The Bacillus Calmette-Guerin (BCG) vaccine has been in use for 99 years, and is regarded as one of the oldest human vaccines known today. It is recommended primarily due to its effect in preventing the most severe forms of tuberculosis, including disseminated tuberculosis and meningeal tuberculosis in children; however, its efficacy in preventing pulmonary tuberculosis and TB reactivation in adults has been questioned. Several studies however have found that asides from its role in tuberculosis prevention, the BCG vaccine also has protective effects against a host of other viral infections in humans, an effect which has been termed: heterologous, non-specific or off-target.
As we approach 100 years since the discovery of the BCG vaccine, we review the evidence of the non-specific protection offered by the vaccine against viral infections, discuss the possible mechanisms of action of these effects, highlight the implications these effects could have on vaccinology and summarize the recent epidemiological correlation between the vaccine and the on-going COVID-19 pandemic.
Several epidemiological studies have established that BCG does reduce all-cause mortality in infants, and also the time of vaccination influences this effect significantly. This effect has been attributed to the protective effect of the vaccine in preventing unrelated viral infections during the neonatal period. Some of such viral infections that have been investigated include: herpes simplex virus (HSV), human Papilloma virus (HPV), yellow fever virus (YFV), respiratory syncytial virus (RSV) and influenza virus type A (H1N1). These effects are thought to be mediated via induction of innate immune memory as well as heterologous lymphocytic activation. While epidemiological studies have suggested a correlation, the potential protection of the BCG vaccine against COVID-19 transmission and mortality rates is currently unclear. Ongoing clinical trials and further research may shed more light on the subject in the future.
BCG is a multifaceted vaccine, with many numerous potential applications to vaccination strategies being employed for current and future viral infections. There however is a need for further studies into the immunologic mechanisms behind these non-specific effects, for these potentials to become reality, as we usher in the beginning of the second century since the vaccine's discovery.
Citation: Oluwafolajimi A. Adesanya, Christabel I. Uche-Orji, Yeshua A. Adedeji, John I. Joshua, Adeniyi A. Adesola, Chibuike J. Chukwudike. Bacillus Calmette-Guerin (BCG): the adroit vaccine[J]. AIMS Microbiology, 2021, 7(1): 96-113. doi: 10.3934/microbiol.2021007
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The Bacillus Calmette-Guerin (BCG) vaccine has been in use for 99 years, and is regarded as one of the oldest human vaccines known today. It is recommended primarily due to its effect in preventing the most severe forms of tuberculosis, including disseminated tuberculosis and meningeal tuberculosis in children; however, its efficacy in preventing pulmonary tuberculosis and TB reactivation in adults has been questioned. Several studies however have found that asides from its role in tuberculosis prevention, the BCG vaccine also has protective effects against a host of other viral infections in humans, an effect which has been termed: heterologous, non-specific or off-target.
As we approach 100 years since the discovery of the BCG vaccine, we review the evidence of the non-specific protection offered by the vaccine against viral infections, discuss the possible mechanisms of action of these effects, highlight the implications these effects could have on vaccinology and summarize the recent epidemiological correlation between the vaccine and the on-going COVID-19 pandemic.
Several epidemiological studies have established that BCG does reduce all-cause mortality in infants, and also the time of vaccination influences this effect significantly. This effect has been attributed to the protective effect of the vaccine in preventing unrelated viral infections during the neonatal period. Some of such viral infections that have been investigated include: herpes simplex virus (HSV), human Papilloma virus (HPV), yellow fever virus (YFV), respiratory syncytial virus (RSV) and influenza virus type A (H1N1). These effects are thought to be mediated via induction of innate immune memory as well as heterologous lymphocytic activation. While epidemiological studies have suggested a correlation, the potential protection of the BCG vaccine against COVID-19 transmission and mortality rates is currently unclear. Ongoing clinical trials and further research may shed more light on the subject in the future.
BCG is a multifaceted vaccine, with many numerous potential applications to vaccination strategies being employed for current and future viral infections. There however is a need for further studies into the immunologic mechanisms behind these non-specific effects, for these potentials to become reality, as we usher in the beginning of the second century since the vaccine's discovery.
Due to inadequate medical facilities in developing countries, they face difficulties to prevent the spread of new and re-emerging infectious diseases. Thus, on the emergence of any new infectious disease, these countries focus on the alternate of medical facilities to slow down its spread. Awareness, which brings the behavioral changes among the population, can be seen as partial treatment at no cost. Apart from this, awareness regarding the spread of any infectious disease also reduces the economic burden required for medication [24,31]. The correct and timely knowledge about the disease has the capacity to halt its spread. To disseminate awareness, several modes, like Television, radio, posters, social media, etc., can be used. Nowadays social media is an important platform to propagate information regarding the risk of infection and its control mechanisms. In India, the number of Smartphone users are predicted to reach
In the recent past, some studies have been conducted to assess the impact of media to control the spread of infectious diseases [11,13,27,28,29,41,37,38,36,43,44]. In particular, Sun et al. [41] have studied an SIS model to see the effect of media-induced social distancing on disease prevalence in two patch. Authors have assumed that the contact rate decreases as the number of infected individuals in respective patch increases. Their finding suggests that the media coverage reduces the disease prevalence and shorten the duration of infection. Misra et al. [29] have proposed an SIS model to see the effects of awareness through media campaigns on the prevalence of infectious diseases by considering media campaigns as a dynamic variable whose growth is proportional to the number of infected individuals. In the modeling process, it is assumed that media campaigns induce behavioral changes among susceptible individuals and they form an isolated aware class, which is fully protected from infection. The model analysis reveals that the number of infectives decreases as the media campaigns increases. Samanta et al. [37] have extended this model by assuming that aware susceptibles are also vulnerable to infection but at a lower rate than unaware susceptibles. In this model, authors have assumed that the growth rate of media campaigns is proportional to the mortality rate due to the disease. It is shown that the rate of execution of awareness programs has enough impact on the endemic state and sustained oscillations may arise through Hopf-bifurcation. Further, Dubey et al. [11] have studied the role of media and treatment on the emergence of an infectious disease. It is shown that media plays an important role to eliminate the disease in presence of treatment. As the information regarding the disease driven by media campaigns modifies the human behavior towards the disease [9,12,35]. Therefore, Funk et al. [14] have studied the impact of public awareness and local behavioral response, where the proportion of both the susceptible and infected individuals is aware and aware susceptibles are less vulnerable to infection whereas awareness among infected individuals reduces the infectivity due to pharmaceutical interventions. Kiss et al. [22] have proposed an SIRS model with treatment compartment by considering two classes of susceptible and infected individuals (responsive and non-responsive towards the information). Authors have assumed that the responsive infected individuals search treatment faster in comparison to non-responsive infected individuals and the rate of disease transmission among responsive susceptibles is less due to the impact of information. It is shown that if the dissemination rate is fast enough, the infection can be eradicated. Here, it may be noted that information provided to the population through media changes the human behavior and population adopt the precautionary measures, like the use of bed nets for Malaria [3,33], condoms for HIV/ AIDS [1,8,20,34], wearing of face masks for influenza [19,23], vaccination [4,5,39], voluntary quarantine [17], etc., to prevent the spread of such diseases. Buonomo et al. [5] have studied the effect of information on vaccination of an infectious disease and it is shown that for low media coverage, the endemic state is globally stable and for higher media coverage, this endemic state becomes unstable and sustained oscillations occur. The effect of media-related information on the spread of any infectious disease is also studied by considering the decrease in the contact rate between susceptibles and infectives as (ⅰ) the exponential decaying function of infectives [6,26], (ⅱ) saturated function of infectives [7,27,42]. In these studies, it is found that media coverage does not affect basic reproduction number of the disease but controls the prevalence of the disease in the population.
For some infectious diseases, mathematical models are also proposed to see the effect of time delay in the implementation of awareness programs [15,30,32] and it is shown that incorporation of time delay in the modeling process destabilizes the system. Recently, Kumar et al. [24] have proposed an SIRS model to see the role of information and limited optimal treatment on disease prevalence. Authors have considered that the growth rate of information is proportional to a saturated function of infected individuals. It is shown that combined effects of information and treatment are more fruitful and economical during the course of infection. Agaba et al. [2] have proposed a SIRS - type model to see the impact of awareness by considering private awareness, which reduces the contact rate between unaware and aware population and public information campaigns on disease prevalence. It is shown that both the private and public awareness have the capacity to reduce the size of epidemic outbreaks.
It may be noted that the change in human behavior towards the diseases through print media, social media, internet are limited only to educated people but TV ads have the capacity to impact large population (less educated people too) in a very short period of time and thus are more effective. In previous studies, it is assumed that the awareness programs are implemented proportional to the number of infected individuals by considering media campaigns as a dynamic variable or transmission rate as decreasing function of infected individuals due to media alerts. However, it is plausible to assume that the cumulative number of TV and social media ads increases proportional to number of infected individuals and their growth rate decreases with the increase in number of aware individuals as cost is also involved in broadcasting the information. In this regard, Kim and Yoo [21] have presented the cost effective analysis for the implementation of TV campaigns to promote vaccination against seasonal influenza in an elderly population and the remarkable increment in vaccination coverage is found due to TV campaigns.
Keeping in mind the importance of information provided through social media and TV ads to the population regarding the protection against any infectious disease and cost involved in TV advertisements, in the present, we formulate and analyze a nonlinear mathematical model to study the effect of TV and social media ads which includes internet information as well as print media for the control of an infectious disease. In the modeling process, it is assumed that the cumulative number of TV and social media ads increases proportional to the number of infected individuals and their growth rate decreases with the increase in number of aware individuals.
Let in the region under consideration,
Taking into account above facts, the dynamics of the problem is governed by the following system of nonlinear ordinary differential equations:
dSdt=Λ−βSI−λSMp+M+νI+λ0A−dS,dIdt=βSI−(ν+α+d)I,dAdt=λSMp+M−(λ0+d)A,dMdt=r(1−θAω+A)I−r0(M−M0), | (1) |
where,
In the above model system (1),
Using the fact that
dIdt=βI(N−I−A)−(ν+α+d)I,dAdt=λMp+M(N−I−A)−(λ0+d)A,dNdt=Λ−dN−αI,dMdt=r(1−θAω+A)I−r0(M−M0). | (2) |
As the study of model system (1) is equivalent to the study of model system (2), so we study model system (2).
For the solutions of model (2), the region of attraction [29] is given by the set:
Ω={(I,A,N,M)∈R4+:0≤I,A≤ N≤Λd,0≤ M≤(M0+rΛr0d)=Mr}. |
and attracts all solutions initiating in the interior of the positive orthant.
Proof. From the third equation of model system (2), we have
dNdt≤Λ−dN |
and
ddt(Nedt)≤Λedt. |
Now integrating the above equation from
N(t)≤N0e−dt+Λd |
Therefore, by using the theory of differential inequalities [25], we obtain,
Further, from the fourth equation of model system (2) and using the fact,
dMdt+r0M≤(r0M0+rΛd). |
From the theory of differential inequality, we obtain
limt→∞supM(t)≤(M0+rΛr0d). |
This implies that
In this section, we show the feasibility of all equilibria by setting the rate of change with respect to time '
(ⅰ) The disease-free equilibrium (DFE)
(ⅱ) The endemic equilibrium (EE)
Let us denote
The feasibility of the equilibrium
Feasibility of equilibrium
In equilibrium
β(N−I−A)−(ν+α+d)=0, | (3) |
λMp+M(N−I−A)−(λ0+d)A=0, | (4) |
Λ−dN−αI=0, | (5) |
r(1−θAω+A)I−r0(M−M0)=0. | (6) |
From equation (5), we obtain
N−I=Λ−(α+d)Id. | (7) |
From equations (3) and (7), we get
A=β(Λ−(α+d)I)−d(ν+α+d)βd. | (8) |
Here, it may be noted that for
From equation (8), we obtain
G(I)=(1−θAω+A)=βdω+(1−θ)[β(Λ−(α+d)I)−d(ν+α+d)]βdω+β(Λ−(α+d)I)−d(ν+α+d). | (9) |
Differentiating above equation with respect to
G′(I)=θβ2dω(α+d)[βdω+β(Λ−(α+d)I)−d(ν+α+d)]2. | (10) |
From the above equation, it is clear that,
Further, from equation (6), we have
Mp+M=r0M0+rIG(I)(p+M0)r0+rIG(I). | (11) |
Substituting equations (8) and (11) in equation (4), we obtain an equation in
F(I)=λ(r0M0+rIG(I)(p+M0)r0+rIG(I))(ν+α+dβ)−(λ0+d)(β(Λ−(α+d)I)−d(ν+α+d)βd). | (12) |
From, equation (12), we may easily note that
(ⅰ)
(ⅱ)
Thus, clearly
Remark 1. From equation (12), it is easy to see that
Remark 2. From equation (12), it is also easy to note that
For the details of remarks 1 and 2, see Appendix B.
In this section, we summarize the results of linear stability of the model system (2) by finding the sign of eigenvalues of the Jacobian matrix around the equilibrium
Theorem 4.1. (i) The equilibrium (DFE)
(ii) The equilibrium (EE)
B3(B1B2−B3)−B21B4>0. | (13) |
For the proof of this theorem, see Appendix C.
The above theorem tells that if
The local stability analysis of the endemic equilibrium tells that if the initial values of any trajectory are near the equilibrium
In this section, we provide the result of the nonlinear stability of the model system (2). The nonlinear stability analysis of the endemic equilibrium follows from the following theorem:
Theorem 5.1. The endemic equilibrium
(G11)2<1645min{15(dθΛ)2,G12} | (14) |
(λM∗λM∗+(p+M∗)(λ0+d))2<1675min{13,dα} | (15) |
where,
For the proof of this theorem, see Appendix D.
Remark 3. From the nonlinear stability conditions (14) and (15) of the endemic equilibrium, it is clear that the growth rate of TV and social media ads and dissemination rate of awareness among susceptible individuals (i.e.,
In this section, we derive the conditions for the existence of Hopf-bifurcation around the endemic equilibrium
η4+B1(r)η3+B2(r)η2+B3(r)η+B4(r)=0. | (16) |
It is clear that
B3(rc)(B1(rc)B2(rc)−B3(rc))−B21(rc)B4(rc)=0. | (17) |
Then at
(η2+B3B1)(η2+B1η+B1B4B3)=0. | (18) |
This equation has four roots, say
η3+η4=−B1 | (19) |
ω20+η3η4=B2 | (20) |
ω20(η3+η4)=−B3 | (21) |
ω20η3η4=B4. | (22) |
If,
κ4+B1κ3+B2κ2+B3κ+B4+ϑ4−6κ2ϑ2−3B1κϑ2−B2ϑ2=0, | (23) |
4κϑ(κ2−ϑ2)−B1ϑ3+3B1κ2ϑ+2B2κϑ+B3ϑ=0. | (24) |
As
−(4κ+B1)ϑ2+4κ3+3B1κ2+2B2κ+B3=0. |
Using the value of
−64κ6−96B1κ5−16(3B21+2B2)κ4−8(B31+4B1B2)κ3−4(B22+2B21B2+B1B3−4B4)κ2−2B1(B1B3+B22−4B4)κ−(B3(B1B2−B3)−B21B4)=0. |
Differentiating above equation with respect to
[dκdr]r=rc=[ddr(B3(B1B2−B3)−B21B4)−2B1(B1B3+B22−4B4)]r=rc. |
Using the value of
[dκdr]r=rc=[ddr(B3(B1B2−B3)−B21B4)−2B1(B1B3+(2B3B1)2−2(2B3B1)B2+B22)]r=rc. |
This implies that
[dκdr]r=rc=[ddr(B1B2B3−B23−B21B4)−2B1(B1B3+(2B3/B1−B2)2)]r=rc≠0 |
if
Theorem 6.1. The reduced model system (2) undergoes Hopf-bifurcation around the endemic equilibrium
(i)
(ii)
i.e.,
In this section, we present the result for the direction of bifurcating periodic solutions. The following theorem provides the information about the direction and stability of the periodic solutions.
Theorem 7.1. The Hopf-bifurcation is forward (backward) if
For the proof of this theorem see, Appendix E.
In the previous sections, we have presented the qualitative analysis of the model system (2) and obtained results regarding the stability of equilibria and Hopf-bifurcation analysis of the endemic equilibrium. In this section, we conduct numerical simulation of model system (2) using MATLAB, regarding the feasibility of our analysis and its stability conditions.
For the set of parameter values, we have checked that the condition for the feasibility of the endemic equilibrium (i.e.,
I∗=932,A∗=57151,N∗=124767,M∗=1618. |
For the set of parameter values given in Table 1, the value of basic reproduction number
Parameter | Values | Parameter | Values |
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For the chosen set of parameter values given in Table 1, the dynamics near the endemic equilibrium changes as the growth rate of TV and social media ads (
In this paper, we have proposed and analyzed a nonlinear mathematical model to assess the impact of TV and social media ads on the spread of an infectious disease. In the model formulation, it is assumed that cumulative number of TV and social media ads increases proportional to the number of infected individuals and their growth rate decreases with the increase in number of aware individuals. It is also assumed that susceptible individuals contract infection due to the direct contact with infected individuals.
The analysis of proposed model reveals that only two equilibria; namely disease-free equilibrium (DFE)
The above discussion in terms of epidemiology states that the augmentation in the dissemination rate of awareness among susceptible individuals increases the number of aware individuals, which leads to decrease in the number of TV and social media ads and thus increases the number of infected individuals. Further, this increase in number of infected individuals increases the TV and social media ads, which increases the aware individuals. This interplay between the number of infected individuals and dissemination of awareness through TV and social media gives rise to oscillatory solution. It may be noted that when number of infected individuals become low and aware population reaches its saturated level, the oscillatory behavior dies out and further increase in dissemination rate provides the stable solution. Further increase in dissemination rate of awareness among the susceptible individuals after a threshold (i.e.,
Authors are grateful to reviewers for their useful suggestions. The second author thankfully acknowledge the University Grants Commission, New Delhi, India for providing financial assistance during the research period.
Here, we will find the basic reproduction number
dxdt=F(x)−V(x), |
where
The Jacobian matrix of
and
JV(E0)=((ν+α+d)000λM0p+M0(λM0p+M0+λ0+d)−λM0p+M0−λp(p+M0)2(Λd−A0)α0d0−r(1−θA0ω+A0)00r0), |
where
KL=(βΛd(ν+α+d)((λ0+d)(p+M0)λM0+(λ0+d)(p+M0))000000000000000). |
Therefore, basic reproduction number is
R1=βΛd(ν+α+d)((λ0+d)(p+M0)λM0+(λ0+d)(p+M0)). |
Here, we proof the results for
(ⅰ) Variation of
Here we show the variation of
β(N∗−I∗−A∗)−(ν+α+d)=0, | (25) |
λM∗p+M∗(N∗−I∗−A∗)−(λ0+d)A∗=0, | (26) |
Λ−dN∗−αI∗=0, | (27) |
r(1−θA∗ω+A∗)I∗−r0(M∗−M0)=0. | (28) |
Using equation (27) in equation (25), we have
Let F1(r,I∗,A∗):=β(Λ−(α+d)I∗d−A∗)−(ν+α+d)=0. | (29) |
And using equations (27) and (28) in equation (26), we have
G1(r,I∗,A∗):=λ(r(1−θA∗ω+A∗)I∗+r0M0r(1−θA∗ω+A∗)I∗+r0(p+M0))(Λ−(α+d)I∗d−A∗)−(λ0+d)A∗=0. | (30) |
Differentiating
∂G1∂r=λpr0(1−θA∗ω+A∗)I∗(r(1−θA∗ω+A∗)I∗+r0(p+M0))2(Λ−(α+d)I∗d−A∗), | (31) |
∂G1∂I∗=−λ(α+d)d(r(1−θA∗ω+A∗)I∗+r0M0r(1−θA∗ω+A∗)I∗+r0(p+M0))+λpr0r(1−θA∗ω+A∗)(r(1−θA∗ω+A∗)I∗+r0(p+M0))2(Λ−(α+d)I∗d−A∗), | (32) |
∂G1∂A∗=−λ(r(1−θA∗ω+A∗)I∗+r0M0r(1−θA∗ω+A∗)I∗+r0(p+M0))−(λpr0rθωI∗(ω+A∗)2(r(1−θA∗ω+A∗)I∗+r0(p+M0))2)×(Λ−(α+d)I∗d−A∗)−(λ0+d). | (33) |
Since
∂F1∂r+∂F1∂I∗dI∗dr+∂F1∂A∗dA∗dr=0. |
Using the values of
dA∗dr=−(α+dd)dI∗dr. | (34) |
Since
∂G1∂r+∂G1∂I∗dI∗dr+∂G1∂A∗dA∗dr=0. |
Using equations (31)-(33) and (34), we get
dI∗dr=−(A11A12). |
where,
Thus, from above equation it is clear that
(ⅱ) Variation of
Let F2(λ,I∗,A∗):=β(Λ−(α+d)I∗d−A∗)−(ν+α+d)=0, | (35) |
and
G2(λ,I∗,A∗):=λ(r(1−θA∗ω+A∗)I∗+r0M0r(1−θA∗ω+A∗)I∗+r0(p+M0))(Λ−(α+d)I∗d−A∗)−(λ0+d)A∗=0. | (36) |
Differentiating
∂F2∂λ+∂F2∂I∗dI∗dλ+∂F2∂A∗dA∗dλ=0. |
Using the values of
dA∗dλ=−(α+dd)dI∗dλ. | (37) |
Since
∂G2∂λ+∂G2∂I∗dI∗dλ+∂G2∂A∗dA∗dλ=0. |
Using the values of
dI∗dλ=−(A21A22), |
where,
Thus, from above equation it is easy to note that
(ⅲ) Variation of
Here we show the variation of
Let F3(M0,I∗,A∗):=β(Λ−(α+d)I∗d−A∗)−(ν+α+d)=0, | (38) |
and
G3(M0,I∗,A∗):=λ(r(1−θA∗ω+A∗)I∗+r0M0r(1−θA∗ω+A∗)I∗+r0(p+M0))(Λ−(α+d)I∗d−A∗)−(λ0+d)A∗=0. | (39) |
Differentiating
∂F3∂M0+∂F3∂I∗dI∗dM0+∂F3∂A∗dA∗dM0=0. |
Using the values of
dA∗dM0=−(α+dd)dI∗dM0. | (40) |
Since
∂G3∂M0+∂G3∂I∗dI∗dM0+∂G3∂A∗dA∗dM0=0. |
Using the values of
dI∗dM0=−(A31A32), |
where,
Thus, it is clear that
(ⅳ) Variation of
Let F4(p,I∗,A∗):=β(Λ−(α+d)I∗d−A∗)−(ν+α+d)=0, | (41) |
and
G4(p,I∗,A∗):=λ(r(1−θA∗ω+A∗)I∗+r0M0r(1−θA∗ω+A∗)I∗+r0(p+M0))(Λ−(α+d)I∗d−A∗)−(λ0+d)A∗=0. | (42) |
Differentiating
Since
∂F4∂p+∂F4∂I∗dI∗dp+∂F4∂A∗dA∗dp=0. |
Using the values of
dA∗dp=−(α+dd)dI∗dp. | (43) |
Since
∂G4∂p+∂G4∂I∗dI∗dp+∂G4∂A∗dA∗dp=0. |
Using the values of
dI∗dp=(A41A42), |
where,
(ⅴ) Variation of
Here, we show the variation of
Let F5(θ,I∗,A∗):=β(Λ−(α+d)I∗d−A∗)−(ν+α+d)=0, | (44) |
and
G5(θ,I∗,A∗):=λ(r(1−θA∗ω+A∗)I∗+r0M0r(1−θA∗ω+A∗)I∗+r0(p+M0))(Λ−(α+d)I∗d−A∗)−(λ0+d)A∗=0. | (45) |
Differentiating
∂F5∂θ+∂F5∂I∗dI∗dθ+∂F5∂A∗dA∗dθ=0. |
Using the values of
dA∗dθ=−(α+dd)dI∗dθ. | (46) |
Since
∂G5∂θ+∂G5∂I∗dI∗dθ+∂G5∂A∗dA∗dθ=0. |
Using the values of
dI∗dθ=(A51A52), |
where,
(ⅵ) Variation of
Let F6(ω,I∗,A∗):=β(Λ−(α+d)I∗d−A∗)−(ν+α+d)=0, | (47) |
and
G6(ω,I∗,A∗):=λ(r(1−θA∗ω+A∗)I∗+r0M0r(1−θA∗ω+A∗)I∗+r0(p+M0))(Λ−(α+d)I∗d−A∗)−(λ0+d)A∗=0. | (48) |
Differentiating
∂F6∂ω+∂F6∂I∗dI∗dω+∂F6∂A∗dA∗dω=0. |
Using the values of
dA∗dω=−(α+dd)dI∗dω. | (49) |
Since
∂G6∂ω+∂G6∂I∗dI∗dω+∂G6∂A∗dA∗dω=0. |
Using the values of
dI∗dω=−(A61A62), |
where,
Proof. Here, we show the local stability of equilibrium
J=(a11−βIβI0−λMp+M−λMp+M−(λ0+d)λMp+Mλp(N−I−A)(p+M)2−α0−d0r(1−θAω+A)−rθωI(ω+A)20−r0). |
where,
a11=β(N−2I−A)−(ν+α+d). |
Let
J0=(a011−(ν+α+d)000−λM0p+M0−λM0p+M0−(λ0+d)λM0p+M0a024−α0−d0r(1−θA0ω+A0)00−r0). |
where,
From the Jacobian matrix evaluated at
Now, we show the local stability of the endemic equilibrium
η4+B1η3+B2η2+B3η+B4=0. | (50) |
where,
B3(B1B2−B3)−B21B4>0. |
Hence, the endemic equilibrium
Proof. To establish the non-linear stability of equilibrium
G=I−I∗−I∗ln(II∗)+12m1(A−A∗)2+12m2(N−N∗)2+12m3(M−M∗)2. | (51) |
where, the coefficient
Differentiating equation (51) with respect to time '
dGdt=−β(I−I∗)2−m1(λM∗p+M∗+(λ0+d))(A−A∗)2−βdα(N−N∗)2−m3r0(M−M∗)2−β(A−A∗)(I−I∗)−m1λM∗p+M∗(I−I∗)(A−A∗)+m1λp(N−I−A)(p+M)(p+M∗)(M−M∗)(A−A∗)−m3rθωI(ω+A)(ω+A∗)(M−M∗)(A−A∗)+m1λM∗p+M∗(N−N∗)(A−A∗)+m3r(1−θA∗ω+A∗)(I−I∗)(M−M∗). |
Thus,
β<415m1(λM∗p+M∗+λ0+d), | (52) |
m1(λM∗p+M∗)2<4β15(λM∗p+M∗+λ0+d), | (53) |
m1(λM∗p+M∗)2<4βd5α(λM∗p+M∗+λ0+d), | (54) |
m1(λΛd(p+M∗))2<4r015m3(λM∗p+M∗+λ0+d), | (55) |
m3(rθΛd(ω+A∗))2<4r015m1(λM∗p+M∗+λ0+d), | (56) |
m3r2(1−θA∗ω+A∗)2<4βr09. | (57) |
From inequalities (52)-(54), we may choose the positive value of
Thus,
Proof. In this section, we proof the result for direction of bifurcating periodic solutions. For this, we translate the origin of the co-ordinate system to the Hopf-bifurcation point
Where,
(dz1dtdz2dtdz3dtdz4dt)=(f1(z1,z2,z3,z4)f2(z1,z2,z3,z4)f3(z1,z2,z3,z4)f4(z1,z2,z3,z4))+O(|z|3). |
where,
In the above expression, we are not interested in the coefficient of the third, forth and higher order terms as they make no contribution in the further calculation.
Now, system takes the form:
˙z=Pz+H(z). | (58) |
where,
H(z)=(h1h2h3h4) |
=(−βz21−βz1z2+βz1z3−λp(N∗−I∗−A∗)(p+M∗)3z24−λp(p+M∗)2z1z4−λp(p+M∗)2z2z4+λp(p+M∗)2z3z40rθωI∗(ω+A∗)3z22−rθω(ω+A∗)2z1z2). |
The eigenvectors
where,
Define,
V=(v11v12v13v14v21v22v23v24v31v32v33v34v41v42v43v44). |
The matrix
V−1PV=(0−ω000ω000000η30000η4). |
Inverse of matrix
V−1=(w11w12w13w14w21w22w23w24w31w32w33w34w41w42w43w44). |
where,
Where,
y=(y1,y2,y3,y4)T. |
Under this linear transformation system (58), takes the form:
˙y=(V−1PV)y+f(y). | (59) |
where,
˙y1=−ω0y2+f1(y1,y2,y3,y4),˙y2=ω0y1+f2(y1,y2,y3,y4),˙y3=η3y3+f3(y1,y2,y3,y4),˙y4=η4y4+f4(y1,y2,y3,y4). |
where,
f=(f1,f2,f3,f4)T. |
f1=w11h1+w12h2+w13h3+w14h4,f2=w21h1+w22h2+w23h3+w24h4,f3=w31h1+w32h2+w33h3+w34h4,f4=w41h1+w42h2+w43h3+w44h4,h1=−β(v11y1+v12y2+v13y3+v14y4)2−β(v11y1+v12y2+v13y3+v14y4)(v21y1+v22y2+v23y3+v24y4)+β(v11y1+v12y2+v13y3+v14y4)(v31y1+v32y2+v33y3+v34y4),h2=−λp(N∗−I∗−A∗)(p+M∗)3(v41y1+v42y2+v43y3+v44y4)2−λp(p+M∗)2(v11y1+v12y2+v13y3+v14y4)(v41y1+v42y2+v43y3+v44y4)−λp(p+M∗)2(v21y1+v22y2+v23y3+v24y4)(v41y1+v42y2+v43y3+v44y4)+λp(p+M∗)2(v31y1+v32y2+v33y3+v34y4)(v41y1+v42y2+v43y3+v44y4),h3=0,h4=−rθω(ω+A∗)2(v11y1+v12y2+v13y3+v14y4)(v21y1+v22y2+v23y3+v24y4)+rθωI∗(ω+A∗)3(v21y1+v22y2+v23y3+v24y4)2. |
Furthermore, we can calculate
Using above, we can find following quantities:
h21=H21+2(H1110σ111+H2110σ211)+H1101σ120+H2101σ220,c1(0)=i2ω0(h11h20−2|h11|2−|h02|23)+h212,μ2=−Re(c1(0))ϕ′(0),τ2=−Im(c1(0))+μ2σ′(0)ω0,β2=−2μ2ϕ′(0). |
where,
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