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Abstract and Applied Analysis
Volume 2014 (2014), Article ID 289349, 9 pages
http://dx.doi.org/10.1155/2014/289349
Research Article

Stability of a Mathematical Model of Malaria Transmission with Relapse

Department of Applied Mathematics, Lanzhou University of Technology, Lanzhou, Gansu 730050, China

Received 6 December 2013; Accepted 10 January 2014; Published 27 February 2014

Academic Editor: Weiming Wang

Copyright © 2014 Hai-Feng Huo and Guang-Ming Qiu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A more realistic mathematical model of malaria is introduced, in which we not only consider the recovered humans return to the susceptible class, but also consider the recovered humans return to the infectious class. The basic reproduction number is calculated by next generation matrix method. It is shown that the disease-free equilibrium is globally asymptotically stable if , and the system is uniformly persistence if . Some numerical simulations are also given to explain our analytical results. Our results show that to control and eradicate the malaria, it is very necessary for the government to decrease the relapse rate and increase the recovery rate.

1. Introduction

Malaria is caused by a parasite called Plasmodium, which is transmitted via the bites of infected mosquitoes. Approximately half of the world’s population is at risk of malaria. Most malaria cases and deaths occur in Sub-Saharan Africa. In 2011, 99 countries and territories had ongoing malaria transmission [1]. Recently, the incidence of malaria has been rising due to drug resistance. Various control strategies have been taken to reduce malaria transmissions.

Many epidemic models have been analyzed mathematically and applied to specific diseases [2, 3]. Since the first mathematical model of malaria transmission is introduced by Ross [4], quite a few mathematical models have been formulated to investigate the transmission dynamics of malaria [512]. Ngwa and Shu [5] analyze a deterministic differential equation model for endemic malaria involving variable human and mosquito populations. Ngwa [6] also analyzes a mathematical model for endemic malaria involving variable human and mosquito populations and uses a perturbation analysis to approximate the endemic equilibrium in the important case where the disease related death rate is nonzero, small but significant. Furthermore, in quasistationarity, the stochastic process undergoes oscillations about a mean population whose size can be approximated by the stable endemic deterministic equilibrium. Chitnis et al. [7, 8] study a model that both human and vector species follow a logistic population, and human have immigration and disease-induced death. They present a bifurcation analysis and analyze a periodically-forced difference equation model for malaria in mosquitoes that captures the effects of seasonality and allows the mosquitoes to feed on a heterogeneous population of hosts. Chamchod and Britton [9] incorporate a vector-bias term into a malaria transmission model to account for the greater attractiveness of infectious humans to mosquitoes in terms of differing probabilities that a mosquito arriving at a human depending on whether he is infectious or susceptible. To take account of the incubation periods of parasites within the human and the mosquito, a delayed Ross-Macdonald model is taken by Ruan et al. [10]. Further, Xiao and Zou [11] use mathematical models to explored a natural concern of possible epidemics caused by multiple species of malaria parasites in one region. They find that epidemics involving both species in a single region are possible. Li [12] provides a basic analysis for the stage-structured malaria model and shows that both the baseline and the stage-structured malaria models undergo backward bifurcations.

Recently, Li et al. [13] consider a fast and slow dynamics of malaria model with relapse, and analyse the global dynamics by using the geometric singular perturbation theory. They find that a treatment should be given to symptomatic patients completely and adequately rather than asymptomatic infection. On the other hand, for the asymptomatic patients, their results strongly suggest that to control and eradicate the malaria, it is very necessary for the government to control the relapse rate strictly. Nadjm and Behrens [14] state that relapse is when symptoms reappear after the parasites had been eliminated from blood but persist as dormant hypnozoites in liver cells. This commonly occurs between 8–24 weeks and is commonly seen with P. vivax and P. ovale infections. Other papers also consider the inluence of relapse in giving up smoking or quitting drinking, please see [15, 16] and references cited therein.

Chitnis et al. [7] assume that the recovered humans have some immunity to the disease and do not get clinically ill, but they still harbor low levels of parasite in their blood streams and can pass the infection to mosquitoes. After some period of time, they lose their immunity and return to the susceptible class. Unfortunately, they do not consider that the recovered humans will return to their infectious state because of incomplete treatment. Li et al. [13] consider the relapse but not that the recovered humans may return to the susceptible class.

Motivated by these works, in this paper, we propose a more realistic mathematical model of malaria, in which we assume that the recovered humans return to the susceptible class and relapse. The basic reproductive number is calculated and the persistence theory is used to analyze the uniformly persistence of the system.

The organization of this paper is as follows. In the next section, a mathematical model of malaria with relapse is formulated. In Section 3, the basic reproduction number and the stability of disease-free equilibria are investigated. The existence of endemic equilibrium and uniformly persistence are proved in Section 4, and some numerical simulations are given in Section 5. In the last section, we give some brief discussions.

2. The Model

2.1. System Description

In this section, we introduce a mathematical model of malaria with relapse. Because hosts might get repeatedly infected due to not acquiring complete immunity so the population is assumed to be described by the SIRS model. Mosquitoes are assumed not to recover from the parasites so the mosquito population can be described by the SI model. The total number of population at time is given by and . The structure of model is shown in Figure 1. The transfer diagram leads to the following system of ordinary differential equations: where , , , , , , and represent the number of susceptible humans, infectious humans, recovered humans, susceptible mosquitoes, infectious mosquitoes, the total size of the human population, and the total size of the mosquitoes population, respectively. is the natural birth and death rate of humans, is the natural birth and death rate of mosquitoes, is from an infectious mosquito to a susceptible human transmission rate in humans, and represent both infectious and recovered human to a susceptible mosquito transmission rate in mosquitoes, is treatment rate, is recovery rate (individuals from recovered class could back to susceptible class again because they had a very small amount of parasites, which would be cleared quickly by their own immune system), is relapse rate, and is the number of mosquitoes per individual. All the parameters can be found in Table 1. In the model, and are constant, so we introduce the new variables in terms of proportion as follows: with . Then the system (1) becomes

tab1
Table 1: The parameters description of malaria model.
289349.fig.001
Figure 1: Transfer diagram of the model (1).
2.2. Basic Properties
2.2.1. Invariant Region

Notice that from (1) we have Thus, the total human population and mosquitoes’ population are constant. Since the system (3) monitor human population, it is plausible to assume that all its state variables and parameters are nonnegative for all . Further, it can be shown that the region is positively-invariant. Thus, each solution of the system (3), with initial conditions in , remains there for . Therefore, the -limit sets of solutions of the system (3), are contained in . Furthermore, in , the usual existence, uniqueness, and continuation results hold for the system, so that the system (3), is well-posed mathematically and epidemiologically. So we consider dynamics of system (3) on the set in this paper.

2.2.2. Positivity of Solutions

For system (3), to ensure the solutions of the system with positive initial conditions remain positive for all , it is necessary to prove that all the state variables are nonnegative, so we have the following lemma.

Lemma 1. If , the solutions , , and of system (3) are positive for all .

Proof. Under the given initial conditions, it is easy to prove that the solutions of the system (3) are positive; if not, we assume a contradiction: that there exists a first time such that there exists a , such that there exists a , such that In the first case, we have which is a contradiction meaning that ,??.
In the second case, we have which is a contradiction meaning that , .
In the third case, we have which is a contradiction meaning that ,??. Thus, the solutions , , and of system (3) remain positive for all .

3. Analysis of the Model

The model (3) has one disease-free equilibrium and one endemic equilibrium .

3.1. Disease-Free Equilibrium and the Basic Reproduction Number

The model has a disease-free equilibrium given by In the following, the basic reproduction number of system (3) will be obtained by the next generation matrix method formulated in [17].

Let , then system (3) can be written as where The Jacobian matrices of and at the disease-free equilibrium are, respectively, The model reproduction number denoted by is thus given by . Here is associated with disease transmission by infected humans as well as the infection of susceptible humans by infected mosquitoes. Susceptible mosquitoes acquire malaria infection from infected humans in two ways, namely, by infected or recoveries. Susceptible humans acquire infection following effective contacts with infected mosquitoes.

3.2. Global Stability of

Theorem 2. For system (3), the disease-free equilibrium is locally asymptotically stable if .

Proof. The linearised system (3) at the disease-free equilibrium is given by Therefore, the characteristic equation is with , , and . We use the Routh-Hurwitz criterion [18] to prove that when , all roots of (17) have negative real part. From (17), we see that and For ease of notation, we introduce and??, so that Thus, for ,??. Lastly,??. Thus, for , all roots of (17) have negative real parts. The disease-free equilibrium point , is locally asymptotically stable if .

In the following, we prove that when , is globally asymptotically stable in .

Theorem 3. For system (3), the disease-free equilibrium is globally asymptotically stable if .

Proof. We introduce the following Lyapunov function [19, 20]: where ,??,?and?. It is easy to see that , , and are all positive. The derivative of is given by If , then . As we know that and , so we obtain . Furthermore, only if or . The maximum invariant set in is the singleton . By LaSalle’s Invariance Principle [21], is globally asymptotically stable in .

3.3. Endemic Equilibrium
3.3.1. Existence of the Endemic Equilibrium

Theorem 4. If , system (3) has a unique endemic equilibrium , where

Proof. It follows from system (3) that From the second equation of (23), we obtain Substituting into the third equation of (23), we have Then substituting (24) and (25) into first equation of (23), we get Hence, if , there is no positive root of (26), while if there is one positive root.

3.3.2. Uniform Persistence of the Disease

We using the persistence theory of dynamical system to show the uniform persistence of the disease when . Let be a closed positively invariant subset of , on which a continuous flow is defined. We denote the restriction to by and note that is in general not positively invariant. Let be the maximal invariant set of on . Suppose is a closed invariant set and there exists a cover of , where is a nonempty index set. , , and are pairwise disjoint closed invariant sets. Furthermore, we propose the following hypothesis and Lemma. All are isolated invariant sets of the flow . , is acyclic; that is, any finite subset of does not form a cycle. () Any compact subset of contains, at most, finitely many sets of [22].

Lemma 5 (see [22, Theorem??4.3]). Let be a closed positively invariant subset of on which a continuous flow is defined. Suppose there is a constant such that is point dissipative on and the assumption () holds. Then the flow is uniformly persistent, if and only if . For any , where , , and is interior of set .

By this lemma, we can show the uniform persistence of disease when , and similar to the proof of Theorem??2.3 in [13], we have the following.

Theorem 6. In system (3), assume that , and the disease is initially present, then the disease is uniformly persistent; that is, there is a constant such that , , and .

Proof. We set ; we will prove below that the conditions of Lemma 5 are satisfied. Clearly is isolated. Hence, the covering is simply , which is acyclic. Thus, the condition () holds. We also can obtain is point dissipative by Lemma 1. Now we show that ; suppose this is not true, then there exists a solution such that: . For any sufficiently small constant , there exists a positive constant such that , for all .
Noting that Therefore, if , as , then by a standard comparison argument and the nonnegativity, the solution of with initial data , converges to as well. Thus , where , is defined by Here, . The derivative of is given by Therefore, goes to either infinity or some positive number as , which is a contradiction to . Thus, we have . Then, we obtain , for some constant . By the second and third equations of (3) and the use of Lemma 1, we have , such that . Denote , , , . Then the proof of Theorem 6 is completed.

4. Numerical Simulation

To illustrate the analytical results obtained above, we give some simulations using the parameter values in Table 2. Numerical results are displayed in Figures 25. First, we choose , , and , numerical simulation gives , then the disease-free equilibrium is globally asymptotically stable (Figure 2). Second, we choose , , and , numerical simulation gives , the disease is uniformly persistent (Figure 3).

tab2
Table 2: The parameters values of malaria model.
fig2
Figure 2: , the disease-free equilibrium, , is globally asymptotically stable.
fig3
Figure 3: , the disease is uniformly persistent.
289349.fig.004
Figure 4: The relationship between and .
289349.fig.005
Figure 5: The relationship between and .

Finally, for showing the effect of relapse and recover rate to the basic reproduction number, we give the relation between and (Figure 4), and the relation between and (Figure 5) in the numerical simulation. From Figures 4 and 5, we know that is increasing with respect to the relapse rate, while it is decreasing with respect to the recovery rate.

5. Discussion

An ordinary differential equation for the transmission of malaria is formulated in this paper. The model exhibits two equilibria, that is, the disease-free equilibrium and endemic equilibrium. By constructing Lyapunov function and persistence theory of dynamical system, it is shown that if , then the disease-free equilibrium point is globally stable, and if , the disease is uniformly persistent. Some numerical simulations for in terms of relapse rate and recover rate are performed. is increasing with respect to the relapse rate while it is decreasing with respect to the recovery rate. Our results strongly suggest that to control and eradicate the malaria, it is very necessary for the government to decrease the relapse rate and increase the recovery rate.

Conflict of Interests

On behalf of all the authors, Hai-Feng Huo declares that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

This work was partially supported by the NNSF of China (10961018), the NSF of Gansu Province of China (1107RJZA088), the NSF for Distinguished Young Scholars of Gansu Province of China (1111RJDA003), the Special Fund for the Basic Requirements in the Research of University of Gansu Province of China, and the Development Program for Hong Liu Distinguished Young Scholars in Lanzhou University of Technology.

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