In the intelligent manufacturing environment, modern industry is developing at a faster pace, and there is an urgent need for reasonable production scheduling to ensure an organized production order and a dependable production guarantee for enterprises. Additionally, production cooperation between enterprises and different branches of enterprises is increasingly common, and distributed manufacturing has become a prevalent production model. In light of these developments, this paper presents the research background and current state of distributed shop scheduling. It summarizes relevant research on issues that align with the new manufacturing model, explores hot topics and concerns and focuses on the classification of distributed parallel machine scheduling, distributed flow shop scheduling, distributed job shop scheduling and distributed assembly shop scheduling. The paper investigates these scheduling problems in terms of single-objective and multi-objective optimization, as well as processing constraints. It also summarizes the relevant optimization algorithms and their limitations. It also provides an overview of research methods and objects, highlighting the development of solution methods and research trends for new problems. Finally, the paper analyzes future research directions in this field.
Citation: Jianguo Duan, Mengting Wang, Qinglei Zhang, Jiyun Qin. Distributed shop scheduling: A comprehensive review on classifications, models and algorithms[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 15265-15308. doi: 10.3934/mbe.2023683
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In the intelligent manufacturing environment, modern industry is developing at a faster pace, and there is an urgent need for reasonable production scheduling to ensure an organized production order and a dependable production guarantee for enterprises. Additionally, production cooperation between enterprises and different branches of enterprises is increasingly common, and distributed manufacturing has become a prevalent production model. In light of these developments, this paper presents the research background and current state of distributed shop scheduling. It summarizes relevant research on issues that align with the new manufacturing model, explores hot topics and concerns and focuses on the classification of distributed parallel machine scheduling, distributed flow shop scheduling, distributed job shop scheduling and distributed assembly shop scheduling. The paper investigates these scheduling problems in terms of single-objective and multi-objective optimization, as well as processing constraints. It also summarizes the relevant optimization algorithms and their limitations. It also provides an overview of research methods and objects, highlighting the development of solution methods and research trends for new problems. Finally, the paper analyzes future research directions in this field.
The treatment of Human Immunodeficiency Virus-1 (HIV) infection presently faces extraordinary opportunities and challenges in achieving durable efficacy in previously untreated subjects. In fact, even though it does not allow a complete elimination of the virus from several tissue and blood cell reservoirs, antiretroviral treatment has been shown to decrease viral replication as detected by HIV ribonucleic acid (RNA) in plasma [27,35]. The virological control obtained on treatment, documented by undetectable levels of HIV RNA in plasma, has been shown to improve the immunological setting of patients, increasing the number of CD4+ cells, the subset of lymphocytes mainly affected by the virus [23].
Since high rates of HIV RNA control have been shown on treatment both in recent cohort studies and in clinical trials [36], this achievement has been associated with a marked decrease in several opportunistic infections and clinical comorbidities [40,12]. Besides, a successful virological control has also beneficial effects in reducing the spread of HIV infection through sexual contacts, as documented by studies evaluating antiretroviral treatment as a tool for the prevention of HIV transmission to healthy partners [14].
The decrease in CD4 cell counts during HIV infection in the absence of therapy has been used in the past to obtain a marker of immune depletion in order to advise in favor or against the introduction of antiretroviral therapy. Since AIDS-related opportunistic infections generally appear when CD4 are lower than 200 cells
According to estimates by the Italian National Institute of Health, about 120,000 individuals are living with HIV in Italy [7]. A recent investigation reports that 94,146 individuals were in care at public clinical centres at the end of 2012 and 82,501 were receiving antiretroviral treatments [6]. The proportion of patients in therapeutic failure is precisely unknown, even if clinical trial data show that the prevalence of HIV undetectability on treatment can reach 80-90
Since the seminal paper by May and Anderson [26], a large number of studies have been devoted to mathematical models of HIV epidemic. We mention some recent investigations [17,16,18] that establish global dynamics properties of classes of models of interest for the HIV epidemic. Other mathematical models have been developed either to assess the impact of larger treatment availability on the spread of HIV [21,11], showing a beneficial impact on the reduction of the infection incidence rate, and to assess the effects of virus mutation and of drug resistance onset [2,34,24].
Many papers aim at describing the epidemic evolution in single countries or in single geografic regions (see e.g [33,28,44]). Concerning Italy, Arcà et al. built a detailed multi-stage ODE model for the HIV transmission in Latium (the Italian region around Rome), with eight groups differentiated by sex and routes of infection [1]. With focus on the epidemic among drug users in Latium, Iannelli et al. [19] proposed an age-structured model validated against data on the number of new AIDS cases. Camoni et al. [7], by considering different high-risk subpopulations, and gathering a number of different statistical information, gave an estimate of the history of the epidemic in Italy, taking also into account the practice of therapy. Nevertheless, no predictions are available about the possible impact of an early antiretroviral treatment and 'test and treat' strategies on HIV epidemic in specific European Countries, such as Italy.
In order to forecast the possible impact of new strategies in antiretroviral management, a differential equation model is developed in the present paper to describe the evolution of HIV epidemic in Italy for the years
In Section 2, the ODE model is formulated, and in Section 3 the parameter values are assessed, in part by means of best fitting of available data over the decade
The present model describes the dynamics of HIV infection in Italy by assuming that the heterosexual/homosexual activity and the exchange of needles among drug users are the only significant modalities of HIV transmission. Therefore, the mother-child transmission and the transmission by blood transfusion are disregarded. This choice is supported by recent reports of the Italian National Institute of Health [4,5], indicating that the percentages of new diagnoses in 2012 and 2013 related to transmission routes not involving inter-individual contacts are less than
The HIV epidemic in Italy is still mainly concentrated in high risk subpopulations (injected drug users, male homosexuals, female sex workers, multi-partners heterosexual male). However, there are evidences of an important spreading of the infection outside these groups. Indeed, 32
On the basis of this evidence and for the sake of simplicity, we assume in our model that the population susceptible of infection,
To represent the intra-host disease progression, the untreated infected population is distributed over four compartments in cascade according to the CD4 counts. In particular, according to [1,34], we distinguish the infected population
•
•
•
•
Compartment
Under the above assumptions, we can describe the dynamics of susceptible and infected subpopulations by the following ODE system (see the block diagram of Figure 1):
˙S(t)=Λ(t)+(1−α)Φ(t)−μ(t)S(t)−(5∑n=1βnIn(t))S(t)˙I1(t)=a1αΦ(t)+(5∑n=1βnIn(t))S(t)−(θ1+μ(t))I1(t),˙I2(t)=a2αΦ(t)+θ1I1(t)−(θ2+μ(t)+δ2)I2(t),˙I3(t)=a3αΦ(t)+θ2I2(t)−(θ3+μ(t)+δ3)I3(t)+ξ(t)I5(t),˙I4(t)=θ3I3(t)−(θ4+μ(t)+δ4)I4(t),˙I5(t)=δ2I2(t)+δ3I3(t)+δ4I4(t)−(ξ(t)+μ(t))I5(t), | (1) |
where
Note that, since immigration of people in AIDS stage appears rather unlikely, no input from immigration is assumed into compartment
an=1/θn1/θ1+1/θ2+1/θ3,n=1,2,3, | (2) |
that is
The possibility of starting the treatment from different stages of the disease is taken into account by the per capita treatment rates,
We assumed in model 1 a bilinear form of the incidence rate, instead of the more usual standard incidence where the infective population is normalized to the total population size, which is
The behaviour of the model has been tested by comparison with epidemiological data over the years
• number of infected individuals over the years
• number of treated individuals at the end of 2012 from data of public clinical centres [6];
• number of new AIDS cases over the years
• number of deaths from AIDS over the years
Data reported by the National Institute of Statistics [31] were used to estimate the time-course of the demographic quantities
μk=MkˉN[20,70]k⋅365, |
where
Since
dN[20,70](t)dt=Λ(t)+Φ(t)−μ(t)N[20,70](t). | (3) |
that does not include the loss due to the ageing of infected people beyond age
Eq. 3 allows the estimation of
Concerning the disease progression, the parameters
Parameters | Value | Source |
[31] | ||
[31] | ||
[31] | ||
[7] | ||
Assumed | ||
Assumed | ||
[34] | ||
[34] | ||
[34,45] | ||
[34,45] | ||
Estimated | ||
[34] | ||
[34] | ||
[34] | ||
[34,6] | ||
δ2 | 1.10·10-19 day-1 | Estimated |
δ3 | 2.27·10-3 day-1 | Estimated |
δ4 | 3.2·10-3 day-1 | Estimated |
We remark here that the parameters
The exit rate from the treatment
The remaining parameters
The best fitting of the available data suggests that, in the decade
We start by predicting the HIV epidemic evolution up to 2025, assuming that
Next, to investigate the effect of changing the treatment eligibility criterion, we hypothesize two scenarios: a)
Finally, we investigated the effect of changing the treatment exit rate and the infectivity of treated subjects. In particular, we first assumed
The effect of changing the infectivity of treated subjects is shown in Table 2. The values of some significant quantities at January 1st 2025 are reported for
Values at January 1st 2025 | |||
0.1 | 0.2 | 0.3 | |
Infected (persons) | | | |
Treated (persons) | | | |
HIV infection rate (persons | 3.85 | 5.794 | 7.816 |
New cases of AIDS (persons | 975.3 | 1062 | 1149 |
AIDS deaths (persons | 557.3 | 606.7 | 656.8 |
In this section, we establish the stability properties of the time-invariant version of the model described by Eqs. 1, where the functions
˙S(t)=Λ+(1−α)Φ−μS(t)−(5∑n=1βnIn(t))S(t)˙I1(t)=a1αΦ+(5∑n=1βnIn(t))S(t)−(θ1+μ)I1(t),˙I2(t)=a2αΦ+θ1I1(t)−(θ2+μ+δ2)I2(t),˙I3(t)=a3αΦ+θ2I2(t)−(θ3+μ+δ3)I3(t)+ξI5(t),˙I4(t)=θ3I3(t)−(θ4+μ+δ4)I4(t),˙I5(t)=δ2I2(t)+δ3I3(t)+δ4I4(t)−(ξ+μ)I5(t), | (4) |
with
Λ+(1−α)Φ>0. | (5) |
The above assumption prevents the susceptible population from the extinction. Indeed, if
We observe that the set
Ω={(S,I1,…,I5)∈IR6+|S+5∑n=1In≤Λ+Φμ}, | (6) |
where
˙N(t)=˙S(t)+5∑n=1˙In(t)=Λ+Φ−μN(t)−θ4I4(t) ≤Λ+Φ−μN(t), | (7) |
for non-negative
N(t)≤N(t0)e−μ(t−t0)+Λ+Φμ(1−e−μ(t−t0)), | (8) |
which in turn implies
Also, we remark that if an equilibrium point of system 4 exists in
Λ+Φ−μN∗−θ4I∗4=0, | (9) |
whereas if
Λ+Φ−μN∗−θ4I∗4<0. |
Denoting by
Λ+Φ=μS∗+(5∑n=1βnI∗n)S∗,Q1I∗1=(5∑n=1βnI∗n)S∗,Q2I∗2=θ1I∗1,Q3I∗3=θ2I∗2+ξI∗5,Q4I∗4=θ3I∗3,Q5I∗5=δ2I∗2+δ3I∗3+δ4I∗4, | (10) |
where
Q1=θ1+μ,Q2=θ2+μ+δ2,Q3=θ3+μ+δ3,Q4=θ4+μ+δ4,Q5=ξ+μ. | (11) |
From the last four equations in 10 we obtain:
I∗n=mnI∗1,n=2,…,5, | (12) |
where
m2=θ1Q2,m3=θ2+δ2(ξ/Q5)Q3−δ3(ξ/Q5)−θ3(δ4/Q4)(ξ/Q5)m2,m4=θ3Q4m3,m5=δ2Q5m2+δ3Q5m3+δ4Q5m4. | (13) |
Introducing the notation
η=β1+β2m2+β3m3+β4m4+β5m5, |
and taking into account 12, the first two equations of the system 10 can be rewritten as
Λ+Φ−μS∗−ηI∗1S∗=0,I∗1(ηS∗−Q1)=0. | (14) |
System 14 admits two different solutions
S∗=Λ+Φμ,I∗1=0 or S∗=Q1η,I∗1=(Λ+ΦQ1−μη), | (15) |
meaning that system 4 has in
S∗=Λ+Φμ,I∗n=0,n=1,…,5, | (16) |
and, if and only if
S∗=Q1η,I∗n=mn(Λ+ΦQ1−μη),n=1,…,5, | (17) |
where
The stability analysis of the disease-free equilibrium has been performed by means of the reproduction number
f=[(5∑n=1βnIn(t))S(t)0000], | (18) |
v=[Q1I1(t)−θ1I1(t)+Q2I2(t)−θ2I2(t)−ξI5(t)+Q3I3(t)−θ3I3(t)+Q4I4(t)−δ2I2(t)−δ3I3(t)−δ4I4(t)+Q5I5(t)], | (19) |
where the generic entry
F=∇f|Edf=Λ+Φμ[β1β2β3β4β500000000000000000000], | (20) |
V=∇v|Edf=[Q10000−θ1Q20000−θ2Q30−ξ00−θ3Q400−δ2−δ3−δ4Q5]. | (21) |
According to [9], the reproduction number is defined as the spectral radius of the next generation matrix
V−1=1Q1[1∗∗∗∗m2∗∗∗∗m3∗∗∗∗m4∗∗∗∗m5∗∗∗∗], | (22) |
where only the first column of
R=ρ(FV−1)=Λ+ΦμηQ1, | (23) |
where
It is useful to rewrite the expression of the endemic equilibrium in terms of
S∗=Λ+Φμ1R,I∗n=mnQ1μ(R−1),n=1,…,5, | (24) |
showing that the endemic equilibrium exists if and only if
Thanks to the bilinear form of the incidence rate, it can be verified that model 4 with
Theorem 5.1. When
proof. See the proof of Theorem 4.1 in [18]. It can be easily verified that our model satisfies all the basic assumptions (
Theorem 5.2. When
Proof. The proof of the existence comes directly from Eq. 24. Indeed, vector 24 is positive if and only if
In view of the analysis of model 4 with
L(S,I1,…,I5)=c1(S−S∗−S∗ln(SS∗))+5∑n=1cn(In−I∗n−I∗nln(InI∗n)), | (25) |
which, for positive coefficients
L(S,I1,…,I5)=0⇔(S,I1,…,,I5)=(S∗,I∗1,…,I∗5),L(S,I1,…,I5)>0,∀(S,I1,…,I5)≠(S∗,…,I∗5). | (26) |
From Eq. 25 it follows
˙L(S,I1,…,I5)=c1(1−S∗S)˙S+5∑n=1cn(1−I∗nIn)˙In. | (27) |
By exploiting either the equations of the dynamic system 4 and the equilibrium equations in 10, and introducing also the compact notation
˙L(S,I1,…,I5)=c1(μS∗(2−s−1s)+5∑n=1βnS∗I∗n(2−sini1−1s−i1+in))+c2(θ1I∗1(1−i1i2−i2+i1))+c3(θ2I∗2(1−i2i3−i3+i2)+ξI∗5(1−i5i3−i3+i5))+c4(θ3I∗3(1−i3i4−i4+i3))+c5(δ2I∗2(1−i2i5−i5+i2)+δ3I∗3(1−i3i5−i5+i3)+δ4I∗4(1−i4i5−i5+i4)). | (28) |
Such a function is non-positive in the interior of
c1=Θ1Θ3(Θ24∑n=2Δn+Ξ5Δ2),c2=5∑n=2BnΘ3(Θ24∑n=2Δn+Ξ5Δ2),c3=Θ1Θ3(B34∑n=2Δn+B44∑n=2Δn+B54∑n=3Δn),c4=Θ1(B3Ξ5Δ4+B4(Θ24∑n=2Δn+Ξ5(Δ2+Δ4))+B5Δ4(Θ2+Ξ5)),c5=Θ1Θ3(Ξ55∑n=3Bn+Θ2B5), | (29) |
where
Θ1=θ1I∗1,Θ2=θ2I∗2,Θ3=θ3I∗3,Δ2=δ2I∗2,Δ3=δ3I∗3,Δ4=δ4I∗4,Ξ5=ξI∗5,Bn=βnS∗I∗n,n=2,…,5. | (30) |
Therefore, we obtain
In order to illustrate the role of antiviral treatment, it may be of interest to compute the reproduction number
In this section we report the equilibrium analysis of model 4 when
For the generic equilibrium point, the following algebraic system holds
Λ+(1−α)Φ=μS∗+(5∑n=1βnI∗n)S∗,Q1I∗1=(5∑n=1βnI∗n)S∗+a1αΦ,Q2I∗2=a2αΦ+θ1I∗1,Q3I∗3=a3αΦ+θ2I∗2+ξI∗5,Q4I∗4=θ3I∗3,Q5I∗5=δ2I∗2+δ3I∗3+δ4I∗4, | (31) |
where the quantities
I∗n=qn+mnI∗1,n=2,…,5, | (32) |
where the coefficients
q2=a2αΦQ2,q3=a3αΦQ3−δ3(ξ/Q5)−θ3(δ4/Q4)(ξ/Q5)+θ2+δ2(ξ/Q5)Q3−δ3(ξ/Q5)−θ3(δ4/Q4)(ξ/Q5)q2,q4=θ3Q4q3,q5=δ2Q5q2+δ3Q5q3+δ4Q5q4. | (33) |
Theorem 5.3. When
Proof. Recalling the definition of
γ=β2q2+β3q3+β4q4+β5q5, |
from the first two equations of 31 and from equations 32 we obtain
S∗=Λ+(1−α)Φγ+ηI∗1+μ, | (34) |
and the quadratic equation in the variable
aI∗12+bI∗1+c=0, |
where
a=−Q1η,b=(Λ+(1+a1α−α)Φ)η−Q1(γ+μ),c=a1αΦμ+(Λ+(1+a1α−α)Φ)γ. |
From 5 we have
I∗1=ζ+√ζ2+ν,ζ=(Λ+(1+a1α−α)Φ)η−Q1(γ+μ)2Q1η,ν=a1αΦμ+(Λ+(1+a1α−α)Φ)γQ1η. | (35) |
Note that if a positive equilibrium exists, Eq. 34 implies that 5 must hold. Thus, 5 is a necessary and sufficient condition for the existence of the unique positive equilibrium defined by Eqs. 34, 35, and 32.
The stability of such an endemic equilibrium can be proved considering the same Lyapunov function defined by 25. Indeed, from the derivative of
˙L(S,I1,…,I5)=˙L′(S,I1,…,I5)+˙L′′(S,I1,…,I5), | (36) |
where
˙L′(S,I1,…,I5)=3∑n=1cnanαΦ(2−in−1in), | (37) |
while the term
Note that if the weaker condition
The present paper presents a mathematical model developed to assess the impact of early treatment strategies in Italy. If the treatment is initiated regardless of CD4 cell counts at diagnosis, a significant impact over AIDS incidence and mortality is predicted, provided that the trend of reduced resistance to drug, observed in the decade
It must be recalled, however, that the knowledge about the HIV prevalence among immigrants and the infectivity of treated subjects is rather uncertain up to now. So, a better assessment of the value of these parameters would improve the quantitative reliability of the model. Conversely, more subtle phenomena like the possible dependence of some model parameters on the virus strain, as well as the possible correlation between the transmission routes and the drug resistance mutations of the transmitted virus [30], are expected to be of minor importance owing to the large prevalence of wild type HIV infections in Europe (more than 90
The formal analysis of the model has allowed to establish the existence of equilibria and their global stability, classifying them on the basis of the prevalence of infected among immigrants. Only in the case in which the fraction of infected among immigrants is zero, the disease-free equilibrium exists and a threshold parameter for its stability can be identified in the reproduction number. The value of the reproduction number, moreover, has a non-trivial relationship with the parameters characterizing the dynamics of the compartment related to patients under treatment.
However, the knowledge of equilibria and their stability is scarcely useful for predicting the HIV epidemic over reasonable time horizons. Numerical simulations have shown indeed a very slow convergence to the equilibrium point (
It may be questioned whether the disease progression in patients for whom therapy is interrupted or is no longer effective can be equated with the progression experienced by naive infected individuals. Thus, it would be worthy to design and study a little more complex model in which different disease progressions for naive and previously treated patients are considered. Finally, it cannot be guaranteed that the trend of reduced drug resistance will continue over the next decade. Therefore, a model of the evolution in Italy of the resistance to different classes of drugs is still needed and it is the main goal of our future work.
Acknowledgments. The present work was partially supported by the PRIN "Study of determinants of secondary and primary resistance to antiretrovirals to support control strategies of HIV transmission". The authors wish to thank the anonymous referees for their useful suggestions and comments.
Appendix. It can be easily verified that the basic hypotheses (
Additional properties are required to prove the global asymptotic stability of the disease-free equilibrium and the global asymptotic stability of the unique endemic equilibrium. In this section, it is shown that all these additional properties are satisfied by our model.
With this aim we introduce the following notation:
•
•
•
•
We also denote by
(A1): For
gn(S,In)−gn(˜S,In)=βn(S−˜S)In≤0,0≤S≤˜S,In≥0,gn(S,In)−gn(˜S,In)=βn(S−˜S)In=0,In>0,⟹S=˜S, |
0<gn(˜S,In)ψ(In)=βn˜SQn<∞,In>0; |
(A2): For
0≤ωj,n(In)ψ(In)=σj,nQn<∞,In>0; |
(B1): For
(χ(S)−χ(S∗))(φ(S)−φ(S∗))=−μ(S−S∗)2<0; |
(B2): For
(gn(S,In)φ(S)−gn(S∗,I∗n)φ(S∗))(gn(S,In)φ(S)ψn(In)−gn(S∗,I∗n)φ(S∗)ψn(I∗n))= βn(In−I∗n)(βnQn−βnQn)=0; |
(B3): For
(ωj,n(In)−ωj,n(In)∗)(ωj,n(In)ψn(In)−ωj,n(In)∗)ψn(I∗n))= σj,n(In−I∗n)(σj,nQn−σj,nQn)=0; |
(B4): All the functions
Let us finally define the weight matrix
M=[β1S∗I∗1β2S∗I∗2β3S∗I∗3β4S∗I∗4β5S∗I∗5θ1I∗100000θ2I∗200ξI∗500θ3I∗3000δ2I∗2δ3I∗3δ4I∗40]. | (38) |
As the the weighted graph (
On the basis of the properties given above, Theorems 4.1 and 5.1 in [18] hold in our case, guaranteeing that the disease-free equilibrium is globally asymptotically stable for
However, for the completeness of our study, we report here some details of the proof of Theorem 5.2 given in Section 5.1. As suggested in [18], we consider the following general Lyapunov function
L(S,I1,…,I5)=c1∫SS∗φ(τ)−φ(S∗)φ(τ)dτ+5∑n=1cn∫InI∗nψ(τ)−ψ(I∗n)ψ(τ)dτ. | (39) |
Recalling that in our case the removal rates
L(M)=diag(5∑n=1m1,n,…,5∑n=1m5,n)−M=[5∑n=2βnS∗I∗n−β2S∗I∗2−β3S∗I∗3−β4S∗I∗4−β5S∗I∗5−θ1I∗1θ1I∗10000−θ2I∗2θ2I∗2+ξI∗50−ξI∗500−θ3I∗3θ3I∗300−δ2I∗2−δ3I∗3−δ4I∗44∑n=2δnI∗n]. | (40) |
It can be easily verified that the five cofactors of
˙L(S,I1,…,I5)=T′(S,I1,…,I5)+T′′(S,I1,…,I5), | (41) |
where
T′(S,I1,…,I5)=c1((μS∗+B1)(2−s−1s)+B2(3−si2i1−1s−i1i2)), | (42) |
and
T′′(S,I1,…,I5)=c15∑n=3Bn(3−sini1−1s−i1i2−i2+in)+c3(θ2I∗2(1−i2i3−i3+i2)+ξI∗5(1−i5i3−i3+i5))+c4(θ3I∗3(1−i3i4−i4+i3))+c5(δ2I∗2(1−i2i5−i5+i2)+δ3I∗3(1−i3i5−i5+i3)+δ4I∗4(1−i4i5−i5+i4)). | (43) |
Since from the arithmetic-mean/geometric-mean inequality we have
T′′(S,I1,…,I5)=B3(Θ1Θ3(Θ24∑n=2Δn(4−si3i1−1s−i1i2−i2i3)+Ξ5Δ2(5−si3i1−1s−i1i2−i5i3−i2i5)+Ξ5Δ3(2−i5i3−i3i5)+Ξ5Δ4(3−i5i3−i3i4−i4i5)))+B4(Θ1Θ3(Θ24∑n=2Δn(5−si4i1−1s−i1i2−i2i3−i3i4)+Ξ5Δ2(6−si4i1−1s−i1i2−i5i3−i3i4−i2i5)+Ξ5Δ3(2−i5i3−i3i5)+Ξ5Δ4(3−i5i3−i3i4−i4i5)))+B5(Θ1Θ3(Θ2Δ2(4−si5i1−1s−i1i2−i2i5)+Θ2Δ3(5−si5i1−1s−i1i2−i2i3−i3i5)+Θ2Δ4(6−si5i1−1s−i1i2−i2i3−i3i4−i4i5)+Ξ5Δ2(4−si5i1−1s−i1i2−i2i5)+ |
+Ξ5Δ3(2−i5i3−i3i5)+Ξ5Δ4(3−i5i3−i3i4−i4i5)). | (44) |
From 44 it is easy to deduce that
Finally, in order to prove the asymptotic stability of
S=S∗,in=λ, n=1,…,5, | (45) |
where
0=Λ−μS∗−λ5∑n=1βnI∗nS∗. | (46) |
Since the same equation holds for the endemic equilibrium with
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Parameters | Value | Source |
[31] | ||
[31] | ||
[31] | ||
[7] | ||
Assumed | ||
Assumed | ||
[34] | ||
[34] | ||
[34,45] | ||
[34,45] | ||
Estimated | ||
[34] | ||
[34] | ||
[34] | ||
[34,6] | ||
δ2 | 1.10·10-19 day-1 | Estimated |
δ3 | 2.27·10-3 day-1 | Estimated |
δ4 | 3.2·10-3 day-1 | Estimated |
Values at January 1st 2025 | |||
0.1 | 0.2 | 0.3 | |
Infected (persons) | | | |
Treated (persons) | | | |
HIV infection rate (persons | 3.85 | 5.794 | 7.816 |
New cases of AIDS (persons | 975.3 | 1062 | 1149 |
AIDS deaths (persons | 557.3 | 606.7 | 656.8 |
Parameters | Value | Source |
[31] | ||
[31] | ||
[31] | ||
[7] | ||
Assumed | ||
Assumed | ||
[34] | ||
[34] | ||
[34,45] | ||
[34,45] | ||
Estimated | ||
[34] | ||
[34] | ||
[34] | ||
[34,6] | ||
δ2 | 1.10·10-19 day-1 | Estimated |
δ3 | 2.27·10-3 day-1 | Estimated |
δ4 | 3.2·10-3 day-1 | Estimated |
Values at January 1st 2025 | |||
0.1 | 0.2 | 0.3 | |
Infected (persons) | | | |
Treated (persons) | | | |
HIV infection rate (persons | 3.85 | 5.794 | 7.816 |
New cases of AIDS (persons | 975.3 | 1062 | 1149 |
AIDS deaths (persons | 557.3 | 606.7 | 656.8 |