analytical solution. There are many potential differences in the results, including as a result of linearization at the equilibrium point and the con- dition number for the matrix involved in the analytic solution.