Meme transmission has become an important way of information dissemination. Three transfer paths were added to the classic infectious disease storehouse model in this study based on characteristics of meme transmission. Individual heterogeneity factors such as individual interest, risk perception and trust perception were used to construct a meme transmission model named Individual Heterogeneity SEIR (IHSEIR) model. Equilibrium of the model and the basic reproduction number were obtained using mean-field theory. Effects of individual heterogeneity factors on meme propagation were analyzed through Multi-Agent simulation. The findings showed that individual interest has a significant effect on the propagation range and speed of meme. A low-level overall trust of the system was correlated with higher risk perception among individuals, which is not conducive for the propagation of meme. Effect of regulation and intervention in the process of meme transmission was significantly lower compared with that at the initial state of transmission.
Citation: Jun Zhai, Bilin Xu. Research on meme transmission based on individual heterogeneity[J]. Mathematical Biosciences and Engineering, 2021, 18(5): 5176-5193. doi: 10.3934/mbe.2021263
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Meme transmission has become an important way of information dissemination. Three transfer paths were added to the classic infectious disease storehouse model in this study based on characteristics of meme transmission. Individual heterogeneity factors such as individual interest, risk perception and trust perception were used to construct a meme transmission model named Individual Heterogeneity SEIR (IHSEIR) model. Equilibrium of the model and the basic reproduction number were obtained using mean-field theory. Effects of individual heterogeneity factors on meme propagation were analyzed through Multi-Agent simulation. The findings showed that individual interest has a significant effect on the propagation range and speed of meme. A low-level overall trust of the system was correlated with higher risk perception among individuals, which is not conducive for the propagation of meme. Effect of regulation and intervention in the process of meme transmission was significantly lower compared with that at the initial state of transmission.
Cholera is an acute diarrhoeal illness caused by infection of the intestine with bacterium vibrio cholerae, which lives in an aquatic organism [16]. The ingestion of contaminated water can cause cholera outbreaks, as John Snow proved in 1854 [24]. This is a possibility of transmission of the disease, but others do exist. For example, susceptible individuals can become infected if they become in contact with contaminated people. When individuals are at an increased risk of infection, then they can transmit the disease to those who live with them, by reflecting food preparation or using water storage containers [24]. An individual can be infected with or without symptoms and some of these can be watery diarrhea, vomiting and leg cramps. If contaminated individuals do not get treatment, then they become dehydrated, suffering acidosis and circulatory collapse. This situation can lead to death within 12 to 24 hours [18,24]. Some studies and experiments suggest that a recovered individual can be immune to the disease during a period of 3 to 10 years. Nevertheless, recent researches conclude that immunity can be lost after a period of weeks to months [19,24].
Since 1979, several mathematical models for the transmission of cholera have been proposed: see, e.g., [2,3,8,9,11,14,16,17,18,19,22,24] and references cited therein. In [19], the authors propose a SIR (Susceptible-Infectious-Recovered) type model and consider two classes of bacterial concentrations (hyperinfectious and less-infectious) and two classes of infectious individuals (asymptomatic and symptomatic). In [24], another SIR-type model is proposed that incorporates hyperinfectivity (where infectivity varies with time since the pathogen was shed) and temporary immunity, using distributed delays. The authors of [18] incorporate in a SIR-type model public health educational campaigns, vaccination, quarantine and treatment, as control strategies in order to curtail the disease.
Between 2007 and 2018, several cholera outbreaks occurred, namely in Angola, Haiti, Zimbabwe and Yemen [1,24,31]. Mathematical models have been developed and studied in order to understand the transmission dynamics of cholera epidemics, mostly focusing on the epidemic that occurred in Haiti, 2010–2011 [1]. In [16], a SIQR (Susceptible-Infectious-Quarantined-Recovered) type model, which also considers a class for bacterial concentration, is analysed, being shown that it fits well the cholera outbreak in the Department of Artibonite - Haiti, from 1
Recently, the biggest outbreak of cholera in the history of the world has occurred in Yemen [26]. The epidemic began in October 2016 and in February-March 2017 was in decline. However, on 27
Following World Health Organization (WHO) recommendations, the first-ever oral vaccination campaign against cholera had been launched on 6
In this paper, we propose a SITRV (Susceptible-Infectious-Treated-Recovered-Vaccinated) type model, which includes a class of bacterial concentration. In Section 2, we formulate and explain the mathematical model. Then, in Section 3, we show that the model is mathematically well posed and it has biological meaning. We prove the existence and uniqueness of the disease-free and endemic equilibrium points and compute the basic reproduction number. The sensitivity of the basic reproduction number with respect to all parameters of the model is analysed in Subsection 3.3 and the stability analysis of equilibria is carried out in Subsection 3.4. In Section 4, we show that the model fits well the cholera outbreak in Yemen, between 27
We trust that our work is of great significance, because it provides a mathematical model for cholera that is deeply studied and allows to obtain important conclusions about the relevance of vaccination campaigns in cholera outbreaks. We show that, if it had existed a vaccination campaign from the beginning of the outbreak in Yemen, then the epidemic would have been extinguished. Actually, we believe that the absence of this type of prevention measures in Yemen, was one of the responsible for provoking the biggest cholera outbreak in world's history [26], killing 2 275 individuals until 15
We modify the model studied in [16], adding a vaccination class and considering different kinds of cholera's treatment. The model is a SITRV (Susceptible-Infectious-Treated-Recovered-Vaccinated) type model and considers a class of bacterial concentration for the dynamics of cholera. The total human population
{S′(t)=Λ−βB(t)κ+B(t)S(t)+ω1R(t)+ω2V(t)−(φ+μ)S(t),I′(t)=βB(t)κ+B(t)S(t)−(δ+α1+μ)I(t),T′(t)=δI(t)−(ε+α2+μ)T(t),R′(t)=εT(t)−(ω1+μ)R(t),V′(t)=φS(t)−(ω2+μ)V(t),B′(t)=ηI(t)−dB(t). | (2.1) |
Throughout the paper, we assume that the initial conditions of system (2.1) are non-negative:
S(0)=S0≥0, I(0)=I0≥0, T(0)=T0≥0, R(0)=R0≥0, V(0)=V0≥0, B(0)=B0≥0. | (3.1) |
Our first lemma shows that the considered model (2.1)-(3.1) is biologically meaningful.
Lemma 1. The solutions
Proof. We have
{dS(t)dt|ξ(S)=Λ+ω1R(t)+ω2V(t)>0,dI(t)dt|ξ(I)=βB(t)κ+B(t)S(t)≥0,dT(t)dt|ξ(T)=δI(t)≥0,dR(t)dt|ξ(R)=εT(t)≥0,dV(t)dt|ξ(V)=φS(t)≥0,dB(t)dt|ξ(B)=ηI(t)≥0, |
where
Lemma 2 shows that it is enough to consider the dynamics of the flow generated by (2.1)-(3.1) in a certain region
Lemma 2. Let
ΩH={(S,I,T,R,V)∈(R+0)5|0≤S(t)+I(t)+T(t)+R(t)+V(t)≤Λμ} | (3.2) |
and
ΩB={B∈R+0|0≤B(t)≤Λημd}. | (3.3) |
Define
ΩV=ΩH×ΩB. | (3.4) |
If
Proof. Let us split system (2.1) into two parts: the human population, i.e.,
N′(t)=S′(t)+I′(t)+T′(t)+R′(t)+V′(t)=Λ−μN(t)−α1I(t)−α2T(t)≤Λ−μN(t). |
Assuming that
From now on, let us consider that
E0=(S0,I0,T0,R0,V0,B0)=(Λa4a0a4−φω2,0,0,0,Λφa0a4−φω2,0). | (3.5) |
Remark 1. Note that, because
Next, following the approach of [18,27], we compute the basic reproduction number
Proposition 3(Basic reproduction number of (2.1)). The basic reproduction number of model (2.1) is given by
R0=βΛηa4(a0a4−φω2)κda1. | (3.6) |
Proof. Consider that
F(t)=[0βB(t)S(t)κ+B(t)0000],V+(t)=[Λ+ω1R(t)+ω2V(t)0δI(t)εT(t)φS(t)ηI(t)] and V−(t)=[βB(t)S(t)κ+B(t)+a0S(t)a1I(t)a2T(t)a3R(t)a4V(t)dB(t)]. |
Therefore, by considering
[S′(t)I′(t)T′(t)R′(t)V′(t)B′(t)]T=F(t)−V(t). |
The Jacobian matrices of
F=[000000βB(t)κ+B(t)0000βκS(t)(κ+B(t))2000000000000000000000000] and V=[βB(t)κ+B(t)+a000−ω1−ω2βκS(t)(κ+B(t))20a100000−δa200000−εa300−φ000a400−η000d]. |
In the disease-free equilibrium
F0=[00000000000βΛa4(a0a4−φω2)κ000000000000000000000000] and V0=[a000−ω1−ω2βΛa4(a0a4−φω2)κ0a100000−δa200000−εa300−φ000a400−η000d]. |
The basic reproduction number of model (2.1) is then given by
R0=ρ(F0V−10)=βΛηa4(a0a4−φω2)κda1, |
found with the help of the computer algebra system
Now we prove the existence of an endemic equilibrium when
Proposition 4 (Endemic equilibrium). If the basic reproduction number (3.6) is such that
E∗=(S∗,I∗,T∗,R∗,V∗,B∗), | (3.7) |
where
{S∗=a1a4{κd(a1a2a3−δεω1)+Ληa2a3}η˜D,I∗=a2a3{βΛηa4−(a0a4−φω2)κda1}η˜D,T∗=a3δ{βΛηa4−(a0a4−φω2)κda1}η˜D,R∗=δε{βΛηa4−(a0a4−φω2)κda1}η˜D,V∗=a1φ{κd(a1a2a3−δεω1)+Ληa2a3}η˜D,B∗=a2a3{βΛηa4−(a0a4−φω2)κda1}d˜D |
and
Proof. We note that
1.
2.
3.
4.
5.
6.
7.
With the above inequalities, we conclude that
βΛηa4=R0(a0a4−φω2)κda1⇔ βΛηa4−(a0a4−φω2)κda1=R0(a0a4−φω2)κda1−(a0a4−φω2)κda1⇔ βΛηa4−(a0a4−φω2)κda1=(a0a4−φω2)κda1(R0−1). |
Therefore, we have that
{I∗=a1a2a3κd(a0a4−φω2)(R0−1)η˜D,T∗=a1a3κdδ(a0a4−φω2)(R0−1)η˜D,R∗=a1κdδε(a0a4−φω2)(R0−1)η˜D,B∗=a1a2a3κ(a0a4−φω2)(R0−1)˜D. |
In order to obtain an endemic equilibrium, we have to ensure that
In this section, we are going to study the sensitivity of
Parameter | |
| 1 |
| |
| 1 |
| -1 |
| 0 |
| |
| |
| |
| 0 |
| |
| 0 |
| 1 |
| -1 |
Definition 1 (See [6,15,25]). The normalized forward sensitivity index of a variable
Υzp=∂z∂p×p|z|. |
Remark 2. When a parameter
Now we prove the local stability of the disease-free equilibrium
Theorem 5 (Stability of the DFE (3.5)). The disease-free equilibrium
1. locally asymptotic stable, if
2. unstable, if
Moreover, if
Proof. The characteristic polynomial associated with the linearised system of model (2.1) is given by
pV(χ)=det(F0−V0−χI6). |
In order to compute the roots of the polynomial
|−a0−χ00ω1ω2−βΛa4(a0a4−φω2)κ0−a1−χ000βΛa4(a0a4−φω2)κ0δ−a2−χ00000ε−a3−χ00φ000−a4−χ00η000−d−χ|=0, |
that is,
χ2+(a0+a4)χ+(a0a4−φω2)=0∨χ2+(a1+d)χ+a1d−βΛηa4(a0a4−φω2)κ=0∨χ=−a2∨χ=−a3. |
As the coefficients of polynomial
χ2+(a1+d)χ+a1d−βΛηa4(a0a4−φω2)κ |
have negative real part if and only if
a1d−βΛηa4(a0a4−φω2)κ>0⇔R0<1. |
Therefore, the DFE
We end this section by proving the local stability of the endemic equilibrium
Theorem 6 (Local asymptotic stability of the endemic equilibrium (3.7)). The endemic equilibrium
Proof. In order to apply the method described in Theorem 4.1 of [5], we are going to do the following change of variables. Let us consider that
X=(x1,x2,x3,x4,x5,x6)=(S,I,T,R,V,B). |
Consequently, we have that the total number of individuals is given by
{x′1(t)=f1=Λ−βx6(t)κ+x6(t)x1(t)+ω1x4(t)+ω2x5(t)−a0x1(t),x′2(t)=f2=βx6(t)κ+x6(t)x1(t)−a1x2(t),x′3(t)=f3=δx2(t)−a2x3(t),x′4(t)=f4=εx3(t)−a3x4(t),x′5(t)=f5=φx1(t)−a4x5(t),x′6(t)=f6=ηx2(t)−dx6(t). | (3.8) |
Choosing
β∗=(a0a4−φω2)κda1Ληa4. |
Considering
J∗0=[−a000ω1ω2−a1dη0−a1000a1dη0δ−a200000ε−a300φ000−a400η000−d]. |
The eigenvalues of
det(J∗0−χI6)=0⇔ χ=0 ∨ χ=−a1−d ∨ χ=−a2 ∨ χ=−a3 ∨ χ=−12(a0+a4±√(a0−a4)2+4φω2). |
Note that the eigenvalue
−12(a0+a4−√(a0−a4)2+4φω2)=−12(φ+μ+ω2+μ−√(φ+μ−ω2−μ)2+4φω2)=−12(φ+ω2+2μ−√φ2−2φω2+ω22+4φω2)=−12(φ+ω2+2μ−√(φ+ω2)2)=φ+ω2≥0−12(φ+ω2+2μ−(φ+ω2))=−μ<0. |
Therefore, we can conclude that a simple eigenvalue of
w=[w1 w2 w3 w4 w5 w6]T and v=[v1 v2 v3 v4 v5 v6], |
given by
w=[−a4(a1a2a3−δεω1)a2a3(a0a4−φω2) 1 δa2 δεa2a3 φa4 ηd]Tw2 |
and
v=[0 1 0 0 0 a1η]v2. |
Remember that
a=6∑i,j,k=1wiwjvk[∂2fk∂xi∂xj(E0)]β=β∗ and b=6∑i,k=1wivk[∂2fk∂xi∂ϕ(E0)]β=β∗ |
with
[∂2f2∂x1∂x6(E0)]β=β∗=[∂2f2∂x6∂x1(E0)]β=β∗=β∗κ and [∂2f2∂x26(E0)]β=β∗=−2β∗Λa4a0a4−φω2. |
Therefore, the constant
a=−2β∗ηa4d(a0a4−φω2)(a1a2a3−δεω1a2a3κ+Ληd)v2w22<0. |
Furthermore, we have that
b=v2w6[∂2f∂x6∂ϕ(E0)]β=β∗=Ληa4κd(a0a4−φω2)v2w2>0. |
Thus, as
{a<0b>0ϕ=β−β∗=a1κd(a0a4−φω2)Ληa4(R0−1)>0⇔{a<0b>0R0>1, |
we conclude from Theorem 4.1 in [5] that the endemic equilibrium
In this section, we simulate the worst cholera outbreak that ever occurred in human history. It occurred in Yemen, from 27
Parameter | Description | Value | Reference |
| Recruitment rate | 28.4 | [12] |
| Natural death rate | 1.6 | [13] |
| Ingestion rate | 0.01694 (day | Assumed |
| Half saturation constant | | Assumed |
| Immunity waning rate | 0.4/365 (day | [19] |
| Efficacy vaccination waning rate | 1/1 460 (day | [7] |
| Vaccination rate | 5/1 000 (day | Assumed |
| Treatment rate | 1.15 (day | Assumed |
| Recovery rate | 0.2 (day | [18] |
| Death rate (infected) | 6 | [13,31] |
| Death rate (in treatment) | 3 | Assumed |
| Shedding rate (infected) | 10 (cell/ml day | [3] |
| Bacteria death rate | 0.33 (day | [3] |
| Susceptible individuals at | 28 249 670 (person) | [34] |
| Infected individuals at | 750 (person) | [31] |
| Treated individuals at | 0 (person) | Assumed |
| Recovered individuals at | 0 (person) | Assumed |
| Vaccinated individuals at | 0 (person) | [30] |
| Bacterial concentration at | | Assumed |
ΥR0φ=−φφ+ω2+μ≃−0.877050<0. |
Furthermore, for
(S∗,I∗,T∗,R∗,V∗,B∗)=(2.943350×107, 1.035599×105, 5.954131×105, 1.070992×108, 0, 3.138180×106) |
and for
(S0,I0,T0,R0,V0,B0)=(1.689119×107, 0, 0, 0, 1.204910×108, 0). |
Note that the previous figures correspond to the equilibrium points for the parameter values of Table 2, which can be obtained numerically for a final time of approximately 1 370 years. We also call attention to the fact that the recruitment rate
In this paper, we proposed and analysed, analytically and numerically, a SITRVB model for cholera transmission dynamics. In order to fit the biggest cholera outbreak worldwide, which has occurred very recently in Yemen, we simulated the outbreak of Yemen without vaccination. Indeed, vaccination did not exist in Yemen from 27
This research was supported by the Portuguese Foundation for Science and Technology (FCT) within projects UID/MAT/04106/2013 (CIDMA), and PTDC/EEI-AUT/2933/2014 (TOCCATA), funded by Project 3599 - Promover a Produção Científica e Desenvolvimento Tecnológico e a Constituição de Redes Temáticas, and FEDER funds through COMPETE 2020, Programa Operacional Competitividade e Internacionalização (POCI). Lemos-Paião is also supported by the FCT Ph.D. fellowship PD/BD/114184/2016, Silva by the postdoctoral grant SFRH/BPD/72061/2010.
The authors are very grateful to an anonymous referee for reading their paper carefully and for several constructive remarks, questions and suggestions.
The authors declare that there is no conflicts of interest in this paper.
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3. | Lilis Suryani, W. M. Kusumawinahyu, N. Shofianah, Dynamical Analysis on A Model of Cholera Epidemic with Quarantine, Vaccination, and Two Path of Transmissions, 2021, 1776, 1742-6588, 012052, 10.1088/1742-6596/1776/1/012052 | |
4. | Ana P. Lemos-Paião, Cristiana J. Silva, Delfim F. M. Torres, Ezio Venturino, Optimal Control of Aquatic Diseases: A Case Study of Yemen’s Cholera Outbreak, 2020, 185, 0022-3239, 1008, 10.1007/s10957-020-01668-z | |
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Parameter | |
| 1 |
| |
| 1 |
| -1 |
| 0 |
| |
| |
| |
| 0 |
| |
| 0 |
| 1 |
| -1 |
Parameter | Description | Value | Reference |
| Recruitment rate | 28.4 | [12] |
| Natural death rate | 1.6 | [13] |
| Ingestion rate | 0.01694 (day | Assumed |
| Half saturation constant | | Assumed |
| Immunity waning rate | 0.4/365 (day | [19] |
| Efficacy vaccination waning rate | 1/1 460 (day | [7] |
| Vaccination rate | 5/1 000 (day | Assumed |
| Treatment rate | 1.15 (day | Assumed |
| Recovery rate | 0.2 (day | [18] |
| Death rate (infected) | 6 | [13,31] |
| Death rate (in treatment) | 3 | Assumed |
| Shedding rate (infected) | 10 (cell/ml day | [3] |
| Bacteria death rate | 0.33 (day | [3] |
| Susceptible individuals at | 28 249 670 (person) | [34] |
| Infected individuals at | 750 (person) | [31] |
| Treated individuals at | 0 (person) | Assumed |
| Recovered individuals at | 0 (person) | Assumed |
| Vaccinated individuals at | 0 (person) | [30] |
| Bacterial concentration at | | Assumed |
Parameter | |
| 1 |
| |
| 1 |
| -1 |
| 0 |
| |
| |
| |
| 0 |
| |
| 0 |
| 1 |
| -1 |
Parameter | Description | Value | Reference |
| Recruitment rate | 28.4 | [12] |
| Natural death rate | 1.6 | [13] |
| Ingestion rate | 0.01694 (day | Assumed |
| Half saturation constant | | Assumed |
| Immunity waning rate | 0.4/365 (day | [19] |
| Efficacy vaccination waning rate | 1/1 460 (day | [7] |
| Vaccination rate | 5/1 000 (day | Assumed |
| Treatment rate | 1.15 (day | Assumed |
| Recovery rate | 0.2 (day | [18] |
| Death rate (infected) | 6 | [13,31] |
| Death rate (in treatment) | 3 | Assumed |
| Shedding rate (infected) | 10 (cell/ml day | [3] |
| Bacteria death rate | 0.33 (day | [3] |
| Susceptible individuals at | 28 249 670 (person) | [34] |
| Infected individuals at | 750 (person) | [31] |
| Treated individuals at | 0 (person) | Assumed |
| Recovered individuals at | 0 (person) | Assumed |
| Vaccinated individuals at | 0 (person) | [30] |
| Bacterial concentration at | | Assumed |