Citation: Shan-Yang Lin. Salmon calcitonin: conformational changes and stabilizer effects[J]. AIMS Biophysics, 2015, 2(4): 695-723. doi: 10.3934/biophy.2015.4.695
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Since the publication of the seminal paper [11] mathematical compartmental models are widely used to describe infectious diseases dymanics in large populations (see, for example [9], [4], [2] and [8]). It is well accepted that once an infected individual comes into contact with an unaffected population, the disease will spread by contact with the infectious individuals. Compartmental models divide the population into compartments characterizing the spread of the diseases and letters are used to denote the number of individuals in each compartment. Usually, the size of the population to be studied is
The basic reproduction number,
Many papers on optimal control applied to epidemiology propose
In this paper we focus on optimal control problems to control, via vaccination, the spread of a disease described by a SEIR model. We follow closely the approach in [16]: we consider
The normalized SEIR model differs from the usual SEIR model since the variables are fractions of the whole population instead of the number of individuals in each compartment. The theoretical and numerical treatment involving the latter model is usually done as if the variables are continuous and not integers; treating such variables as integers would demand the use of integer programming what is known to be very heavy computationally. When we turn to normalized models the variables are, by nature, continuous. In the literature, normalized models are common when the total population is assumed to remain constant during the time frame under study. This is not our case; here we normalize a SEIR model that incorporates exponential natural birth and death, as well as disease-caused death (similarly to what is done in [14]). As far as optimal control is concerned, normalizing such model brings out some new issues related to the choice of costs and the introduction of non standard constraints, questions we discuss here when comparing optimal control for normalized and not normalized SEIR models.
Herein, we refer to the SEIR model, where the variables
We emphasize that we do not concentrate on any particular disease. Rather, our aim is to illustrate how previously proposed optimal control formulations can be handled by this new model, when different scenarios are considered. Taking into account that the set of parameters for the population in [16], based on [17], are not to be found in today's world, we use different population's parameters closed related to some European countries.
Like other models in epidemiology, SEIR models represent only a rough approximation of reality. However, they provide new insights into the spreading of diseases and, when optimal control is applied, new insight on different vaccination policies.
This paper is organized in the following way. In Section 2 we introduce an optimal control problem with
The SEIR model is a compartmental model well accepted as modelling some infectious diseases. At each instant
Optimal control techniques for SEIR models allow the study of different vaccines policies; different policies are confronted in [17] and [1] where the minimizing cost is
$
˙S(t)=bN(t)−dS(t)−cS(t)I(t)N(t)−u(t)S(t),S(0)=S0,
$
|
(1) |
$
˙E(t)=cS(t)I(t)N(t)−(f+d)E(t),E(0)=E0,
$
|
(2) |
$
˙I(t)=fE(t)−(g+a+d)I(t),I(0)=I0,
$
|
(3) |
$
˙N(t)=(b−d)N(t)−aI(t),N(0)=N0,
$
|
(4) |
where
For some
$ \label{control-constraint} 0 \leq u(t) \leq \bar u \;\;\;\; a.e. \; t \in [0,T] , $ | (5) |
where
$ \label{Rdifeq} \dot R(t) = g I(t)-dR(t)+u(t)S(t) , \;\;\;\; R(0) = R_0 . $ | (6) |
Here, the aim of applying optimal control to SEIR models is to control the spreading of the disease with some minimum financial cost. The cost should then be a weighted sum of the society financial costs of having, at each time,
$ \label{L1-objective} J_C(X,u)= \int_0^{T} \left( AI(t)+Bu(t)\right)~dt, $ | (7) |
where
Throughout this paper we refer to the optimal control problem of minimizing
$\nonumber
(P) \left\{
Minimize∫T0(AI(t)+Bu(t)) dtsubject to˙S(t)=bN(t)−dS(t)−cS(t)I(t)N(t)−u(t)S(t),S(0)=S0,˙E(t)=cS(t)I(t)N(t)−(f+d)E(t),E(0)=E0,˙I(t)=fE(t)−(g+a+d)I(t),I(0)=I0,˙N(t)=(b−d)N(t)−aI(t),N(0)=N0,u(t)∈[0,ˉu] for a. e.t∈[0,T], with ˉu∈]0,1].
\right.
\nonumber
$
|
Next, we associate
$ s(t)=\frac{S(t)}{N(t)}, \;\;\;\; e(t)=\frac{E(t)}{N(t)},\;\;\;\; i(t)=\frac{I(t)}{N(t)}, \;\;\;\; r(t)=\frac{R(t)}{N(t)} , $ | (8) |
we have
$ s(t)+e(t)+i(t)+r(t)=1\text{ for all }t. $ | (9) |
Notice that
$
˙s(t)=b−cs(t)i(t)−bs(t)+ai(t)s(t)−u(t)s(t),
$
|
(10) |
$
˙e(t)=cs(t)i(t)−(f+b)e(t)+ai(t)e(t),
$
|
(11) |
$
˙i(t)=fe(t)−(g+a+b)i(t)+ai2(t),
$
|
(12) |
$
˙r(t)=gi(t)−rb(t)+ai(t)r(t)+u(t)s(t).
$
|
(13) |
Remarkably, the dead rate parameters do not appear in this model (a feature we discuss in Remark 1 below). It is a simple matter to see that due to (9) we can discard equation (13), allowing us to reduce the number of differential equations from the normalized SEIR model (10)-(13).
Now we are faced with the choice of the cost for the normalized model. Taking into account that the main aim is to control or to eliminate the disease from the population under study, different costs is may be considered, reflecting different concerns.
The choice of the cost for
An almost straightforward translation of this reasoning to our normalized model yields
We postpone this discussion of the introduction of different costs to future research and we proceed now with the cost
$\nonumber
(P_{n}) \left\{
Minimize∫T0(ρi(t)+u(t)) dtsubject to˙s(t)=b−cs(t)i(t)−bs(t)+ai(t)s(t)−u(t)s(t),s(0)=s0,˙e(t)=cs(t)i(t)−(f+b)e(t)+ai(t)e(t),e(0)=e0,˙i(t)=fe(t)−(g+a+b)i(t)+ai2(t),i(0)=i0,u(t)∈[0,ˉu] for a. e.t∈[0,T], with ˉu∈]0,1].
\right.
\nonumber
$
|
Note that the dynamics is of the form
Remark 1. A word of caution regarding the way the system (10)-(13) is viewed. We cannot interpret the dynamics between these new compartments in the same way as with the classical model. Indeed, in equation (10) the term
We will discuss pros and cons of
Optimal control problems can be solved numerically by direct or indirect methods. Here, we opt to use the direct method (for a description these two methods see, for example, [19]): first the problem is discretized and the subsequent optimization problem is then solved using software packages with large scale nonlinear continuous optimization solvers. In this work all the simulations were made with the Applied Modelling Programming Language (AMPL), developed by [7], and interfaced to the Interior-Point optimization solver IPOPT, developed by [21]. Alternatively, the optimization solver WORHP (see [3]) can also be interfaced with AMPL. We refer the reader to [16] and references within for more information on software for optimal control problems.
The application of the Maximum Principle to problems in the form of
In all the computations we consider the time horizon to be 20 years: thus
Parameter | Description | Value |
b | Natural birth rate | 0.01 |
d | Death rate | 0.0099 |
c | Incidence coefficient | 1.1 |
f | Exposed to infectious rate | 0.5 |
g | Recovery rate | 0.1 |
a | Disease induced death rate | 0.2 |
T | Number of years | 20 |
Parameter | Description | Value |
A | weight parameter | 1 |
B | weight parameter | 2 |
S0 | Initial susceptible population | 1000 |
E0 | Initial exposed population | 100 |
I0 | Initial infected population | 50 |
R0 | Initial recovered population | 15 |
N0 | Initial population | 1165 |
Parameter | Description | Value |
s0 | Percentage of initial susceptible population | 0.858 |
e0 | Percentage of initial exposed population | 0.086 |
i0 | Percentage of initial infected population | 0.043 |
The problem
Although the two problem
Clearly, the reason why
When considering problem
$ u(t)S(t)\leq V_0, \label{eq:restricao} $ | (14) |
can be mathematically translated to normalized models but they loose their meaning. However, this drawback may be overcome by considering
We now focus on the Maximum Principle for the problem
$H(x,\;p,\;u)=pf(x)+pg(x)u-\lambda(\rho i+u),$ |
for appropriated
(ⅰ)
(ⅱ)
(ⅲ)
(ⅳ)
Since
It is a simple matter to see that condition (ⅲ) is equivalent to
$ \phi (t)u^*(t)=\displaystyle\max\limits_u \{\phi (t)u(t)\::\: 0\leq u \leq \bar{u}\}. $ | (15) |
It follows that
$
u^*(t)= \left\{
ˉu, if ϕ(t)>0,0, if ϕ(t)<0,singular, if ϕ(t)=0.
\right.
\label{eq:sis_sing_sem_restr}
$
|
(16) |
In terms of the data of
$ -\dot{p}_s(t)\;\;\;\; = (ai(t)-ci(t)-b-u(t))p_s(t)+ci(t)p_e(t), $ | (17) |
$ -\dot{p}_e(t) \;\;\;\;= (ai(t)-b-f)p_e(t)+fp_i(t), $ | (18) |
$ -\dot{p}_i(t) \;\;\;\; = (as(t)-cs(t))p_s(t)+(cs(t)+ae(t))p_e(t)+\nonumber \\ \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;(2ai(t)-a-b-g)p_i(t)-\rho. $ | (19) |
Also, we have
$ \phi(t)=-1-p_s(t)s(t). $ | (20) |
Since our computations show that a singular arc appear, let us assume that
1Since the initial condition belong to the interior of
${ \mathcal{R}}:=\left\{(s,e,i)\in\mathbb{R}^3:~s\geq 0,~e\geq 0, ~ i\geq 0\right\}$ |
and so we deduce that
$\phi(t)=0 \text{ implies that }p_s(t) = -\frac{1}{s(t)}< 0.$ |
In the interior of the singular interval we have
$\dfrac{d\phi}{dt}=cs(t)i(t)p_e(t)-bp_s(t)=0$ |
implies that that
$
d2ϕdt=aci(t)2pe(t)s(t)−aci(t)pe(t)s(t)−bci(t)pe(t)s(t)+cfe(t)pe(t)s(t)−cgi(t)pe(t)s(t)−c2i(t)2pe(t)s(t)−ci(t)pe(t)s(t)u(t)+2bci(t)pe(t)+cfi(t)pe(t)s(t)−cfi(t)s(t)pi(t)+abi(t)ps(t)−bci(t)ps(t)−b2ps(t)−bps(t)u(t),
$
|
(21) |
depends on the control variable
$\dfrac{d }{d u}\left(\dfrac{d^2 \phi}{d t}\right)=-ci(t)p_e(t)s(t)-bp_s(t)>0.$ |
Thus the strict Generalized Legendre-Clebsch Condition (GLC) holds and we can solve
$
using(x,p)=c(−c+a)s(t)i(t)2pe(t)ci(t)pe(t)s(t)+bps(t)+cefs(t)pe(t)−b2ps(t)ci(t)pe(t)s(t)+bps(t)+((((−a−b+f−g)s(t)+2b)pe(t)−fs(t)pi(t)−bps(t))c+abps(t))i(t)ci(t)pe(t)s(t)+bps(t)
$
|
(22) |
It is important to observe that the above expression for singular controls depends on the multipliers. Since we do not establish that the multipliers are unique, we can only expect to use (22) to validate numerical findings but not to prove to optimality. In fact, to prove optimality of computed solution we need to check numerically sufficient conditions. Unfortunately, there are no numerically verifiable sufficient conditions for problems with singular arcs.
We now present and discuss the results of our simulations for
● Case 1:
● Case 2:
● Case 3:
In the first two cases the computed optimal control exhibits a bang-singular-bang structure while in the last one the optimal control is bang-bang. For all the three cases we present graphs with the computed controls and trajectories. As in [16] and to keep the exposition short, we do not present the graphs of the multipliers but we give their computed initial values, and we also present the final states, the costs and the switching times
Case 1. Taking
Numerical results for Case 1:
$
s(T)=0.095341,e(T)=0.00051104,i(T)=0.0020380,ps(0)=−126.5,pe(0)=−2253,pi(0)=−3219.
$
|
Case 2. The results of the simulations are shown in figures 6 and 7. In figure 6 the optimal control,
Numerical results for case 2:
$
s(T)=0.16598,e(T)=0.0033060,i(T)=0.0079433,ps(0)=−377.0,pe(0)=−5137,pi(0)=−7081.
$
|
When we go from case 1 to case 2, the optimal control goes from singular to bang-bang. This is because when we decrease the value of
Case 3. While keeping
Numerical results for case 3:
$
s(T)=0.071623,e(T)=0.0031999,i(T)=0.015275,ps(0)=−1025,pe(0)=−4692,pi(0)=−6872.
$
|
If the control is bang-bang as in case 2 and 3 second order sufficient conditions may be checked numerically as described in [15] and [16]. Here we refrain from engaging in such discussion to keep the exposition short. Here we compute numerically the switching times using the so called induced optimization problem as in [15]. Recall that the switching times are the points
Implementing the induced optimization problem with AMPL for case 2, with
For case 3, with
We now turn to case 1 where the computed control
We studied the optimal control of an epidemiological normalized SEIR model using a
Moreover, we confronted this problem with the one previously studied in [16] where the so called classical SEIR model is used. The normalized model may cover in one single problem populations of different size and it is defined with what may be seen as a more realistic cost. Because of the use of normalized model, the solution of
The authors would like to thank Prof. Helmut Maurer for numerous and enlightening discussions on this topic as well as his help in writing up AMPL codes for the problems reported here. Thanks are due to the anonymous referees whose many comments and suggestions greatly improved this paper.
The financial support of FEDER funds through COMPETE and Portuguese funds through the Portuguese Foundation for Science and Technology (FCT), within the FCT project PTDC/EEI-AUT/1450/2012—FCOMP-01-0124-FEDER-028894, PTDC/EEI-AUT/2933/2014, TOCCATTA -funded by FEDER funds through COMPETE2020 -POCI and FCT as well as POCI-01-0145-FEDER-006933 -SYSTEC -funded by FEDER funds through COMPETE2020 – Programa Operacional Competitividade e Internacionalização (POCI) – and by national funds through FCT -Fundação para a Ciência e a Tecnologia, are gratefully acknowledged.
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Parameter | Description | Value |
b | Natural birth rate | 0.01 |
d | Death rate | 0.0099 |
c | Incidence coefficient | 1.1 |
f | Exposed to infectious rate | 0.5 |
g | Recovery rate | 0.1 |
a | Disease induced death rate | 0.2 |
T | Number of years | 20 |
Parameter | Description | Value |
A | weight parameter | 1 |
B | weight parameter | 2 |
S0 | Initial susceptible population | 1000 |
E0 | Initial exposed population | 100 |
I0 | Initial infected population | 50 |
R0 | Initial recovered population | 15 |
N0 | Initial population | 1165 |
Parameter | Description | Value |
s0 | Percentage of initial susceptible population | 0.858 |
e0 | Percentage of initial exposed population | 0.086 |
i0 | Percentage of initial infected population | 0.043 |
Parameter | Description | Value |
b | Natural birth rate | 0.01 |
d | Death rate | 0.0099 |
c | Incidence coefficient | 1.1 |
f | Exposed to infectious rate | 0.5 |
g | Recovery rate | 0.1 |
a | Disease induced death rate | 0.2 |
T | Number of years | 20 |
Parameter | Description | Value |
A | weight parameter | 1 |
B | weight parameter | 2 |
S0 | Initial susceptible population | 1000 |
E0 | Initial exposed population | 100 |
I0 | Initial infected population | 50 |
R0 | Initial recovered population | 15 |
N0 | Initial population | 1165 |
Parameter | Description | Value |
s0 | Percentage of initial susceptible population | 0.858 |
e0 | Percentage of initial exposed population | 0.086 |
i0 | Percentage of initial infected population | 0.043 |