Exploring the Stochastic Host-Pathogen Tuberculosis Model with Adaptive Immune Response

In this literature, we probe a stochastic host-pathogen tuberculosis model with the adaptive immune response of four states of epidemiological classification: Mycobacterium tuberculosis, uninfected macrophages, infected macrophages, and immune response CD4+ T cells. 1is model is pertinent to the latent stage of tuberculosis infection and active tuberculosis-infected individuals. 1e stochastic host-pathogen tuberculosis model in pathology is constituted based on the environmental influence on theMycobacterium tuberculosis and macrophage population, elucidated by stochastic perturbations, and it is proportional to each state. We evince the existence and a unique global positive solution of a stochastic tuberculosis model. We attain sufficient conditions for the extinction of the tubercle bacillus. Moreover, we acquire the existence of the stationary distribution of the positive solutions by the Lyapunov function method. Eventually, numerical simulations validate analytical findings and the dynamics of the stochastic TB model.


Introduction
In the annals of history, there have been many threats such as natural calamity, diseases, and wild animals for the existence of humans; among these, diseases seem to be the main cause of death. In the previous centuries, tuberculosis posed a great threat to the survival of human beings. It is believed that it was the cause of death of more than a third population [1]. And block death in Europe is the standing testimony for these facts. Fortunately, due to the development of medical science, tuberculosis is completely curable but still persists.
Tuberculosis is traced back to 3400-2400 BC. It is as ancient as that of ancient countries such as Egypt, Greece, Rome, and India. e German Nobel laureate Robert Koch (1843-1910) discovered the tubercle bacillus (March 24, 1882) at the backdrop of one in seven who died in Europe due to mysterious disease.
Infection of the lungs bacteria (Mycobacterium tuberculosis (MTB)) leads to tuberculosis (TB). e bacterial infection may also spread into the brain, kidneys, spine, etc., but it is fortunate that tuberculosis is curable and preventable [1]. Tuberculosis is contagious and spreads through the air. Even the inhalation of a few of these germs is potential enough to infect a person. e physical contact, the nearness, and the air around the infected person can also infect. Sharing the same space with the infected person, weak immunity, malnutrition, unemployment, poor lifestyle, poor food habits, lack of shelter, unhygienic environment, drug abuse, imperfect sexual attitude, ill-literacy, and smoking are vulnerable factors for the spread of tuberculosis [2]. Infection is mainly occurred by the aerosol route like inhalation, wherein bacilli contained droplet nuclei sets in the alveoli. Here, alveolar macrophages [3] engulf the bacteria and ultimately bring out the infection to dendritic cells and all other cells as epithelial cells.
ese alveolar macrophages possess multiple microbicidal mechanisms which include phagolysosome fusion and respiratory burst to prevent postinfective mechanisms. For successful sustainability of infection, the tuberculosis bacilli must understand its encounter with the collection of activated macrophages (granuloma) [4,5] and eventually gain access to the lymphocytes, neutrophils, fibroblasts, eosinophils, or the bloodstream [6,7]. Macrophages with the potential to sterile and kill the bacteria are segmented apart in the tissue. In the case of adaptive immunity with the accumulation of effector CD4+ and CD8+ T cells setting in lungs, the multiplication of the bacterial population is contained and enumerable patients become asymptomatic and unable to shed bacteria, and they are then considered as vulnerable to latent TB infection (LTBI). Varied mechanisms cause the limited ability with adaptive immune responses to encounter and eradicate MTB, some of which are then categorized and characterized such as weakened histocompatibility complex (MHC) class II-mediated antigen presentation; adaptation of the anti-inflammatory mediator lipoxin A4; containing by regulatory T cells; monitoring bacterial antigen gene expression and hence incapable of reducing antigen-specific CD4+ T cells; and immune to the macrophages activating effector cytokines such as interferon-c (INF-c) and tumour necrosis factor-α (TNF-α) [4,8].
e latent stage of the tuberculosis infection is nonchallenged; though the germs are present in the body, they are inactive and harmless. Antibiotics such as isoniazid (INH), rifampin (Rifadin, Rimactane), and rifapentine can also be used to contain the aggravation of the germs at this stage. It is very easy to wipe out the germs from the body at this stage. Indications of active tuberculosis infection are coughing, sudden weight loss, loss of appetite, sweating during nights, fever, and chest pain. e potential roles for adjunctive immunotherapy in multidrug-resistant TB (MDR-TB) and extensively drugresistant TB (XDR-TB) [9][10][11] consist in enhancing cure rates and reducing time spent to cultural shifts, reducing the tissue damage concerned with nonproductive postimmune reactions, and elevating overall health status by subdoing systematic impacts of prolonged chronic inflammation. In addition, enumerable powerfully useful cytokines and other immunomodulatory agents already exist in medical use for other conditions. A significant role in the immunity against pathogen agent is played by T-cell differentiation and memory and effector T cells [12][13][14]. Hence, an effective T-cell response decides if the infection decreases or develops into a medically evident disease. Studies prove that CD4+ T cells [13][14][15] do their part in the protection against MTB as strengthened by the evidence that CD4+ T cells such as T helper 1 ( 1), 2, 17, and regulatory T cells (Tregs) and all these subsets support one another to restrict infection. MTB-specific CD4+ 1 cell response is recognized as possessing a protective role for the power to generate cytokines such as INF-c and TNF-α that involve the processes of recruitment and activation of innate immune cells as monocytes and granulocytes. In this way, when other antigen-specific T cells such as CD8+ cells [14], natural killer (NK) cells, cδT cells, nonclassical (MHC) class I molecule CD1, controlled T cells   are able to raise INF-c during MTB infection and they are  unable to equalize the lack of CD4+ T cells. Of late, studies have revealed that multifunctional/polyfunctional T cells (i.e., T cells possessed with multiple effector functions) [13,14] can produce IFN-c coupled with IL-2, and subsequently, a subset of cells is able to concurrently produce IFN-c, TNF-α, IL-2, and IL-17 and was diagnosed in patients with active TB disease compared to LTBI, which rapidly decreased on anti-TB treatment. And also, the polyfunctional T cells were recognized for their potentiality to proliferate and to secrete very many cytokines, and these cells were discovered to act a protective role in antiviral immunity in chronic infections (when the antigen load is low).
Memory T cells are a subset of infection-fighting T cells (T lymphocytes) that are highly proliferative and possess a strong immune response. Based on the role of L-selectin and CC-chemokine receptor 7, memory T cells can be segmented into CD62L hi CCR7 + and CD62L lo CCR7 − [12][13][14]. In the case of in vitro stimulation, humanCD62L hi CCR7 + memory CD4+T cells caused the production of IL-2 but hardly produce interferon-c, IL-4, or IL-5. But CD62L lo CCR7 − cells rapidly produced these effector cytokines but produced little IL-2. CCR7 + cells named as central memory (T CM ) cells [13,14] are potential enough to produce numerous IL-2 but low lying of effector cytokines and home to secondary lymphoid tissues. But CCR7 − cells termed as effector memory (T EM ) cells [14] are capable of producing high order of cytokines and home to peripheral tissues. Subsequently, there are various other subpopulations of memory T cells such as memory T stem cells (T SCM , multiple stem-like properties), T naive phenotype (T N ), and transitional memory T cells (T TM ) [13,14], very many of which were secreted in the peripheral blood of healthy individuals.
Regulatory T cells (T reg ) [16] are inevitable for monitoring peripheral tolerance, preventing self-generating immunity, and reducing chronic inflammatory disease. In fact, T reg have varied mechanisms within their powers, and one such mechanism is the suppression by inhibitory cytokines which include IL-10, transforming (TGF-β), and latest identified IL-35 which are prime mediators of T reg − cell function. CD4+CD25+FOXP3+ circulating T reg play a primary role in restricting immune responses by downregulating CD4+ or CD8+ cell functions. Hectic suppression by T reg is important in regulating immune responses against MTB.
In spite of the fact that the role of CD8 T cells [13] in TB is less clear than that of CD4 T cells, they are considered in general to impart optimal immunity and protection. CD8 T cells contain enumerable antimicrobial effector mechanisms that are less vital or cease to exist in CD4 1 and 17 T cells. CD8 T cells emit cytokines or cytotoxic molecules, which cause apoptosis of target cells. 1 CD4 T cells propel effector functions in macrophages that contain intracellular MTB, and their role has been monitored with protection. Besides, many studies have depicted that 17 cells, which can produce IL-17, are implicated in immune protection against MTB, mainly due to the effect of this cytokine in incurring and vibrating neutrophils.
17 cells have been implicated in the protection against TB at initial levels, for their power to employ monocytes and 1 lymphocytes to the space of granuloma formation [5]. Contrarily, very many studies have manifested that unrestricted 17 stimulation confirms an exaggerated inflammation catalyzed by neutrophils and inflammatory monocytes that rush to the place of disease during the damage of tissue.
MTB-specific CD4+ T-cell protective response is simply due to 1 cells and is negotiated by IFN-c and TNF-α that employ monocytes and granulocytes and enhance their antimicrobial attitudes. Subsequently, higher part of antigenspecific effector memory cells and diminished frequency of central memory CD4+ Tcells have been discovered in patients with active TB in comparison with distribution witness in LTBI individuals. All the information converges on the increase of this complicated disease with the aim of improving diagnosis, prognosis, drug therapy, and vaccination.
Zhang [17] acclaimed the following host tuberculosis model: e biological meaning of all positive parameters and symbols in the deterministic TB model (1) are listed in Table 1. According to the theory in [17], the deterministic TB model (1) has the following properties: e positive invariant set Γ is given by (2) e disease-free equilibrium is given by e tuberculosis model (1) has a unique positive infected equilibrium E 1 if and only if R 0 > 1 and it is globally asymptotically stable when it exists. e positive infected equilibrium and B 1 is determined by In reality, the environmental noise perverts through and medal with the population dynamics, ecology, environmental sciences, and mathematical biology. Stochasticity has its impact upon various biological [18][19][20][21][22][23][24][25] and other models [26][27][28][29][30]. Many researchers probed stochastic tuberculosis models in which the total host population can be divided into three, four, and five epidemiological classes, respectively, such as susceptible (S), exposed (E), and infected (I) [31]; susceptible (S), latent (L), infectious (I), and treated (T) [32]; susceptible (S), exposed (E), infected (I), and recovered (R) [33]; and susceptible (S), vaccinated (V), infected with TB in latent stage (L), infected with TB in active stage (I), and treated individuals infected with TB (T) [34,35]. On the other hand, Khalid Hattaf et al. [36,37] investigated specific functional response and temporary immunity and delayed viral infection models with adaptive immune response like cytotoxic T lymphocyte (CTL) cells which is in MHC class I molecules. In all the above models such as SEI, SLIT, SEIR, SVLIT, the researchers probed on host population, temporary immunity, and CTL cells. However, the authors have not examined the functions of macrophages and immune response CD4+ T cells which are in MHC class II molecules so far. e macrophages inundate the MTB and quarantine them in a cellular compartment where the bacteria are killed or cannot thrive. If the number of MTB is higher than that of macrophages, it will result in the death of the macrophages in the granuloma. en the granuloma's core liquidizes and is transmitted through airways to other people. During infection, the MTB cells set sporadically in the alveoli of an individual. e immunologic effects of both infected and uninfected macrophages vary randomly due to environmental sources of MTB. e adaptive immune responses in an individual start with very few number of cells and later vary greatly among individual immune systems. is variation is random, and on the other hand, individuals with smoking habit, weak immunity, malnutrition, etc., possess low immune response and have to be treated with more dosages of antigens to increase the immunity contingently.
is study is challenging and useful in the sense that TB in its various dimensions and nature can be disclosed. Based on the above factors, we put forth the stochastic hostpathogen TB model with adaptive immune response. e stochastic host-pathogen TB model on the basis of the influence of the environment on the Mycobacterium tuberculosis and macrophage population was manifested by stochastic perturbations and it is proportional to each state [38,39].
We establish the following stochastic host-pathogen TB model: ) be a 4-dimensional Wiener process defined on the given probability space. e components of W(t) are supposed to be mutually independent. In the stochastic model (5), the nonnegative constants σ 1 , σ 2 , σ 3 , and σ 4 denote the intensities of the environmental white noise. e intention of this literature is structured as follows. In Section 2, the existence of the global and positive solutions to the stochastic host-pathogen TB model (5) is examined. In Section 3, sufficient conditions for the extinction of the tubercle bacillus are probed. In Section 4, we probe that there is a unique ergodic stationary distribution of the positive solutions of the stochastic TB model (5) under some conditions. In Section 5, we demonstrate some numerical simulations that validate our analytical findings. Eventually, Section 6 provides conclusions and future directions.

Global Positive Solutions of the Stochastic TB Model
Since B, M u , M i , and T in the stochastic TB system (5) denotes the population of MTB, uninfected macrophages, infected macrophages, and immune response CD4+ T cells, respectively, they should be nonnegative. For a stochastic differential equation to have a unique global (i.e., no explosion in a finite time) solution for any given initial value, the coefficients of the equation are eventually required to satisfy the linear growth condition and local Lipschitz condition. Yet, the coefficients of equation (5) do not satisfy the linear growth condition; nevertheless, they are locally Lipschitz continuous, and thus, the solution of equation (5) may explode at a finite time. Hence, for further studies, we should set some preconditions under which stochastic system (5) has unique global positive solutions.

Theorem 1. For any given initial value
of the stochastic TB model (5) on t ≥ 0 and the solution will remain in R 4 + with probability one.
Proof. It is clear that the stochastic TB system (5) satisfies the local Lipschitz condition for any given initial value where τ * is the explosion time [43]. To exhibit that the solution is universal, i.e., inevitable to affirm that For each integer κ ≥ κ 0 , define the stopping time [40]: with the typical format infϕ � ∞, where ϕ reflects the empty set. Evidently, τ κ is increasing as κ ⟶ ∞. We have s. for all t ≥ 0. Presume that τ ∞ < ∞, then there is a pair of constants T > 0 and ε ∈ (0, 1) such that us, there exists an integer κ 1 ≥ κ 0 such that where φ 1 > 0 and φ 2 > 0 will be determined later.
By Itô's formula to V, we obtain Choose

and then we have
where Λ is a positive constant. us, Integrate both sides of equation (13) 6 Mathematical Problems in Engineering Taking expectations on both sides of (14) yields Let Ω κ � τ κ ≤ T for all κ ≥ κ 1 , and by (8), then P(Ω κ ) ≥ ε. Notice that for every ω ∈ Ω κ , there exists at least erefore, en, we attain Mathematical Problems in Engineering It is a contradiction, and hence, we obtain τ ∞ � ∞ a.s., which completes the proof of eorem 1. □ Remark 1. Considering the region R 4 + is positively invariant, the unique solution of the stochastic TB model (5) exists for any given initial value in R 4 + . Hence, it is sufficient to probe its dynamics of the host-pathogen stochastic TB model in the region R 4 + .

Remark 2.
As stated in the introduction, immunotherapy with LTBI and active TB individuals and adaptive immune responses are known to be quite effective against MTB. However, the prevailing opinion among public health and medical practitioners in the entire world, prior to 1944 [44], was a less effective treatment for TB patients. During the years between 2016 and 2035, the efforts at the global, regional, and national levels to ease and eradicate the burden of TB disease have the colossus objective "ceasing TB epidemic," within the purview of the UN's Agenda for Sustainable Development and based on the WHO's End TB Strategy [1]. Hence, it is fruitful to study the eradication of the MTB among LTBI and active TB individuals. is is done as follows.

Extinction of the MTB
In this section, the extinction of the MTB is discussed. e death rate of uninfected macrophages and the intensities of the white noise in the stochastic system (5) are the consequences of the random fluctuations. e population dynamics show direct results on the qualitative outcome of the MTB dynamics in the system with regard to the factors that decide MTB eradication during the long course of time. Firstly, we shall present a lemma which will be used in our analysis.
Define a parameter as follows: Theorem 2. For any given initial value (B(0), M u (0), M i (0), T(0)) ∈ R 4 + , then the MTB of the stochastic TB system Proof. Consider the second and third equations of the stochastic TB system (5). Integrating these equations from 0 to t and dividing both sides by t, we get It follows that By Lemma 1, we have On the other hand, let Applying Itô's formula, we obtain where N 1 � (η 1 − 1) > 0 and N 2 � (η 2 − 1) > 0.
Integrating both sides of (28) from 0 to t, we attain Dividing both sides of by t and then taking the limit superior yield Mathematical Problems in Engineering 9 e conclusion is confirmed. □ Remark 3. e immunomodulatory agents are compatible to immune responses such as IL-2 and IL-4 to effectively active T helper cells. Immunosuppressive agents such as TNF-α moderate dangerous inflammation and cytokine therapy INF-c tends to accelerate the mycobacterial activity of effector immune cells [42]. All these facts exist in reality, which wipe out the MTB.

Remark 4.
Patients have lack of information, lack of money for treatment, side effects, lack of commitment to a long course, social barriers, irregular treatment and do not take proper drugs and so they have a low level of immunity. ese are the factors for the TB persistent and prevalence among population.

Ergodic Stationary Distribution
When considering epidemic dynamical systems, we are interested in when the disease will persist and prevail in a population. In this section, we present some theories about the stationary distribution (see Has'minskii [43]), and we show that there exists an ergodic stationary distribution, which reveals that the disease will persist. Let X(t) be a homogeneous Markov process in R 4 + , which is described by the following stochastic differential equation: e diffusion matrix is defined as follows: Lemma 2 (see [39]). e Markov process X(t) has a unique ergodic stationary distribution π(.) if there exists a bounded domain D ⊂ R 4 + with regular boundary Γ, having the following properties: for all x ∈ R 4 + , where f(.) is a function integrable with respect to the measure π.
e diffusion matrix of the stochastic TB system (5) is given by where
Next, we tend to prove the condition A 2 . Define where k 1 and k 2 are positive constants to be resolved later. By applying Itô's formula to V 1 , we get Choose en Define

Mathematical Problems in Engineering
where k 3 , k 4 , and k 5 are positive constants to be determined later. By calculating, Choose Hence, Define where Define a C 2 -function U: R 4 ⟶ R, as follows: where

Mathematical Problems in Engineering 13
where (50) Easily we can check that By applying Itô's formula, we obtain where It follows that Now we are in the portion to construct a compact subset D ϵ such that condition A 2 in Lemma 2 holds. Define the following bounded closed set where 0 < ϵ < 1 is a sufficiently small constant. In the set R 4 + ∖D ϵ , we can choose ϵ sufficiently small such that the following conditions hold:

Mathematical Problems in Engineering
where E, G, H, J, N, and O are positive constants which can be found from the equations (50), (66), (70), (72), (74), and (76). Conveniently, we can divide R 4 + ∖D ϵ into the following eight domains: ϵ , which is equivalent to proving it on the above eight domains, respectively. where 2 .

Numerical Simulations
In this section, we probe the extinction of the MTB, ergodic stationary distribution of the stochastic system (5), and the effect of varied environmental noise on model dynamic behavior based on the real-life parameters of theoretical outcomes by numerical simulations. Here, we utilize Milstein's higher-order method [44] and the host-pathogen stochastic TB model can be written as the subsequent discretization equations: where ϑ k , ψ k , ρ k , and ] k , k � 1, 2, . . .  Table 2.
3. By calculating, and 25 � μ M > ((σ 2 1 ∨ σ 2 3 )/2) � min(0.08, 0.045) � 0.045, the conditions of eorem 2 are satisfied. Figure 2 exhibits that the population of MTB and population of infected macrophages (LTBI or active TB) of the stochastic TB system (5) to eradicate the tuberculosis bacilli germs are almost surely from the infected TB individuals at this stage. On the other hand, increasing the infected macrophage killing rate b from 1.1 × 10 −4 to 1.25, increasing bacterium growth rate B, increasing the death rate of activated CD4+ T cells, and decreasing the natural recruitment rate of activated CD4+ T cells, etc., are vulnerable factors for tubercle bacillus which can grow from latency TB infection to oscillation and then reach as to active TB infection.

Conclusions and Future Directions
MTB is a leading communicable opportunistic disease in many countries and the main cause of chronic lung disease. In this literature, we studied the dynamical behavior of a stochastic host-pathogen tuberculosis model with adaptive immune response. Firstly, we exhibited that the stochastic TB model (5) has certain unique global positive solutions. We established sufficient conditions for the extinction of the tubercle bacillus. Also, we attained sufficient conditions for the existence of a unique ergodic stationary distribution of the positive solutions to the stochastic TB model (5) by applying a suitable Lyapunov function. Finally, the numerical simulations are provided to validate analytical outcomes and the dynamics of the stochastic host-pathogen TB model.
Certain interesting topics and practical realism earn further consideration. On the flip side, one may suggest some more realistic but complex models, such as considering the effects of impulsive perturbations on the stochastic TB system (5). e motivation for impulsive perturbations is that discontinuity is a common phenomenon and the real-life scenarios are generally discontinuous fashion like time and space. During the anti-TB treatment, pharmacokinetic variation and association of several drugs in a regimen are naturally nonlinear and occur only within some limited precise data spaces and often in a discontinuous manner [45]. Additionally, in the summer the incidence of TB is high, since it is speculated that the disease may have been obtained during the winter season. Due to air pollution, carbon monoxide stimulates bacillary reactivation and other factors such as poor water quality, soil pollution, and inadequate sanitation, which increase the risk of TB outbreaks. Owing to these sudden environmental changes, it is arousing curiosity to introduce the colored noise, for instance, continuous-time Markov chain into the stochastic TB system (5). Often, the switching among distinct environments is memoryless and the waiting time for the next switch is exponentially distributed [46,47].
Finally, the memory CD8+ T cells can swiftly obtain effector functions to destroy infected cells and/or secrete inflammatory cytokines such as IL-2, IL-4, and IL-5 prevent replication of the host-pathogen [14]. e predominant bestow factor is that the exhibition of MHC-I molecules is nearly omnipresent, whereas MHC-II molecules are revealed on a more limited set of cells. In this connection, the memory CD8+ T cells have more opportunities to encounter antigen [14]. Hence, in the stochastic TB model (5), another compartment has to be added like immune response CD8+ T cells which may be more effective and efficient to eradicate the tuberculosis bacilli. We leave these explorations for our future work.

Data Availability
No data were used to support this study.

Conflicts of Interest
e authors declare that they have no conflicts of interest.