In 2015, Shijun Liao introduced a new method of directly defining the inverse mapping (MDDiM) to approximate analytically a nonlinear differential equation. This method, based on the Homotopy Analysis Method (HAM) was proposed to reduce the time it takes in solving a nonlinear equation. Very recently, Dewasurendra, Baxter and Vajravelu (Applied Mathematics and Computation 339 (2018) 758–767) extended the method to a system of two nonlinear differential equations. In this paper, we extend it further to obtain the solution to a system of three nonlinear differential equations describing the HIV infection of CD4+ T-cells. In addition, we analyzed the advantages of MDDiM over HAM, in obtaining the numerical results. From these results, we noticed that the infected CD4+ T-cell density increases with the number of virions N; but decreases with the blanket death rate _{I}

#### Keywords

- HIV infection model
- directly defining inverse mapping
- coupled nonlinear system
- analytical method
- homotopy analysis method
- series solution

#### MSC 2010

- 34A34
- 92B05
- 34K28

Mathematical models become an important tool in analyzing the dynamics of human immunodeficiency virus (HIV), hepatitis B virus (HBV), and hepatitis C virus (HCV) infection [1]–[2]. The HIV mainly targets a host's CD4+ T-cells, which are the most abundant white blood cells of the immune system. Further, HIV wreaks the most havoc on the CD4+ T-cells by causing their destruction and decline, and decreasing the body ability to fight infection.

Chronic HIV infection causes gradual depletion of the CD4+ T-cell pool. Hence, progressively compromises the host's immune response to opportunistic infections, leading to acquired immunodeficiency syndrome (AIDS). For this reason, the count of CD4+ T-cells is a primary indicator used to measure progression of HIV infection.

In 1989, Perelson et al. [3] developed a simple model for the infection between the human immune system and HIV. Perelson et al. [4] then extended further the model, and observed that the model exhibits many of the symptoms of AIDS seen clinically: The long latency period, low levels of free virus in the body, and the depletion of CD4+ T-cells. They defined the model by considering four separate variables: Uninfected cells, latently infected cells, actively infected cells, and the concentration of free virus particles. Then, described the dynamics of these populations by a system of four coupled nonlinear differential equations. This model was then simplified by Culshaw and Ruan [5] by assuming that all of the infected cells are capable of producing the virus. In other words, they combined the latently infected cells and the actively infected ones.

This simplification resulted in a new system of three differential equations, which we will approximate using the expanded form of MDDiM. The simplified model is as follows:

In this model, the variables T(t), I(t), and V(t) are the uninfected CD4+ T-cells, the infected CD4+ T-cells, and the concentration free HIV particles at time _{T}_{T}_{max}_{1} represents the rate of infection of T-cells with free virus, and is the source term for infected cells.
_{1} is a blanket death term for infected cells, _{b}_{V}

Variables and parameters for viral spread

Parameters and Variables | Meaning |
---|---|

Uninfected CD^{+} T-cell population size | |

Infected CD^{+} T-cell density | |

Initial density of HIV RNA | |

_{T} | Natural death rate of CD^{+} T-cell |

_{I} | Blanket death rate of infected CD^{+} T-cel |

_{b} | Lytic death rate for infected cells |

_{v} | Death rate of free virus |

_{1} | Rate CD^{+} T-cel become infected with virus |

Rate infected cells becomes active | |

Growth rate of CD^{+} T-cell population | |

Number of virions produced by infected CD^{+} T-cel | |

_{max} | Maximal population level of CD^{+} T-cel |

Source term for uninfected CD^{+} T-cel | |

_{0} | CD^{+} T-cel population for HIV-negative persons |

This particular model and its variations have been solved multiple times by different methods including the HAM by M. Goreishi et al. [6], Homotopy Perturbation Method [7] and the Laplace Adomian decomposition method (LADM) by Ongun [8]. In the present paper we solve the model by MDDiM. This is the first time we used MDDiM to solve a system of three nonlinear coupled ordinary differential equations arise in the field of mathematical biology, using a single inverse linear map to solve the three deformation equations (2.21)–(2.23).

In this section, we first discuss the space that solution and base functions come from, and then will introduce the Method of Directly Defining inverse Mapping Method (MDDiM). MDDiM is an extension to the Optimal Homotopy Analysis Method (OHAM). In OHAM we solve an infinite number of linear ordinary differential equations to obtain series solution but in MDDiM with the help of directly defined inverse map 𝒥 we solve systems of linear equations.

Let _{∞} = {1,^{2},...} be a complete set of base functions and define _{∞} as
^{*} = {1,_{∞} to define ^{*} because each equation in our coupled nonlinear system (1.1) has two initial conditions. Next, we define

Similarly, let _{R}_{0}(_{1}(_{R}

Now we define three nonlinear operators _{1},_{2},_{3} : _{1}(_{0}(_{2}(_{0}(_{3}(_{0}(_{1},_{2},_{3} are linear operators, _{0}(_{0}(_{0}(^{*} are initial guesses, _{0} is the convergence control parameter and

Assuming that the solutions to the zeroth-order deformation equations are analytic at _{1}(_{2}(_{3}(_{k}^{th}

Now applying ^{th}^{th}

The benefit of OHAM is that we have a considerable freedom to choose linear operators _{1}, _{2} and _{3} so that the corresponding form of equations (2.16)–(2.18) can be determined. Then the number of terms desired in _{k}_{k}_{k}

Now applying the inverse linear map to OHAM deformation equations (2.16)–(2.18). We obtain the following deformation equations in the frame of MDDiM

Here we use only one inverse linear operator to all three deformation equations which leads to less complicated solutions. However, a different inverse linear map could be used to obtain series solutions in different structures. Here we define the inverse linear map

The approximate series solution for the coupled nonlinear system (1.1)–(1.3) with boundary conditions (1.4) are obtained using MDDiM. Further, error analysis is carried out to get a general idea about how accurate the approximate solutions are.

First, define three term approximations for
^{2}-norm of residual error functions we define square residual error functions as
_{ξ}

Next, we optimize the total error function with respect to

Taking three sets of parametric values for _{I}_{0} = 1000, _{0} = 0, _{0} = 0.001, _{T}_{b}_{V}_{1} = 2.4 × 10^{−5},
_{max}

Solution curves for uninfected CD+ T-cell population size _{0} = 1000, _{0} = 0, _{0} = 0.001, _{T}_{I}_{b}_{V}_{1} = 2.4 × 10^{−5},
_{max}

In the present study, MDDiM has been developed and used to solve the model of HIV infection of CD4+ T-cells. Approximate analytical solutions for the uninfected CD4+T-cell population size, infected CD4+ T-cell density and initial density of HIV RNA were found. Our analytical solutions are in good agreement with the results of Ghoreishi et al. [4], and with the numerical results, we obtained by Runge-Kutta method (see Figures 5–13). From these results, we noticed that the infected CD4+ T-cell density increases with the number of virions N; but decreases with the blanket death rate _{I}

Since, inverse mapping is directly defined, approximate series solutions are obtained with less CPU time compare with OHAM solution. Also, it is investigated that selected inverse map leads converge series solutions with total error 10^{−10} (see the Table 2 and Figure 4).

Minimum of the squared residual error _{I}_{0} = 1000, _{0} = 0, _{0} = 0.001, _{T}_{b}_{V}_{1} = 2.4 × 10^{−5},
_{max}

_{I} | ||||
---|---|---|---|---|

0.26 | 500 | −0.4295 | 0.4869 | 4.068 × 10^{−10} |

0.26 | 600 | −0.4187 | 0.4731 | 5.682 × 10^{−10} |

0.1 | 500 | −0.4311 | 0.4852 | 4.235 × 10^{−10} |

Numerical comparison for

t | MDDiM | OHAM | Runge-Kutta |
---|---|---|---|

0.0 | 0 | 0 | 0 |

0.2 | 3.1568 × 10^{−6} | 1.7626 × 10^{−7} | 3.1318 × 10^{−6} |

0.4 | 5.1445 × 10^{−6} | 2.9451 × 10^{−7} | 5.1352 × 10^{−6} |

0.6 | 6.5569 × 10^{−6} | 3.7344 × 10^{−7} | 6.5894 × 10^{−6} |

0.8 | 7.7445 × 10^{−6} | 4.2602 × 10^{−7} | 7.8026 × 10^{−6} |

1.0 | 8.8153 × 10^{−6} | 4.6108 × 10^{−7} | 8.9418 × 10^{−6} |

Numerical comparison for

t | MDDiM | OHAM | Runge-Kutta |
---|---|---|---|

0.0 | 0.01 | 0.01 | 0.01 |

0.2 | 6.4618 × 10^{−4} | 6.1744 × 10^{−4} | 5.5042 × 10^{−4} |

0.4 | 4.7805 × 10^{−4} | 3.8431 × 10^{−4} | 4.8189 × 10^{−4} |

0.6 | 4.0834 × 10^{−4} | 2.4258 × 10^{−4} | 4.0993 × 10^{−4} |

0.8 | 3.8591 × 10^{−4} | 1.5659 × 10^{−4} | 3.9044 × 10^{−4} |

1.0 | 3.9576 × 10^{−4} | 1.0406 × 10^{−4} | 4.0056 × 10^{−4} |

In this study, we used a single inverse linear operator for all three deformation equations (2.21)–(2.23), but it is open to use two, or three inverse maps instead. Further, convergence of the series solutions may depend on the choice of inverse linear map and also with the defined solution space. So, it is worth to investigate properties of inverse linear maps in the frame of MDDiM.

To the best of our knowledge, this is the first time someone has used this method to solve HIV infection model. This novel method is more general and can be used to analyze non-linear systems of differential equations arising in science and engineering problems.

#### Minimum of the squared residual error E(h,A) for three different sets of μI and N for fixed parametric values T0 = 1000, I0 = 0, V0 = 0.001, r = 0.03, μT = 0.02, μb = 0.24, μV = 2.4, k1 = 2.4 × 10−5, k1'=2×10−5k.1^{'} = 2 \times 10^{-5} , s = 10, Tmax = 1500.

_{I} | ||||
---|---|---|---|---|

0.26 | 500 | −0.4295 | 0.4869 | 4.068 × 10^{−10} |

0.26 | 600 | −0.4187 | 0.4731 | 5.682 × 10^{−10} |

0.1 | 500 | −0.4311 | 0.4852 | 4.235 × 10^{−10} |

#### Numerical comparison for I(t)

t | MDDiM | OHAM | Runge-Kutta |
---|---|---|---|

0.0 | 0 | 0 | 0 |

0.2 | 3.1568 × 10^{−6} | 1.7626 × 10^{−7} | 3.1318 × 10^{−6} |

0.4 | 5.1445 × 10^{−6} | 2.9451 × 10^{−7} | 5.1352 × 10^{−6} |

0.6 | 6.5569 × 10^{−6} | 3.7344 × 10^{−7} | 6.5894 × 10^{−6} |

0.8 | 7.7445 × 10^{−6} | 4.2602 × 10^{−7} | 7.8026 × 10^{−6} |

1.0 | 8.8153 × 10^{−6} | 4.6108 × 10^{−7} | 8.9418 × 10^{−6} |

#### Numerical comparison for V (t)

t | MDDiM | OHAM | Runge-Kutta |
---|---|---|---|

0.0 | 0.01 | 0.01 | 0.01 |

0.2 | 6.4618 × 10^{−4} | 6.1744 × 10^{−4} | 5.5042 × 10^{−4} |

0.4 | 4.7805 × 10^{−4} | 3.8431 × 10^{−4} | 4.8189 × 10^{−4} |

0.6 | 4.0834 × 10^{−4} | 2.4258 × 10^{−4} | 4.0993 × 10^{−4} |

0.8 | 3.8591 × 10^{−4} | 1.5659 × 10^{−4} | 3.9044 × 10^{−4} |

1.0 | 3.9576 × 10^{−4} | 1.0406 × 10^{−4} | 4.0056 × 10^{−4} |

#### Variables and parameters for viral spread

Parameters and Variables | Meaning |
---|---|

Uninfected CD^{+} T-cell population size | |

Infected CD^{+} T-cell density | |

Initial density of HIV RNA | |

_{T} | Natural death rate of CD^{+} T-cell |

_{I} | Blanket death rate of infected CD^{+} T-cel |

_{b} | Lytic death rate for infected cells |

_{v} | Death rate of free virus |

_{1} | Rate CD^{+} T-cel become infected with virus |

Rate infected cells becomes active | |

Growth rate of CD^{+} T-cell population | |

Number of virions produced by infected CD^{+} T-cel | |

_{max} | Maximal population level of CD^{+} T-cel |

Source term for uninfected CD^{+} T-cel | |

_{0} | CD^{+} T-cel population for HIV-negative persons |

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