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Modeling codynamics of pneumonia and meningitis diseases
Advances in Difference Equations volumeÂ 2019, ArticleÂ number:Â 149 (2019)
Abstract
In this paper an SIR deterministic mathematical model for coinfection of pneumonia and meningitis is proposed. We use a seven compartment model by using ordinary differential equations. The positivity of future solution of the model, the invariant region, and diseasefree as well as endemic equilibrium are studied. To study the stability of the equilibria, a basic reproduction number is obtained by using the next generation matrix. The robustness of the model is also investigated. To identify the effect of each parameter on the expansion or control of the diseases, sensitivity analysis is performed. The effects of treating pneumonia infected only, meningitis infected only, and coinfected individuals have been identified by using the numerical simulation.
1 Introduction
Pneumonia, which can be categorized as one of airborne diseases, claims for the death of millions of human beings through inhaling pathogenic organism, mainly Streptococcus pneumoniae [1]. These bacteria are also responsible for the cause of other diseases such as meningitis, ear infections, and sinus infections. The diseases can affect all stages of human beings, from children to elderly people, and pneumonia becomes dangerous when the immunity level is lowered, and also in children or elderly people, as well as when it is coinfected with other diseases like meningitis [2]. Meningitis, which is an infection of the covering around the brain and spinal cord, can be both bacterial and viral. Bacterial infection of meningitis is the most common one; particularly, Streptococcus pneumoniae, haemophilus influenza, and Neisseria meningitidis are responsible for 80% of meningitis cases [3]. A lot of studies have been conducted to indicate controlling mechanisms of infectious diseases in a community, some of them are [4,5,6]. Mathematical modeling has a great role in describing the dynamics of infectious diseases in a community. Several scholars have developed different models to study the dynamics of different epidemic diseases. Some of them [7, 8] proposed a mathematical model of pneumonia and meningitis; moreover, [9] investigated the codynamics of pneumonia and typhoid fever diseases with optimal control and costeffectiveness analysis. The result of the study revealed that prevention of typhoid fever and treatment of pneumonia is the most costeffective strategies from the proposed ones. Different authors have studied coinfection of various diseases: [10] studied coinfection of HIV and pneumonia, [11] studied coinfection of pneumonia and malaria. However, to the best of our knowledge, no one has investigated coinfection of pneumonia and meningitis. Therefore, in this paper we are interested in filling this gap.
2 Description of the model
In this model we consider heterogeneous population. In this model we consider deterministic seven compartmental human population. The total population is divided into seven subclasses, which are susceptible population \((S)\), pneumonia infectious \((I_{\mathrm{p}})\), meningitis infectious \((I_{\mathrm{m}})\), pneumonia and meningitis coinfectious \((I_{\mathrm{pm}})\), pneumonia recovered \((R_{\mathrm{p}})\), meningitis recovered \((R_{\mathrm{m}})\), and pneumoniaâ€“meningitis coinfectious recovered \((R_{\mathrm{pm}})\). Susceptible are recruited with rate of Ï€ through birth or immigration, and their number increases from individuals that come from subclasses of pneumonia recovered, meningitis recovered, and coinfectious recovered by losing their temporary immunity with rate of \(\delta _{1}\), \(\delta _{2}\), and \(\delta _{3}\), respectively. In the entire susceptible population, individuals can get pneumonia with contact rate of a from a pneumonia only infected or coinfected person with force of infection of pneumonia \(f_{1} =\frac{a(I_{\mathrm{p}}+I_{\mathrm{pm}})}{N} \) and join \(I_{\mathrm{p}}\) compartment. In a similar way, individuals can get meningitis by contact rate of b from a meningitis only infected or coinfected person with force of infection of meningitis \(f_{2} = \frac{b(I_{\mathrm{m}}+I_{\mathrm{pm}})}{N}\) and join \(I_{\mathrm{m}}\) compartment. Pneumonia only infected individuals also can get an additional meningitis infection with force of infection \(f_{2}\) and join coinfected compartment \((I_{\mathrm{pm}})\). The coinfected compartment increases because of individuals that come from meningitis only infected compartment when they are infected by pneumonia with \(f_{1}\) force of infection. Pneumonia only infected individuals can recover with rate of \(\sigma _{1}\) and join pneumonia only recovered compartment \((I_{\mathrm{p}})\). In a similar way, meningitis only infected individuals also recover with rate of \(\sigma _{2}\) and join meningitis only recovered compartment \((I_{\mathrm{pm}})\). The coinfected compartment also recovers with Ïƒ rate, but those individuals either recover only from pneumonia disease and join pneumonia only recovered compartment with probability of \(\sigma (1  e)\), or recover only from meningitis only disease and join meningitis only recovered compartment with probability of \(\sigma g(1  e)\), or recover from both diseases and join coinfected recover compartment with probability of \(\sigma (1g)(1e)\). In all compartments there is a natural death rate, which is Î¼. Moreover, \(\alpha _{1}\) is pneumonia only caused death rate, and \(\alpha _{2}\) is meningitis caused death rate. The above description of the model is plotted in Fig.Â 1. From the flow graph (Fig.Â 1) of the model, we get the following system of differential:
3 Qualitative analysis
In this section we study the qualitative behavior of the model. For simplification of the work, we split the full model into submodels, which are pneumonia and meningitis only models. The qualitative behavior of the submodel is studied first and that of the full model then follows.
3.1 Pneumonia only model
To get this model from equation (1), we set \(I_{\mathrm{m}}=I_{\mathrm{pm}}=R _{\mathrm{m}}=R_{\mathrm{pm}}=0\), \(\delta _{2}=\delta _{3}= f_{2}=0\), and then we get
3.1.1 Invariant region
To get an invariant region, which shows as the solution is bounded, the total population of the model is \(N=S+I_{\mathrm{p}} +R_{\mathrm{p}}\). By differentiating N both sides and substituting respective expressions of \(\frac{dS}{dt}\), \(\frac{dI_{\mathrm{p}}}{dt}\), and \(\frac{dR_{\mathrm{p}}}{dt}\) from equation (2), we get
If there is no death from pneumonia, equation (3) becomes
Solving equation (4) gives
Therefore, all the solution set of (2) is bounded in Î©.
3.1.2 Positivity of the solution
To show solutions of the model, as it is positive, first we take \(\frac{dS}{dt}\) from equation (2).
We rewrite equation (5) as
After evaluating equation (6), we obtain
Similarly, we obtain
Therefore, the solution of the model is positive for future time.
3.1.3 Diseasefree equilibrium (DFE)
By equating equation (2) to zero and substituting \(I_{\mathrm{p}}=0\), we obtain the diseasefree equilibrium (DFE), \(E_{0p}=(\frac{\pi }{ \mu },0 ,0)\).
3.1.4 Basic reproduction number (\(\Re _{0}p\))
The basic reproduction number is an average number of secondary case infections by a single infected person in total susceptible population. To obtain \(\Re _{0p}\), we use the next generation matrix method that was formulated by [12], and we get
3.1.5 Local stability of DFE
Theorem 1
The DFE is locally asymptotically stable if \(\Re _{0p}<1\) and unstable if \(\Re _{0p}>1\).
Proof
To prove TheoremÂ 1, first we take the second equation of equation (2) and consider the righthand side \(f=f_{1} S (\sigma _{1} + \alpha _{1} +\mu )I_{\mathrm{p}}\). Then the partial derivative of f with respect to \(I_{\mathrm{p}}\) at diseasefree equilibrium is
For the diseasefree equilibrium to be stable,
Therefore, the diseasefree equilibrium is locally asymptomatically stable if \(\Re _{0p} <1\) and unstable otherwise.â€ƒâ–¡
3.1.6 Global stability of DFE
Theorem 2
The DFE is globally asymptotically stable if \(\Re _{0p}< 1\).
Proof
To prove the global asymptotic stability of the DFE, we use the method of Lyapunov functions.
Systematically, we define a Lyapunov function L such that
Then
So \(\frac{dL}{dt}\leq 0\) if \(\Re _{0p}\leq 1\). Furthermore, \(\frac{dL}{dt}=0\) if \(I_{\mathrm{p}}=0\) or \(\Re _{0p}=1\).
From this we see that \((S_{0},0,0)\) is the only singleton in \(\lbrace (S,I_{\mathrm{p}},R_{\mathrm{p}})\in \varOmega _{1}:\frac{dL}{dt}=0\rbrace \).
Therefore, by the principle of [12], DFE is globally asymptotically stable if \(\Re _{0p}\leq 1\).â€ƒâ–¡
3.1.7 Endemic equilibrium (EE)
The endemic equilibrium is denoted by \(E_{\mathrm{p}}^{*}=(S^{*} ,I_{\mathrm{p}}^{*} , R _{\mathrm{p}}^{*} )\) and it occurs when the disease persists in the community. To obtain it, we equate all the model equations (2) to zero. Then we obtain
From this we see that for the endemic equilibrium to exist, \(\Re _{0p} >1\).
3.1.8 Global stability of EE
Theorem 3
If \(\Re _{0p}>1\), the EE (\(E^{*}\)) of model (2) is globally asymptotically stable.
Proof
Systematically, we define an appropriate Lyapunov function L such that
Then differentiating equation (8) with respect to t gives
After substituting expressions for \(\frac{dS}{dt}\), \(\frac{dI_{\mathrm{p}}}{dt}\), and \(\frac{dR_{\mathrm{p}}}{dt}\) from (2) into (8) and collecting all positive terms together and also negative terms together, we obtain
where \(L_{1}=\pi +\delta _{1}R_{\mathrm{p}} +f_{1} S +\sigma _{1} I_{\mathrm{p}}+\frac{ \delta _{1}R_{\mathrm{p}}^{*} S^{*}}{S}+\frac{f_{1} S^{*} I_{\mathrm{p}}^{*}}{I_{\mathrm{p}}}+\frac{ \sigma _{1} I_{\mathrm{p}}^{*} R_{\mathrm{p}}^{*}}{R_{\mathrm{p}}}\), \(L_{2}= \delta _{1} R_{\mathrm{p}}^{*} +f_{1} S^{*} +f_{1} S I_{\mathrm{p}}^{*} +\frac{ \pi S^{*}}{S}+\frac{\delta _{1}R_{\mathrm{p}}S^{*}}{S} +\frac{(f_{1}+\mu )(SS^{*})^{2}}{S}+\frac{f_{1} SI_{\mathrm{p}}^{*}}{I_{\mathrm{p}}}+\frac{( \sigma _{1}+\alpha _{1}+\mu )(I_{\mathrm{p}}I_{\mathrm{p}}^{*})^{2}}{I_{\mathrm{p}}}+\frac{\sigma _{1} I_{\mathrm{p}} R_{\mathrm{p}}^{*}}{R_{\mathrm{p}}}+\frac{(\delta _{1}+\mu )(R_{\mathrm{p}}R_{\mathrm{p}}^{*})^{2}}{R _{\mathrm{p}}}\).
Thus if \(L_{1}< L_{2}\), then \(\frac{dL}{dt}\leq 0\) and \(\frac{dL}{dt}=0\) if and only if \(S=S^{*}\), \(I_{\mathrm{p}} = I_{P}^{*}\), \(R_{P}= R _{\mathrm{p}}^{*} \).
From this, we see that \(E^{*}=(S^{*}, I_{P}^{*},R_{\mathrm{p}}^{*}) \) is the largest compact invariant singleton set in \(\lbrace ( S^{*}, I _{P}^{*},R_{\mathrm{p}}^{*}) ) \in \varOmega _{1} : \frac{dL}{dt}=0 \rbrace \). Therefore, by the principle of [13], the endemic equilibrium (\(E ^{*} \)) is globally asymptotically stable in the invariant region if \(L_{1}< L_{2}\).â€ƒâ–¡
3.2 Meningitis only model
By letting \(I_{P}=I_{\mathrm{pm}}=R_{\mathrm{p}}=R_{\mathrm{pm}}=0 \) in equation (1), we obtain the meningitis only model.
3.2.1 Invariant region
In this section we obtain a region in which the solution of (10) is bounded. For this model, the total human population \(N_{2}=S+I_{\mathrm{m}} +R_{\mathrm{m}}\). Then, after differentiating \(N_{2}\) with respect to time and substituting the expression for \(\frac{dS}{dt}\), \(\frac{dI_{\mathrm{m}}}{dt}\), and \(\frac{dR_{\mathrm{m}}}{dt}\) from equation (10), we obtain
If there is no death from meningitis, equation (11) becomes
After solving equation (12) and evaluating it as time tends to infinity, we get
Therefore, all the solution set of (10) is bounded in \(\varOmega _{2}\).
3.2.2 Positivity of the solution
In this section we show that all the solutions of model (10) are positive for future time.
We take \(\frac{dS}{dt}\) from equation (10),
It is true that equation (13) can be written as
After solving equation (14), we get
Similarly, we obtain
3.2.3 Diseasefree equilibrium (DFE)
By letting equation (10) to zero and evaluating at \(I_{\mathrm{m}}=0\), we get DFE \(E_{02}=(\frac{\pi }{\mu },0 ,0,0)\).
3.2.4 Basic reproduction number (\(\Re _{0m}\))
The basic reproduction number is an average number of secondary case infections by a single infected person in total susceptible population. To obtain \(\Re _{0m}\), we use the next generation matrix method that was formulated by [12]. Then we get
3.2.5 Local stability of DFE
Theorem 4
The DFE point is locally asymptotically stable if \(\Re _{0m}<1\) and unstable if \(\Re _{0m}>1\).
Proof
To prove this, let us take the righthand side expression of the second equation of (10) \(f=f_{2} S (\sigma _{2} +\alpha _{2} +\mu )I_{\mathrm{m}}\). Then the partial derivative of f with respect to \(I_{\mathrm{m}}\) at the diseasefree equilibrium is
For the diseasefree equilibrium to be stable,
Therefore, the diseasefree equilibrium is locally asymptomatically stable if \(\Re _{0m} <1\) and unstable otherwise.â€ƒâ–¡
3.2.6 Global stability of DFE
Theorem 5
The DFE is globally asymptotically stable if \(\Re _{0m}< 1\).
Proof
To prove the global asymptotic stability of the DFE, we use the method of Lyapunov functions.
Systematically, we define a Lyapunov function L such that
Then
So, \(\frac{dM}{dt}\leq 0\) if \(\Re _{0m}\leq 1\). Furthermore, \(\frac{dM}{dt}=0\) if \(I_{\mathrm{m}}=0\) or \(\Re _{0m}=1\).
From this we see that \((S_{0},0,0)\) is the only singleton in \(\lbrace (S,I_{\mathrm{m}},R_{\mathrm{m}})\in \varOmega _{2}:\frac{dM}{dt}=0\rbrace \).
Therefore, by the principle of [12], DFE is globally asymptotically stable if \(\Re _{0m}\leq 1\).â€ƒâ–¡
3.2.7 Endemic equilibrium (EE)
The endemic equilibrium is denoted by \(E_{\mathrm{m}}^{*}=(S^{*} ,I_{\mathrm{m}}^{*} , R _{\mathrm{m}}^{*} )\) and it occurs when the disease persists in the community. To obtain it, we equate all the model equations (10) to zero. Then we obtain
From this we see that for the endemic equilibrium to exist, \(\Re _{0m} >1\).
Lemma 6
A unique endemic equilibrium point \(E^{*}\) exists and is positive if \(\Re _{0m}>1\).
3.2.8 Global stability of EE
Theorem 7
If \(\Re _{0m}>1\), the EE (\(E^{*}\)) of model (10) is globally asymptotically stable.
Proof
Systematically, we define an appropriate Lyapunov function G such that
Then differentiating equation (16) with respect to t gives
After substituting expressions for \(\frac{dS}{dt}\), \(\frac{dI_{\mathrm{m}}}{dt}\), and \(\frac{dR_{\mathrm{m}}}{dt}\) from (10) into (16) and collecting all positive terms together and also negative terms together, we obtain
where \(G_{1}=\pi +\delta _{2} R_{\mathrm{m}} +f_{2} S +\sigma _{2} I_{\mathrm{m}}+\frac{ \delta _{2}R_{\mathrm{m}}^{*} S^{*}}{S}+\frac{f_{2} S^{*} I_{\mathrm{m}}^{*}}{I_{\mathrm{m}}}+\frac{ \sigma _{2} I_{\mathrm{m}}^{*} R_{\mathrm{m}}^{*}}{R_{\mathrm{m}}}\), \(G_{2}= \delta _{2} R_{\mathrm{m}}^{*} +f_{2} S^{*} +f_{2} S I_{\mathrm{m}}^{*} +\frac{ \pi S^{*}}{S}+\frac{\delta _{2}R_{\mathrm{m}}S^{*}}{S} +\frac{(f_{2}+\mu )(SS^{*})^{2}}{S}+\frac{f_{2} SI_{\mathrm{m}}^{*}}{I_{\mathrm{m}}}+\frac{( \sigma _{2}+\alpha _{2}+\mu )(I_{\mathrm{m}}I_{\mathrm{m}}^{*})^{2}}{I_{\mathrm{m}}}+\frac{\sigma _{2} I_{\mathrm{m}} R_{\mathrm{m}}^{*}}{R_{\mathrm{m}}}+\frac{(\delta _{2}+\mu )(R_{\mathrm{m}}R_{\mathrm{m}}^{*})^{2}}{R _{\mathrm{m}}}\).
Thus if \(G_{1}< L_{G}\), then \(\frac{dG}{dt}\leq 0\) and \(\frac{dG}{dt}=0\) if and only if \(S=S^{*}\), \(I_{\mathrm{m}} = I_{\mathrm{m}}^{*}\), \(R_{\mathrm{m}}= R _{\mathrm{m}}^{*} \).
From this, we see that \(E^{*}=(S^{*}, I_{\mathrm{m}}^{*},R_{\mathrm{m}}^{*}) \) is the largest compact invariant singleton set in \(\lbrace ( S^{*}, I _{\mathrm{m}}^{*},R_{\mathrm{m}}^{*}) ) \in \varOmega _{2} : \frac{dG}{dt}=0 \rbrace \). Therefore, by the principle of [13], the endemic equilibrium (\(E ^{*} \)) is globally asymptotically stable in the invariant region if \(G_{1}< G_{2}\).â€ƒâ–¡
3.3 Pneumoniaâ€“meningitis coinfection model
The model equation of pneumonia and meningitis coinfection given in equation (1) is as follows:
where \(f_{1}=\frac{a(I_{\mathrm{p}}+I_{\mathrm{pm}})}{N}\), \(f_{2} = \frac{b(I_{\mathrm{m}}+I_{\mathrm{pm}})}{N}\), and \(S(0)=S_{0} \), \(I_{\mathrm{p}}(0)=I_{p_{0}} \), \(I _{\mathrm{m}}(0)=I_{m_{0}}\), \(I_{\mathrm{pm}}(0)=I_{pm_{0}}\), \(R_{\mathrm{p}}(0)=R_{p0}\), \(R_{\mathrm{m}}(0)=R _{m0}\), \(R_{\mathrm{pm}}(0)=R_{pm0} \), and \(B(0)=B_{0}\) are nonnegative initial values.
3.4 Qualitative analysis
3.4.1 Invariant region
To obtain invariant region, we consider the total population, which is \(N=S+I_{\mathrm{p}} + I_{\mathrm{m}} +I_{\mathrm{pm}}+R_{\mathrm{p}}+R_{\mathrm{m}}+R_{\mathrm{pm}}\). Then, by a technique similar to previous sections, we obtain
If we do not consider deaths from pneumonia and meningitis, then (19) becomes
Then solving equation (20) gives
3.4.2 Positivity of the solution
Theorem 8
If \(S_{0} >0\), \(I_{p_{0}} >0 \), \(I_{m_{0}}>0\), \(I_{pm_{0}}>0\), \(R_{p0} >0\), \(R_{m0}>0\), \(R_{pm0} > 0\), then all the solution set \((S(t),I_{\mathrm{p}}(t),I _{\mathrm{m}}(t), I_{\mathrm{pm}}(t),R_{\mathrm{p}}(t),R_{\mathrm{m}}(t),R_{\mathrm{pm}}(t))\) is positive for future time.
Proof
Consider \(t_{1}\) defined as follows:
Since \(S_{0} \geq 0\), \(I_{p_{0}} \geq 0 \), \(I_{m_{0}}\geq 0\), \(I_{pm_{0}} \geq 0\), \(R_{p0}\geq 0\), \(R_{m0}\geq 0\), \(R_{pm0} \geq 0\), thus \(t_{1} >0\). If \(t_{1} < \infty \), then necessarily S or \(I_{\mathrm{p}}\) or \(I_{\mathrm{m}}\) or \(I_{\mathrm{pm}}\) or \(R_{\mathrm{p}}\) or \(R_{\mathrm{m}}\) or \(R_{\mathrm{pm}}\) is equal to zero at \(t_{1}\). From equation (18), let us take the first equation
Using the variation of constants formula, the solution of equation (21) at \(t_{1}\) is given by
Moreover, since all the variables are positive in \([0, t_{1} ]\), then \(S(t_{1} ) > 0\).
It can be shown in a similar way that \(I_{\mathrm{p}}(t_{1})>0\), \(I_{\mathrm{m}}(t_{1})>0\), \(I_{\mathrm{pm}}(t_{1})>0\), \(R_{\mathrm{p}}(t_{1})>0\), \(R_{\mathrm{m}}(t_{1})>0\), and \(R_{\mathrm{pm}}(t_{1})>0\), which is a contradiction. Hence \(t_{1} =\infty \).â€ƒâ–¡
3.4.3 Diseasefree equilibrium
By substituting \(I_{\mathrm{p}}=0\), \(I_{\mathrm{m}} =0 \), and \(I_{\mathrm{pm}}=0\) in equation (18), DFE becomes
3.4.4 Basic reproduction number (\(\Re _{0}\))
Let us consider the infective compartments of the model
By using the next generation matrix outlined in [12], we obtain F and V in the following way:
The eigenvalues of \(FV^{1}\) are
The dominant eigenvalue, which is the basic reproduction number of \(FV^{1}\), is
3.4.5 Local stability of diseasefree equilibrium
Theorem 9
The diseasefree equilibrium point is locally asymptotically stable if \(\Re _{0}<1\), otherwise unstable.
Proof
The Jacobian matrix of the model at DFE is obtained below in equation (22).
where
From equation (22) we can get the following characteristic polynomial in equation (23).
Then from equation (23) we can get
Therefore, the DFE to be stable, \(\lambda _{6}\) and \(\lambda _{7}\) must be negative, i.e., \(\frac{a\pi }{\mu }k_{1}<0\) and \(\frac{b\pi }{ \mu }k_{2}<0\). For \(\frac{a\pi }{\mu }k_{1}<0\) means that \(\frac{a\pi }{\mu }< k_{1}\), which gives us \(\frac{a\pi }{\mu k_{1}}<1\), this means \(\Re _{0p}<1\).
Similarly, for \(\frac{b\pi }{\mu }k_{2}<0\) means that \(\frac{b\pi }{ \mu k_{2}}<1\), this is equivalent to \(\Re _{0m}<1\). Therefore, DFE is locally asymptotically stable if and only if \(\Re _{0}=\operatorname{Max}\{\Re _{0p}, \Re _{0m}\}<1\).â€ƒâ–¡
3.5 Global asymptotic stability of diseasefree equilibrium
To investigate the global stability of diseasefree equilibrium, we use the technique implemented by [9]. First the full pneumoniaâ€“meningitis model (18) can be rewritten as follows:
where X stands for the uninfected population, that is, \(X=(S,R_{\mathrm{p}}, R_{\mathrm{m}}, R_{\mathrm{pm}})\), and Z also stands for the infected population, that is, \(Z=(I_{\mathrm{p}},I_{\mathrm{m}},I_{\mathrm{pm}})\). The diseasefree equilibrium point of the model is denoted by \(U=(X^{*},0)\).
The point \(U=(X^{*},0)\) is a globally asymptotically stable equilibrium for the model provided that \(\Re _{0}< 1\) (which is locally asymptotically stable) and the following conditions must be met:
 \((H_{1})\) :

For \(\frac{dX}{dt}=F(X,0)\), \(X^{*}\) is globally asymptotically stable;
 \((H_{2})\) :

\(G(X,Z)= AZ\tilde{G}(X,Z)\), \(\tilde{G}(X,Z) \geq 0\) for \((X,Z)\in \varOmega \).
If model (18) meets the above two criteria, then the following theorem holds.
Theorem 10
The point \(U=(X^{*}, 0)\) is globally asymptotically stable equilibrium provided that \(\Re _{0}<1\) and conditions \((H_{1})\) and \((H _{2})\) are satisfied.
Proof
From system (18) we can get \(F(X,Z)\) and \(G(X,Z)\):
Consider the reduced system:
From equation (24), it is obvious that \(X^{*}=(\frac{\pi }{ \mu },0)\) is the global asymptotic point. This can be verified from the solution, namely \(S= \frac{\pi }{\mu } + (S(0)\frac{\pi }{\mu }) e ^{\mu t}\). As \(t \rightarrow \infty \), the solution \((S) \rightarrow \frac{\pi }{\mu }\) implies the global convergence of (24) in Î©.
Let
Then \(G(X,Z)\) can be written as \(G(X,Z)= AZ\tilde{G}(X,Z)\), where
In equation (25), \(\tilde{G_{2}}(X,Z) <0\), which leads to \(\tilde{G}(X,Z) <0\); that means the second condition (\(H_{2}\)) is not satisfied, so \(U=(X^{*},0)\) may not be globally asymptotically stable when \(\Re _{0} <1\).â€ƒâ–¡
3.6 Sensitivity analysis
In this subsection sensitivity analysis is performed to identify the most influential parameters for the expansion as well as for control of infection in the community. To perform this, we use techniques described in [14]. The sensitivity index of \(\Re _{0}\) with respect to a parameter, say x, is given by \(\varLambda _{x}^{\Re_{0}}=\frac{\partial \Re _{0}}{ \partial x}\frac{x}{\Re _{0}}\). Since \(\Re _{0}=\max \{\Re _{0p},\Re _{0m} \}\), we obtain the sensitivity analysis of \(\Re _{0p} \) and \(\Re _{0m}\) separately in the following way:
The above computation of sensitivity analysis is summarized in TableÂ 1.
From TableÂ 1, we understand that the parameters with positive sensitivity indices, particularly a and b, have great potential in expanding pneumonia, meningitis, and their coinfection in the community, because they increase their respective reproduction number, which is the average number of a secondary infection. However, the parameters with negative sensitivity index have a great contribution in controlling the expansion of pneumonia and meningitis in the community if their values are increased by keeping other parameters constant.
4 Numerical simulation
In this section, some numerical simulation is performed for the full model (pneumoniaâ€“meningitis coinfection model). We have used Maple 18 for checking the effect of some parameters in the expansion as well as for the control of pneumonia only, meningitis only, and coinfection of pneumonia and meningitis. The parameter values in TableÂ 2 are used for simulation purpose.
4.1 Effect of recovery rate on pneumonia infectious population
In this subsection, as we see in Fig.Â 2, we have experimented on the effect of \(\sigma _{1}\) in decreasing the number of pneumonia only infectious population by keeping the contact rate constant (\(a= 0.9\)). The figure reflects that when the values of \(\sigma _{1}\) increase, the number of pneumonia only infectious population is going down. Therefore public policy makers must concentrate on maximizing the values of recovery rate either by treating infected population or by boosting the immunity level of individuals to pneumonia disease.
4.2 Effect of recovery rate on meningitis infectious population
In Fig.Â 3 we see that \(\sigma _{2}\) plays a significant role in bringing down the meningitis infection. When the value of \(\sigma _{2}\) is increased from 0.1 to 0.9, the amount of infectious population due to meningitis is decreased, whereas the contact rate is kept constant, which is \(b= 0.06\). In fighting the meningitis disease, healthy workers or government must give big attention to treating of the infected population in a community.
4.3 Effect of pneumonia contact rate (a) on coinfectious population
In this section, as we see in Fig.Â 4, the contact rate of meningitis (b) and the recovery rate of coinfectious population (Ïƒ) are kept constant. The figure reflects that as the value of contact rate of pneumonia is increased, the coinfectious population increases, which means the expansion of coinfection of pneumonia and meningitis will increase. To control coinfection of pneumonia and meningitis, decreasing the contact rate of pneumonia is important. Therefore, stakeholders must concentrate on decreasing the contact rate of pneumonia by quarantine of pneumonia infected or by using an appropriate method of prevention mechanism to bring down the expansion of coinfection in the community.
4.4 Effect of contact rate of meningitis on coinfectious population
Similarly, in this section we have investigated the effect of meningitis contact rate (b) in the expansion of pneumoniaâ€“meningitis coinfection while keeping the recovery rate of coinfection (Ïƒ) constant. FigureÂ 5 shows that coinfectious population decreases as the meningitis contact rate is decreasing, by keeping pneumonia contact rate (a) and Ïƒ constant. This implies that, to bring down coinfection of pneumonia and meningitis, decreasing the contact rate of meningitis is vital.
4.5 Effect of recovery rate on coinfectious population
In this subsection, we experimented on the effect of recovery rate of pneumonia and meningitis (Ïƒ) on the coinfectious population. As we explained in the model description in Sect.Â 2, due to treatment or other mechanisms, coinfectious population either recover from pneumonia only or from meningitis only or from both diseases with their own probability and join their respective recovered compartment. Therefore, Fig.Â 6 shows that increasing the recovery rate of coinfectious population, which is Ïƒ, has a great advantage of eradicating both diseases in the community.
5 Discussion and conclusion
In Sect.Â 2 the proposed model is described in brief. The deterministic model is developed using ordinary differential equation and is subdivided into seven compartments. In Sect.Â 3, the model is analyzed qualitatively. To study the qualitative behavior of the model, first we split the full model into two, which are pneumonia only and meningitis only models. The qualitative behaviors, i.e., invariant region of the models, positivist of future solutions of the models, diseasefree equilibrium, basic reproduction numbers, endemic equilibria, stability analysis of DFE, and sensitivity analysis of basic reproductions of pneumonia only, meningitis only, and the full model, are analyzed in their respective order. In Sect.Â 4, numerically, we experimented on the effect of basic parameters in the expansion or control of pneumonia only, meningitis only, and coinfectious diseases. From the result we conclude that increasing the pneumonia recovery rate has a great contribution to bringing down pneumonia infection in the community. Similarly, increasing the meningitis recovery rate also has a contribution of eliminating meningitis diseases. The coinfection recovery rate also has an influence of minimizing coinfectious population if its value is increased. The other result obtained in this section is that decreasing the contact rate of either pneumonia or meningitis has a great influence on controlling coinfection of pneumonia and meningitis in the population.
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Acknowledgements
I would like to express my heartfelt appreciation to Haramaya University for financial support and also I am grateful to the anonymous reviewers.
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The main idea of this paper was proposed by GTT. GTT prepared the manuscript initially and performed all the steps of the proof in this research. The author read and approved the final manuscript.
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Tilahun, G.T. Modeling codynamics of pneumonia and meningitis diseases. Adv Differ Equ 2019, 149 (2019). https://doi.org/10.1186/s1366201920873
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DOI: https://doi.org/10.1186/s1366201920873
Keywords
 Coinfection
 Pneumonia
 Meningitis
 Stability
 Numerical simulation