
A model of COVID-19 in an interconnected network of communities is studied. This model considers the dynamics of susceptible, asymptomatic and symptomatic individuals, deceased but not yet buried people, as well as the dynamics of the virus or pathogen at connected nodes or communities. People can move between communities carrying the virus to any node in the region of n communities (or patches). This model considers both virus direct (person to person) and indirect (contaminated environment to person) transmissions. Using either matrix and graph-theoretic methods and some combinatorial identities, appropriate Lyapunov functions are constructed to study global stability properties of both the disease-free and the endemic equilibrium of the corresponding system of 5n differential equations.
Citation: Jorge Rebaza. On a model of COVID-19 dynamics[J]. Electronic Research Archive, 2021, 29(2): 2129-2140. doi: 10.3934/era.2020108
[1] | Jorge Rebaza . On a model of COVID-19 dynamics. Electronic Research Archive, 2021, 29(2): 2129-2140. doi: 10.3934/era.2020108 |
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A model of COVID-19 in an interconnected network of communities is studied. This model considers the dynamics of susceptible, asymptomatic and symptomatic individuals, deceased but not yet buried people, as well as the dynamics of the virus or pathogen at connected nodes or communities. People can move between communities carrying the virus to any node in the region of n communities (or patches). This model considers both virus direct (person to person) and indirect (contaminated environment to person) transmissions. Using either matrix and graph-theoretic methods and some combinatorial identities, appropriate Lyapunov functions are constructed to study global stability properties of both the disease-free and the endemic equilibrium of the corresponding system of 5n differential equations.
Since the announcement of the novel virus SARS-CoV-2 affecting the population of Wuhan, China in December 2019, the disease caused by it, COVID-19, has extended from a small cluster in that region in China to almost every corner of the world. With globalization, countries are more interconnected than ever (with direct flights from Wuhan to the U.S. included), so human mobility is a factor that should be included in the mathematical modeling of this disease. Most current models of COVID-19 consider only one community or node (no human mobility), only direct or indirect transmission and do not include pathogen dynamics [10, 13, 18, 26]. These and other models represent in part an extension of previous research on other epidemics including cholera, as well as Zika and Ebola virus diseases [2, 6, 9, 12, 14, 17, 19, 21, 23].
One of the main goals in this type of studies is to establish conditions under which the disease is expected to persist or die out, and to study possible control measures. To this end, it is important to study conditions for the existence and local or global stability of the steady states in the model.
The model considered here includes human mobility across
As with similar mathematical models, conditions under which the disease is expected to spread or die out are related to the stability properties of the disease-free equilibrium point, and to the value of the basic reproduction number. Here we prove that for the corresponding system of
Researchers are still learning about the dynamics of COVID-19, which has been affecting regions and countries around the world, especially China, USA, Europe and Latin America. In this theoretical model, we assume that people can move with certain freedom between the
We let
S′i=Λi−[(1−ms)αip(Ei)+msn∑j=1Qijαjp(Ej)]Si−[(1−ms)βig(Vi)+msn∑j=1Qijβjg(Vj)]Si−μSiA′i=θ[(1−ms)αip(Ei)+msn∑j=1Qijαjp(Ej)]Si+θ[(1−ms)βig(Vi)+msn∑j=1Qijβjg(Vj)]Si−ϕiAiI′i=(1−θ)[(1−ms)αip(Ei)+msn∑j=1Qijαjp(Ej)]Si+(1−θ)[(1−ms)βig(Vi)+msn∑j=1Qijβjg(Vj)]Si−˜ϕiIiD′i=˜ξiIi−μDDiV′i=pi[(1−mI)(λAi+Ii)+˜λDi+n∑j=1mIQji(λAj+Ij)]−μVVi, | (1) |
where
Thus, according to model (1), susceptible individuals can get infected via direct or indirect transmission in any given community (including their own) where infected people or contaminated surfaces are present. If infected, they move to asymptomatic or symptomatic stage, from which they either recover (most of them) or die of natural or induced-disease causes. Infected individuals are able to shed the virus in their own community and in communities they visit.
Denoting
Γ={(S,A,I,D,V)∈R5n+:Ni≤Hi,Di≤Li,Vi≤MiμV,i=1,…n}. | (2) |
Observe that if we write system (1) as
System (1) has a unique disease-free equilibrium point (DFE):
E0=(S0i,A0i,I0i,D0i,V0i)=(Λiμ,0,0,0,0)∈R5n+. | (3) |
The system also has an endemic equilibrium point, which will be discussed in Section 4.
In this section, we establish conditions under which the disease will persist or die out. In addition to the roles played by the problem parameters and by the density or concentration of each population (people and virus), the mobility of infected people throughout the network also plays a key role. We start by studying the stability properties of the disease-free equilibrium (3).
To study global stability of the DFE, we can write system (1) as a compartmental model, by splitting the variables into two compartments: a disease compartment
x=[A1,…,An,I1,…In,D1,…,Dn,V1,…,Vn]T,andy=[S1,…,Sn].T |
For
Ri=(1−ms)αip(Ei)+msn∑j=1Qijαjp(Ej),Ti=(1−ms)βig(Vi)+msn∑j=1Qijβjg(Vj), |
and
F(x,y)=[θR1S1+θT1S1⋮θRnSn+θTnSn(1−θ)R1S1+(1−θ)T1S1⋮(1−θ)RnSn+(1−θ)TnSn˜ξ1I1⋮˜ξnInh1⋮hn],V(x,y)=[ϕ1A1⋮ϕnAn˜ϕ1I1⋮˜ϕnIn μDD1⋮μDDnμVV1⋮μVVn]. |
In general, one denotes with each entry
With this notation, model (1) can be split into disease/nondisease systems:
x′=F(x,y)−V(x,y),y′=G(x,y). | (4) |
Denote
F=[∂Fi∂xj(0,y0)],andV=[∂Vi∂xj(0,y0)],1≤i,j≤4n. | (5) |
Thus, the matrices
F=[θC1θC2θC3θC4(1−θ)C1(1−θ)C2(1−θ)C3(1−θ)C40˜D00C5C6˜τI0], |
V=[diag(ϕ1,…,ϕn)diag(˜ϕ1,…,˜ϕn)μDIμBI], |
where each
The basic reproduction number
Theorem 3.1. If
Proof. Following the matrix-theoretic method in [20], we set
f(x,y):=(F−V)x−F(x,y)+V(x,y). |
Thus,
[θR1(S01−S1)+θ[(1−mS)β1V1(S01K−S1K+V1)+mSn∑j=1Q1jβjVj(S01K−S1K+Vj)]⋮θRn(S0n−Sn)+θ[(1−mS)βnVn(S0nK−SnK+Vn)+mSn∑j=1QnjβjVj(S0nK−SnK+Vj)](1−θ)R1(S01−S1)+(1−θ)[(1−mS)β1V1(S01K−S1K+V1)+mSn∑j=1Q1jβjVj(S01K−S1K+Vj)]⋮(1−θ)Rn(S0n−Sn)+(1−θ)[(1−mS)βnVn(S0nK−SnK+Vn)+mSn∑j=1QnjβjVj(S0nK−SnK+Vj)]0⋮0]. |
Observe that
D=ωTV−1x |
is a Lyapunov function for (1) in
Now let
D′=ωTV−1x′=ωTV−1(F−V)x−ωTV−1f(x,y)=(R0−1)ωTx−ωTV−1f(x,y). |
Then,
Existence of an Endemic Equilibrium. In Theorem 3.1, we proved that if
E∗:=(S∗i,A∗i,I∗i,D∗i,V∗i)∈R5n+. | (6) |
In Theorem 4.2 below, we show that the condition
To study global stability of the EE (6), we use a graph-theoretic method as presented in [20]. First, we summarize some basic terminology and results from [16, 20] on directed graphs, including a technique for the construction of a Lyapunov function.
Given a weighted digraph
lij={−aij,i≠j,∑k≠iaik,i=j. |
Let
If
ciaij=p∑k=1cjajk. | (7) |
If
ciaij=p∑k=1ckaki. | (8) |
The following theorem provides a graph-theoretic technique to construct a Lyapunov function
Theorem 4.1. [16, 20]
For a given open set
˙z=f(z), | (9) |
and assume that
(i) There exist functions
Q′i=Q′i|(9)≤p∑j=1aijGij(z),withz∈E,i=1,…,p, |
(ii) Each directed cycle
∑(s,r)∈S(C)Grs(z)≤0,z∈E, |
where
Then, there exist constants
Q(z)=p∑i=1ciQi(z) |
satisfies
With these tools at hand, we can give a result on global stability of the endemic equilibrium of system (1). In the theorem below, we use the functions
bij={(1−mS)αi,j=imSQijαj,j≠i,cij={(1−mS)βi,j=imSQijβj,j≠i, |
and
Theorem 4.2. If
Proof. For
Qi=Si−S∗i−S∗iln(SiS∗i)+Ai−A∗i−A∗iln(AiA∗i)+Ii−I∗i−I∗iln(IiI∗i). |
Then, taking derivatives, and solving for
Q′i=(1−S∗iSi)S′i+(1−A∗iAi)A′i+(1−I∗iIi)I′i=(1−S∗iSi)[n∑j=1bij(S∗ip(E∗j)−Sip(Ej))+n∑j=1cij(S∗ig(V∗j)−Sig(Vj))−μ(Si−S∗i)]+(1−A∗iAi)[θn∑j=1bij(Sip(Ej)−S∗ip(E∗j)AiA∗i)+θn∑j=1cij(Sig(Vj)−S∗ig(V∗j)AiA∗i)]+(1−I∗iIi)[(1−θ)n∑j=1bij(Sip(Ej)−S∗ip(E∗j)IiI∗i)+(1−θ)n∑j=1cij(Sig(Vj)−S∗ig(V∗j)IiI∗i)]≤n∑j=1bijS∗ip(E∗j)[1−Sip(Ej)S∗ip(E∗j)−S∗iSi+p(Ej)p(E∗j)+θ (Sip(Ej)S∗ip(E∗j)−AiA∗i−Sip(Ej)S∗ip(E∗j)A∗iAi+1)+(1−θ)(Sip(Ej)S∗ip(E∗j)−IiI∗i−Sip(Ej)S∗ip(E∗j)I∗iIi+1)]+n∑j=1cijS∗ig(V∗j)[1−Sig(Vj)S∗ig(V∗j)−S∗iSi+g(Vj)g(V∗j)+θ (Sig(Vj)S∗ig(V∗j)−AiA∗i−Sig(Vj)S∗ig(V∗j)A∗iAi+1)+(1−θ)(Sig(Vj)S∗ig(V∗j)−IiI∗i−Sig(Vj)S∗ig(V∗j)I∗iIi+1)]=n∑j=1bijS∗ip(E∗j)[2−S∗iSi+p(Ej)p(E∗j)−(θAiA∗i+(1−θ)IiI∗i)−Sip(Ej)S∗ip(E∗j)(θA∗iAi+(1−θ)I∗iIi)]+n∑j=1cijS∗ig(V∗j)[2−S∗iSi+g(Vj)g(V∗j)−(θAiA∗i+(1−θ)IiI∗i)−Sig(Vj)S∗ig(V∗j)(θA∗iAi+(1−θ)I∗iIi)]≤n∑j=1bijS∗ip(E∗j)[lnSiS∗i+ln(S∗ip(E∗j)Sip(Ej)AiIiθA∗iIi+(1−θ)I∗iAi)+p(Ej)p(E∗j)−θI∗iAi+(1−θ)A∗iIiA∗iI∗i]+n∑j=1cijS∗ig(V∗j)[lnSiS∗i+ln(S∗ig(V∗j)Sig(Vj)AiIiθA∗iIi+(1−θ)I∗iAi)+g(Vj)g(V∗j)−θI∗iAi+(1−θ)A∗iIiA∗iI∗i]≤n∑j=1bijS∗ip(E∗j)[ln(θI∗iAi+(1−θ)A∗iIiA∗iI∗i)+p(E∗j)p(Ej)θI∗jAj+(1−θ)A∗jIjA∗jI∗j−1−ln(θI∗jAj+(1−θ)A∗jIjA∗jI∗j)+p(Ej)p(E∗j)−θI∗iAi+(1−θ)A∗iIiA∗iI∗i]+n∑j=1cijS∗ig(V∗j)[ln(θI∗iAi+(1−θ)A∗iIiA∗iI∗i)+g(V∗j)g(Vj)VjV∗j−1−lnVjV∗j+g(Vj)g(V∗j)−θI∗iAi+(1−θ)A∗iIiA∗iI∗i]=n∑j=1bijS∗ip(E∗j)[(p(Ej)p(E∗j)−1)(1−p(E∗j)p(Ej)θI∗jAj+(1−θ)A∗jIjA∗jI∗j)+ln(θI∗iAi+(1−θ)A∗iIiA∗iI∗i)−ln(θI∗jAj+(1−θ)A∗jIjA∗jI∗j)−θI∗iAi+(1−θ)A∗iIiA∗iI∗i+θI∗jAj+(1−θ)A∗jIjA∗jI∗j]+n∑j=1cijS∗ig(B∗j)[(g(Vj)g(V∗j)−1)(1−g(V∗j)Vjg(Vj)V∗j)+ln(θI∗iAi+(1−θ)A∗iIiA∗iI∗i)−lnVjV∗j−θI∗iAi+(1−θ)A∗iIiA∗iI∗i+VjV∗j]≤n∑j=1bijS∗ip(E∗j)[ln(θI∗iAi+(1−θ)A∗iIiA∗iI∗i)−ln(θI∗jAj+(1−θ)A∗jIjA∗jI∗j)−θI∗iAi+(1−θ)A∗iIiA∗iI∗i+θI∗jAj+(1−θ)A∗jIjA∗jI∗j]+n∑j=1cijS∗ig(V∗j)[ln(θI∗iAi+(1−θ)A∗iIiA∗iI∗i)−lnVjV∗j−θI∗iAi+(1−θ)A∗iIiA∗iI∗i+VjV∗j]=:2n∑j=1aijGij,aij={bijS∗ip(E∗j),1≤j≤ncijS∗ig(V∗j)n+1≤j≤2n, |
With corresponding functions
Qn+i=Vi−V∗i−V∗ilnV∗iVi, |
we get:
Q′n+i=h∗i[1−ViV∗i+hih∗i−hih∗iV∗iVi]≤h∗i[ln(h∗ihi(θAiA∗i+(1−θ)IiI∗i))−ln(θAiA∗i+(1−θ)IiI∗i)+lnViV∗i−ViV∗i+hih∗i]≤h∗i[h∗i(θI∗iAi+(1−θ)A∗iIi)hiA∗iI∗i−1−lnθI∗iAi+(1−θ)A∗iIiA∗iI∗i+lnViV∗i−ViV∗i+hih∗i]=h∗i[(hih∗i−1)(1−h∗i(θI∗iAi+(1−θ)A∗iIi)hiA∗iI∗i)+θI∗iAi+(1−θ)A∗iIiA∗iI∗i−lnθI∗iAi+(1−θ)A∗iIiA∗iI∗i−ViV∗i+lnViV∗i]≤h∗i[θI∗iAi+(1−θ)A∗iIiA∗iI∗i−lnθI∗iAi+(1−θ)A∗iIiA∗iI∗i−ViV∗i+lnViV∗i]=:an+i,iGn+i,i. |
With the coefficients
Then, we can let
Q:=n∑i=1ciQi+n∑i=1cn+iQn+i, |
for some constants
Q′≤n∑i=1cin∑j=1aijGij+n∑i=1cin∑j=1ai,n+jGi,n+j+n∑i=1n∑j=1cjaj,n+iGn+i,i=n∑i=1n∑j=1ci(aij+ai,n+j)[ln(θI∗iAi+(1−θ)A∗iIiA∗iI∗i)−ln(θI∗jAj+(1−θ)A∗jIjA∗jI∗j)−θI∗iAi+(1−θ)A∗iIiA∗iI∗i+θI∗jAj+(1−θ)A∗jIjA∗jI∗j]. |
One can let
˜Q′≤n∑i=1n∑j=1˜ci˜aij[ln(θI∗iAi+(1−θ)A∗iIiA∗iI∗i)−ln(θI∗jAj+(1−θ)A∗jIjA∗jI∗j)−θI∗iAi+(1−θ)A∗iIiA∗iI∗i+θI∗jAj+(1−θ)A∗jIjA∗jI∗j]=0. |
In addition, one can show that the endemic equilibrium
A general theoretical model of COVID-19 dynamics has been proposed. The model considers the connection and interaction between people from
For the particular case of the U. S., after about five months of the first case reported, the number of asymptomatic people seems to be much larger of that of symptomatic, and they seem to be the group most responsible for recent spikes in the number of infections and spreading of the disease. This is probably explained in part because of the inconsistent protocols about reopening the country, with strategies varying very much from state to state and from county to county, ignoring the role that connectivity and human mobility across counties and states play in the persistence of the disease.
Using matrix and graph-theoretic techniques from [16, 20], it was shown that when the basic reproduction number
Model (1) includes the following cases: If all
This work builds on previous research on modeling and analyzing mathematical models of COVID-19 and other disease epidemics, including those in [2, 5, 9, 8, 10, 13, 15, 16, 21, 26]. Some extensions and generalizations of this model are possible for future research, such as splitting infectious people into tested (or detected) and non-tested (not detected), isolated and not isolated, adding the exposed stage into the dynamics, and possibly also splitting symptomatic into those with mild and those with severe symptoms.
I would like to thank the anonymous referees for their valuable feedback and suggestions, which led to the improvement of this article.
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