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Advances in Social Sciences Research Journal – Vol.7, No.10
Publication Date: October 25, 2020
DOI:10.14738/assrj.710.9211.
Ning, Y. (2020). Effects Of Epidemic Prevention On The University Management. Advances in Social Sciences Research Journal, 7(10) 311-
316.
Effects Of Epidemic Prevention On The University Management
Yi-Zi Ning
Central University of Finance
And Economics, China.
ABSTRACT
The information transmission network is different from the physical
contact network. It is of great significance to study the spread range of
epidemic diseases by distinguishing the topological structure of
perceptual information transmission network from that of physical
contact disease diffusion network. SIR model is used to describe the
transmission process of epidemic, and it is very important to explore the
disease diffusion model which integrates perceptual transmission and
disease diffusion. Furthermore, with a multi-layer network coupling the
diffusion of perceptual information and the spread of disease, the
relationship between different layers is the key element of the system
model. Using multi-layer network to describe the system in the real
world, through the introduction of individual awareness propagation
mechanism, this paper studies the interaction between epidemic
diffusion and awareness propagation in the framework of multiple
networks, and establishes multiple policy adjustment rules to study the
propagation dynamics of awareness in different networks. Considering
the two-layer network, the first layer network is described as physical
contact network, and epidemic diseases spread through the physical
contact network, which affects the mutual transmission of information
at the level of awareness network. The other layer is awareness
communication network. It is an important task to study the complex
interaction between human society and biological infectious diseases.
In this work, we study the influence of awareness and behavior based on
multiple networks on infection density. The university management
should pay attention to topological structures of networks and the
strategies.
Keywords: Infectious disease; Complex network; Evolutionary game model;
University management.
INTRODUCTION
More and more scholars have paid attention to the influence of perceptual information transmission
on epidemic dynamics [1]. But most studies assume that the spread of information and the spread
of epidemics are in the same network [2]. With the development of science and technology,
information, disease, public opinion and so on can be spread rapidly through many different
channels [3]. The multi network theory, which has been widely studied, is used to simulate the
spread of information or related consciousness perception, so the interaction between information
and epidemic dynamics has been paid more and more attention [4].
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In multi-layer network, the whole network model is composed of different layers of network, each
layer of network has the same node, but the connection edge may be different. It is of great practical
significance to study the spread model of infection uploaded by multi-layer network [3]. On the one
hand, in the real social interpersonal contact network, there are different kinds of relationships
between people, and the connection of different kinds of relationships may be different. The spread
of epidemic diseases needs certain physical contact, but the spread of consciousness does not need
physical contact.
Awareness information is spread through different networks. Because the spread of the information
of consciousness can be carried out through the way of no actual contact, its transmission path and
disease transmission path may not be the same. On the other hand, because the epidemic disease
may have different transmission paths, there may be different connections between the same nodes,
and different topological structures lead to different diffusion results of epidemic disease.
Therefore, it is necessary to distinguish the spread network of consciousness and the spread
network of epidemic diseases in the process of studying the spread model of infectious diseases.
Sahneh et al. showed that the information transmitted in another network can help to improve the
resilience of agent groups to resist propagation, and find the best information propagation for
different topologies [5]. Wang et al. studied the interaction of epidemic dynamics and information
dynamics in multiple networks based on SIR (r-recovery) model, focusing on two basic quantities
of epidemic disease threshold and final prevalence rate [6]. Zhang Haifeng (2015) studied the
interaction between epidemic spread and perceived spread in multiple networks, and described the
spread process of epidemic with SIS model [7]. Wang et al. (2019) proposed an infectious disease
model based on the perception diffusion network and the disease transmission network based on
whether to detect infection [8]. To sum up, the existing research of disease transmission does not
consider the impact of the adjustment of prevention strategies based on the level of awareness on
the scope of disease transmission.
In the existing literature, in the perceptual network, the study of consciousness mainly assumes that
whether an individual can be aware of being infected by his neighbors, and the way in which
individual consciousness is generated, one is to be informed by other conscious neighbors, the other
is to obtain the result of consciousness through the change of his own state. In reality, individuals
can obtain information about the spread of disease through many ways. We will study the spread of
epidemics and the spread of awareness information in a two-layer coupling network, one is the
spread network of awareness information, the other is the spread network of epidemic disease. The
university management should pay attention to topological structures of networks and the
strategies.
THE MODEL DESCRIPTION
The Awareness Propagation
At the beginning of the epidemic season (τ), there are three strategies that can be adopted by
everyone, i.e. vaccination (©v), self-protection (ÖÑ), and laissez faire (TMv) [9]. Vaccination can
protect nodes from infection, that is, successful immunization. Taking self-protection measures can
reduce the possibility of being infected with a certain probability (δ). Laissez faire means not taking
any preventive measures [10].
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Ning, Y. (2020). Effects Of Epidemic Prevention On The University Management. Advances in Social Sciences Research Journal, 7(10) 311-316.
At the end of the epidemic season (τ), each node i adjusts the strategy in the new season (τ + 1) by
comparing its own income in the previous season (τ) with that of its neighbor node j. The node is
dynamically adjusted from the strategy to another in the epidemic season (τ + 1).
The Disease Diffusion
SIR model is used to describe the process of disease transmission in the Equation (1) [11].
̈ã(Ü), ̈ä(Ü) and ̈≠(Ü) represent the proportions of nodes in S, I, and R state at the time of epidemic
dynamic (Ü) in each epidemic season (τ). Set the infection rate as K, and the recovery rate as Æ. The
spread range /Ø∞%6± of disease transmission is the average epidemic range in 1000 seasons.
⎩
⎪⎪
⎨
⎪⎪
⎧ d ̈ã(Ü)
dÜ = −K ̈ã
ã(Ü) ̈ä
(Ü)
d ̈ä(Ü)
dÜ = K ̈ã
ã(Ü) ̈ä
(Ü) − Æ ̈ä
(Ü)
d ̈≠(Ü)
dÜ = Æ ̈ä
(Ü)
(1)
The Network Description
There are N nodes in the two-layer network. The nodes in each layer are the same, but the edges
between nodes are different in network layers. One layer is the network of awareness and
perception. The other is the network of epidemic disease.
Information Transmission Network
The spread of preventive immune strategies is carried out at the level of awareness network. ∂* =
(©,.*) is represented by adjacency matrix Ç* = ∑v∏Wπ
∫×∫.
Epidemic Transmission Network
The spread of epidemic diseases is carried out at the level of disease diffusion network. ∂, = (©, .,)
is represented by adjacency matrix Ç, = ∑w∏Wπ
∫×∫.
The epidemic disease diffusion network and the awareness propagation network are respectively
WM and BA networks. Set the number of nodes as 1000 in WM network; the number of nodes as
1000 and the average degree as 4 in BA network [12].
Numerical Simulations and Analysis
Results on Synthetic Datasets
WM network is used as the spread network of epidemic diseases. BA is used as the spread network
of consciousness perception respectively.
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Table 1. The results of disease transmission and diffusion.
T /Ø∞%6±
(K, Æ) 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
0.20.2 0.237 0.222 0.163 0.171 0.085 0.068 0.397 0.300 0.200 0.100 0.081
0.20.4 0.183 0.151 0.173 0.141 0.109 0.083 0.397 0.299 0.200 0.100 0.077
0.20.6 0.228 0.193 0.132 0.103 0.109 0.096 0.398 0.299 0.200 0.101 0.102
0.20.8 0.250 0.213 0.155 0.145 0.053 0.061 0.398 0.299 0.200 0.101 0.060
0.21.0 0.207 0.230 0.128 0.121 0.068 0.068 0.396 0.300 0.200 0.101 0.116
0.40.2 0.221 0.187 0.176 0.139 0.092 0.091 0.397 0.300 0.200 0.100 0.097
0.40.4 0.164 0.206 0.142 0.105 0.076 0.089 0.396 0.300 0.200 0.100 0.091
0.40.6 0.211 0.194 0.131 0.126 0.092 0.063 0.398 0.299 0.199 0.100 0.111
0.40.8 0.154 0.202 0.138 0.102 0.085 0.113 0.398 0.299 0.201 0.100 0.078
0.41.0 0.111 0.167 0.130 0.094 0.078 0.075 0.397 0.300 0.200 0.100 0.125
0.60.2 0.274 0.209 0.103 0.115 0.111 0.087 0.397 0.299 0.200 0.100 0.114
0.60.4 0.217 0.167 0.188 0.118 0.106 0.059 0.398 0.299 0.200 0.100 0.128
0.60.6 0.273 0.220 0.150 0.124 0.093 0.059 0.397 0.299 0.200 0.100 0.084
0.60.8 0.264 0.168 0.122 0.132 0.086 0.103 0.397 0.300 0.200 0.100 0.132
0.61.0 0.222 0.219 0.153 0.133 0.100 0.054 0.398 0.299 0.200 0.100 0.120
0.80.2 0.183 0.166 0.147 0.123 0.105 0.065 0.397 0.300 0.200 0.100 0.113
0.80.4 0.265 0.179 0.172 0.106 0.099 0.097 0.398 0.299 0.199 0.101 0.070
0.80.6 0.229 0.137 0.199 0.118 0.084 0.071 0.396 0.299 0.200 0.100 0.123
0.80.8 0.233 0.178 0.152 0.091 0.094 0.094 0.397 0.300 0.200 0.101 0.117
0.81.0 0.250 0.149 0.156 0.124 0.085 0.096 0.396 0.299 0.200 0.101 0.071
1.00.2 0.183 0.166 0.180 0.120 0.086 0.080 0.397 0.299 0.200 0.100 0.085
1.00.4 0.266 0.211 0.165 0.132 0.076 0.070 0.397 0.300 0.200 0.101 0.117
1.00.6 0.229 0.242 0.144 0.108 0.083 0.063 0.397 0.300 0.199 0.101 0.096
1.00.8 0.204 0.193 0.164 0.099 0.100 0.063 0.397 0.299 0.200 0.101 0.096
1.01.0 0.286 0.200 0.173 0.125 0.077 0.059 0.396 0.299 0.200 0.100 0.113
The spread range /Ø∞%6± of diseases is shown in Table1. The spread range /Ø∞%6± of disease
transmission is obtained in 1000 seasons.
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Ning, Y. (2020). Effects Of Epidemic Prevention On The University Management. Advances in Social Sciences Research Journal, 7(10) 311-316.
Analysis
When K is small and Æ is large, the proportion of infected individuals in the population is low. With
the increase of the infection rate K, the highest value of the spread range /Ø∞%6± is very close. When
the self-protection rate is δ~(0.47,0.7), the spread range /Ø∞%6± of epidemic diseases shows the
counter intuitive phenomenon of first increasing and then decreasing, and then the spread range of
epidemic diseases is gradually weakened. When the self-protection rate is in other value range, the
spread range of epidemic disease /Ø∞%6± is significantly low.
CONCLUSIONS AND FUTURE WORK
Many complex systems are composed of coupling networks at different layers, each of which
represents one of many possible interaction types. The connection between different layers is the
key element of the system model. Using multi-layer network to describe the system in the real
world, this paper studies the interaction through the introduction of individual awareness
propagation mechanism. Considering the two-layer network, the first layer network is described as
physical contact network, and epidemic diseases spread through the physical contact network,
which affects the mutual transmission of information at the level of awareness network. The other
layer is awareness communication network. The spread of epidemic diseases through physical
contact network can trigger the spread of awareness information in other different layers of
network. It is an important task to study the complex interaction between human society and
biological infectious diseases. In this work, we study the influence of awareness and behavior based
on multiple networks on infection density. In the university management, the strategies used should
be paid attention to.
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