Atlantis Press Journal style


Received 11 October 2014

Accepted 28 January 2015

Research on Society Risk Evolution Mechanism and Counter Measures in Severe Emergency 
Infectious Disease — in the Case of H7N9 Avian Influenza 

Xuanhua Xu, Chenguang Cai *, Chunhong Wang 

School of Business, Central South University, Changsha 410083, China 
 

 
 

Abstract 

In this paper, the evolution and control of society risks caused by severe emergency infectious disease will be 
analyzed and studied. Firstly, the evolution chain of society risks caused by severe emergency infectious disease is 
constructed to analyze the evolution rule of society risks and identify the essential factors in counter measures. 
Then, the system dynamics model is established and employed to simulate the effects of society risk control with 
various countermeasures. Finally, based on the simulation results, a conclusion is drawn: improving medical 
treatment capacity, strengthening quarantine level under epidemic situation and enhancing the effective ness of 
dealing with public opinions are effective in controlling society risks. Especially when the three aspects mentioned 
above are promoted at the same time, the society risk control can achieve a more notable effect. 

Keywords: Severe emergency infectious disease; Society risk; Evolution; Counter measure; Disaster chain; System 
dynamics. 

*Corresponding Author: ccg169@126.com 

1. Introduction 

In recent years, severe emergency infectious diseases 
broke out frequently, such as SRAS in 2003, 
H5N1avian influenza in 2009, eye disease in 2012, 
H7N9 avian influenza in 2010, etc. On one hand, the 
occurrence of epidemic poses threats to human health 
and life. On the other hand, the increasingly cumulative 
society risk caused by epidemic gives rise to various 
social riots and exerts immense adverse effects on social 
stability and economic order. Therefore, it is of great 
necessity to carry out research on evolution mechanism 
and countermeasures of society risk given rise by severe 
infectious disease. By analyzing the evolution process 
of society risk caused by severe emergency infectious 
disease, the basic rule of society risk evolution is 
grasped and the key elements during society risk 
evolution is identified. On this basis, the coping strategy 
is developed to improve the effectiveness of social risk 

control and reducing the probability of society risk 
outbreak. 

The dynamic evolution process of disaster has its 
own law and mechanism. Therefore, only by grasping 
the evolution law and mechanism of disaster can the 
social risk initiated thereof be reduced to a minimum. 
Over the past, enormous studies have been conducted 
on the evolution mechanism of flood, earthquake, 
extreme weather, drought and other natural disasters, 
focusing on forming a complete disaster chain and 
identifying potential risks through simulating the 
evolution process of disasters. In addition, a suitable 
method is necessarily proposed in studying the 
evolution mechanism of risk. Li etc. [5] made a cross 
coupling analysis of the over two thousand disasters 
from 2001 to 2010 by statistical method, and 
summarized the occurrence rule of disasters in recent 
ten years in our country. Peng [6] studied the 
influencing mechanism of gas seepage generated in 

Journal of Risk Analysis and Crisis Response, Vol. 5, No. 1 (April 2015), 54-65

Published by Atlantis Press 
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54



sudden disaster by conducting the thermal coupling 
experiment. Wind [7] investigated the post-flood 
psychological health of community residents in the 
towns and countryside of Northern England, and studied 
the evolution rule of residents’ mental health. He [8] 
proposed a chaotic differential evolution algorithm, 
providing a better method to predict the evolution 
process of flood disaster. Tinguaro, etc. [9] proposed a 
data-based bipolar intellectual decision support system 
prototype, where society risks after natural disaster were 
managed through the construction and application of 
induced rules. On the part of identifying and evaluating 
disaster risk, Du[10] and Birkmann [11] conducted 
some researches on identifying possible risks after 
natural disasters. Guikema [12] put forward a risk 
assessment system on natural disaster and concrete 
method based on a large amount of statistical data, and 
applied it in the field of infrastructure system. From the 
perspective of meteorology, geography, disaster science 
and environmental science, Zhang etc. [14] proposed a 
risk identification and assessment method of droughts 
based on GIS (Geographical Information Systems). Zio 
[15] described the issues concerning the post-disaster 
risk identification resulted from such disaster as nuclear 
and gas leak, and proposed a risk assessment and 
management framework. Jiang [16] utilized the fuzzy 
mathematics theory and method, and applied the fuzzy 
comprehensive evaluation method, fuzzy clustering and 
fuzzy similarity method into the risk identification, 
classification and assessment of floods, providing an 
effective method for risk management of flood disaster. 
In the field of risk coping strategy, Hu and Zhu [17, 
18],with meteorological disaster as the research object, 
described the method of preventing disaster risk. 
Batabyal[19], targeting at financial risks of disaster, 
presented the theory of financial risk management 
caused by natural disaster and its warning model. Liu 
[20] put forward a decision analysis method of risk 
based on prospect theories, and applied it into the 
emergency response risk decision analysis. Huang [21] 
applied three risk analysis models of natural disaster 
into practical decision-making problems. On the basis of 
the utility function theory, Tamura [22] presented the 
risk analysis and decision model for natural disasters. At 
present, research on social risks mainly focuses on 
natural disaster itself. Nevertheless, studies on social 
risks caused by infectious disease are far from 
sufficient. Thus, this paper, with social risks caused by 

major infectious diseases as the research subject, is to 
construct the evolution chain of social risk to describe 
its process, and on this basis, to identify the key 
elements of risk response strategy and simulate the 
effectiveness of risk control with different strategies by 
means of the system dynamics model. The finding 
obtained in this paper is of certain reference value to 
develop and implement the prevention and control plan 
of severe infectious disease. 

2. Society Risk Evolution Mechanism in Severe 
Emergency Infectious Disease  

Social risk is defined as the possibility of giving rise to 
social conflicts and endangering social stability and 
order, namely, the possibility of social crisis outbreak. 
Once this possibility becomes a reality, social risk is 
transformed into social conflict, producing disastrous 
effects on social stability and order. During SARS and 
avian influenza outbreak, many events of social 
disruption effects due to epidemic situation broke out 
nationally, for example, many college students and 
migrant workers rushed to their hometown during 
SARS, which formed a so-called “homebound rush”; 
During avian influenza, some people deceived by 
rumors sparked a run on medical supplies, daily 
necessities, and some unscrupulous merchants seized 
the opportunity to bid up prices, disrupting social 
economic order; some lawbreakers began to publicize 
feudal superstitions, inciting a crowd riot. The cases 
cited above are all caused by the accumulation of social 
risks. 

The social risk system (Fig. 1) mainly covers three 
basic elements: hazard-formative factor, hazard-
inducing environment, and hazard-affected body. Take 
social risk caused by avian influenza as an example, the 
outbreak of avian influenza is the incident that touches 
off the generation and evolution of social risk, which is 
thus defined as hazard-formative factor. Hazard-
formative environment falls into two types: natural 
environment and social environment. In terms of natural 
environment, epidemic spreads very rapidly because of 
bird migration in spring and fall, and meanwhile the 
warm and humid climate in spring also provides 
favorable conditions for the spreading of disease. In the 
part of social environment, most of poultry farms are 
short of necessary monitoring and prevention measures 
against new virus, and in addition, the spreading of 
rumors and psychological fear of human beings 

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accelerates the formation and accumulation of social 
risks. Hazard-affected body is mainly the human beings 
with psychological fear and vulnerability of social 
environment. When the number of residents with fear 
accumulates to a certain number, social risk will burst 
out, and then a variety of group social events will occur, 
causing negative influences on social development and 
stability. 
     The evolution chain of society risk caused by severe 
emergent infectious disease is shown in Fig. 2. When 
Bird flu outbreaks at a certain moment, social risk began 
to generate; as time passes by, the number of people 
infected and the death toll are on constant increase, and 
all kinds of rumors began to spread, causing the 

psychological fear of residents to soar. In turn, the fear 
of residents also intensifies the spreading of rumors to a 
certain extent, making more people infected by rumors. 
Thus, social risks began to accumulate rapidly. The 
enlargement of social risk is mainly manifested in two 
aspects: intensifying of psychological fear of residents 
and the increasing number of people infected by rumors. 
Once social risk accumulates to a certain extent, social 
risk will burst out, giving rise to a series of mass events. 
Meanwhile, these group social events will in turn speed 
up the accumulation of social risks. 
 
 
 

 

hazard-inducing 

environments 

Hazard 

-affected  

bodies 

 

Fears of residents; 

vulnerability of social 

environment 

Birds  

migratory; 

weather 

conditions; 

Regulatory 

loopholes of 

farm 

 
 

  

Hazard 

-formative 

factors 

Outbreak of avian influenza 

 
Fig. 1.  Social risk system of avian influenza. 

  

Increasing 

number of people 

infected by rumors 

 

Epidemic 

breakout 

Increasing number of 

infected people  

Increasing death toll  

 

Rumors beginning to 

spread 

 

Increasing level 

 of resident’s fear 

 
 

Increasing 

 number of mass 

incidents 

 

Stage of risk generating Stage of risk spreading Stage of risk assembling Stage of risk outbreak 

 
Fig.2. Evolution chain of social risk of avian in fluenza. 

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56



3. Running Head Society Risk Control 
Strategies of Severe Emergent Infectious 
Disease 

3.1. Key elements of the strategy 

From Fig. 1 and Fig. 2, it is known that there are 
primarily three influence factors in the formation and 
evolution of social risk: the death toll of infections, the 
spreading degree of epidemic and rumors. Therefore, 
"strengthening the medical treatment ability", 
"enhancing the quarantine level during epidemic 
situation "and "promoting the effectiveness of handling 
public opinion are the key elements in formulating the 
risk control strategy.  

(1) Strengthening the medical treatment ability to 
reduce the mortality of patients. The purpose of 
strengthening the medical treatment ability is to save the 
life of infections and minimize the mortality rate. Once 
a person is found to infect with bird flu, he or she 
should be subject to early report, early quarantine and 
early treatment. Moreover, the development of vaccine 
and its application in clinical trials should be accelerated 
as soon as possible. 

(2) Enhancing the quarantine level to prevent the 
further spreading of epidemic. Bird flu and SRAS has 
varied mode of infection. According to the existing 
studies, avian influenza can only be transmitted through 
"bird to bird" and "bird to human", excluding "person to 
person". However, in practice, once a bird-flu patient is 
diagnosed, quarantine treatment is conducted firstly, and 
meanwhile his or her contacts must be put in quarantine 
for a few days. When avian influenza outbreaks, the 
poultry farm and surrounding farms need to be 
disinfected and all the poultry therein should be killed, 
and vehicles carrying the livestock and poultry should 
be disinfected. Through controlling the infection source 
and cutting off the mode of infection, the epidemic will 
be prevented from spreading to the surrounding area. 

(3) Handling public opinions effectively to reduce 
their impacts on the masses. People’s fear of the 
epidemic is mainly because H7N9 avian influenza virus 
is a new one with few known information, which causes 
uneasiness over the mass psychology. Especially when 
the number of infections and the death toll continues to 
rise, people’s fear will intensity and rumors begin to 
spread. Confronted with the above situation, a series of 
measures should be taken to clarify the rumors timely 
and effectively, crack down on such illegal activities as 

spreading of rumors by network, publications and other 
means so as to reduce the effect of rumors on residents. 
3.2.  A system dynamics model over emergency 

infectious disease epidemic situation of social 
risk control effect 

In order to display the effect of social risk control 
emergency strategy on emergency infectious diseasein a 
more visual way, the system dynamics (SD) model is 
constructed to simulate the process of society risk 
control. The recovery rate, the source processing 
efficiency and the rumor clarification rate are optioned 
to represent the medical treatment capacity, the 
epidemic quarantine level and rumor processing 
effectiveness of emergency strategy, respectively. Take 
H7N9 outbreak in Shanghai as an example, based on the 
relationship between different variables, relevant 
parameters and formula are set as shown in Fig.3. 

 (1) Basic situation of the epidemic area. Shanghai 
city has an area of 8064.3 square kilometers and a total 
of 23.83 million populations. The area is distributed 
with dozens of poultry farms, with each producing 
about 200000 feather on average. By the end of July 16, 
2013, a total of 30 H7N9 infected cases, including 11 
death cases, has been reported in Shanghai. The 
mortality was 36.7%, slightly higher than the national 
average. According to the relevant statistics, during the 
outbreak, mass incidents caused by epidemic have up to 
dozens of times, which produced a huge influence on 
the normal life of residents and social stability. 

(2) Medical treatment of avian influenza. H7N9 is a 
highly lethal avian influenza. If the patient fails to 
receive timely and effective treatment, their life would 
be threatened. Therefore, the recovery rate of avian flu 
is set as the index to evaluate the treatment, namely, the 
higher the treatment level, the better the medical effect 
on the avian influenza, and the lower the mortality rate. 
The infection probability of avian influenza through 
contact is 6.12% to 7.63%, sothe parameter of the 
infection probability of avian influenza through contact 
is set as RANDOM UNIFORM (0.0612, 0.0763, 0.001). 
The infection rate refers to the number of newly 
infected patients per day, which is associated with the 
infection probability through contact and the number of 
people having contact with the virus. Thus, the infection 
rate of avian influenza is set as: the infection probability 
of avian influenza through contact* the number of 
people contacting the virus. 

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(3) Epidemic quarantine and control. The infection 
mechanism of H7N9 avian influenza is different from 
that of SARS. It has not confirmed thatH7N9 avian 
influenza can be transmitted by "person to person". The 
major infection source of H7N9 avian influenza is the 
bird and poultry with avian influenza virus. The 
epidemic quarantine and control, on one hand, is to 
disinfect the farm where avian influenza breaks out so 
as to reduce the probability of human exposure to the 
infection source. On the other hand, disinfection of the 
ground and vehicles used in the farm in epidemic area 
should be strengthened to cut off the transmission route 
of virus and reduce the diffusion rate of virus. 
 

The infection treatment efficiency index is 
introduced to indicate the quarantine and control level 
of an epidemic. There is a positive correlation between 
the infection treatment efficiency and the control level 
of epidemic situation. According to relevant statistical 
data, it can be obtained: the disinfection level of 

infected avian =50000* processing efficiency of 
infectious source (the quarantine and control level of an 
epidemic). The diffusion coefficient of epidemic 
occurred in a farms refers to the number of newly 
infected farms nearby at unit time when bird flu hits one 
farm, which is mainly infected by  the characteristics of 
avian influenza virus and bird migration. Avian 
influenza often take place at the beginning of the spring 
or autumn until the rising or falling temperature is 
unsuitable for virus survival as time passes by. In 
addition, migratory birds are obviously seasonal. Thus, 
spring and autumn are two high-occurrence seasons of 
bird flu. The diffusion coefficient of epidemic taken 
place in a farm changes with time when the climate is 
warming or cooling, and the scale of bird migratory 
gradually decreases until the end. It can be concluded 
that the diffusion coefficient of an epidemic is in a 
negative correlation with time. According to relevant 
statistical data concerning the spreading of H7N9 bird 
flu in Shanghai, the independent variable “time” is set to 

Number of
infectionsInfection rate

Region's
population

Infection probability
of avian influenza
through contact

Processing efficiency of
infectious source (the

quarantine and control level of
an epidemic)

Recovery rate of
avian flu (medical

treatment)

Mortality of
infections

Number of
rehabilitation

Death toll of
infections

Resident number
of infected by

rumor
Increasing rate of

the number of
residents infected

by rumors

Residents
fear index

Frequency of
mass incidentsOutbreak of massincidents

<Time>

Probability
of rumor
spreading

Number of residents
having contact with avian

influenza virus

Trust degree of
clarification
information

Diffusion coefficient
of rumor

Total area of the
region

Diffusion coefficient of
the epidemic in the farm

Number of patients from
other regions into the

local region

Virus contact
probability

Decrease rate of
the number of

residents infected
by rumors

Threshold value of
mass incidents

breakout

Effectiveness of disease
prevention and control

measures

Rumor clarification rate
(the level of public opinion

processing)

Number of
infected
poultry

Decrease in the
number of sick

poultry

Average size of
farms

Number of
farms with
epidemic
outbreaks

Diffusion rate
of farms
outbreak

Average
infection rate

Increase in the
number of sick

poultry Disinfection level of
infected avian

Quarantine level
of epidemic

Number of group social events
occurred that are

psychologically acceptable to
residents

Maximum number of
infections that are

psychologically acceptable to
residents

Maximum number of deaths
that are psychologically
acceptable to residents

 

Fig. 3. System dynamics model of social risk control of disease epidemic situation. 

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fit the diffusion coefficient of the farm with epidemic: 
the diffusion coefficient of the epidemic in the 
farm=MAX ((1.8254* (1/ (1.4525*Time+1)) -
0.000256*Time), 0). 

The increase rate of farms with epidemic outbreak 
is the number of newly infected farms at unit time, 
which is associated with the diffusion coefficient,the 
quarantine level and the number of farms infected. The 
increase rate of farms with epidemic outbreak = the 
diffusion coefficient of epidemic in the farm * (1-the 
quarantine level of epidemic) * the number of farms 
with epidemic outbreaks. According to the occurred 
cases, most of patients have contact with poultry before 
infection, so the probability ofinfection is related with 
the number of resident who isexposed to the infection. 
Based on actual data,the equation is obtained: the 
probability of infection through contact = the number of 
infected poultry *0.85*10^-11, the number of residents 
having contact with avian influenza virus mainly 
includes local ones and those coming from other places. 
So the number of residents having contact with avian 
influenza virus = the total population in the area * 
probability of people having contact with virus + the 
number of patients from other regions into the local 
region. 

 (4) Social risk processing. Relevant parameters of 
social risk comprise the number of rumors, the number 
of residents infected by rumors and the number of group 
events, etc. Among them, the resident fear index refers 
to the fear of ordinary residents for epidemic situation, 
the number of infections and the number of group social 
events caused by epidemic. It can be expressed as the 
following equation: the residents fear index =0.2* 
frequency of mass incidents/ the number of group social 
events occurred that are psychologically acceptable to 
residents +0.4* the number of infected people 
/maximum number of infections that are 
psychologically acceptable to residents +0.4* deaths 
/maximum number of deaths that are psychologically 
acceptable to residents. The psychological fear index of 
residents is between [0, 1], the greater the index, the 
more fear the residents have psychologically. We use 
questionnaire to determine the coefficients mentioned in 
the equation above. I n this paper, the number of 
maximum acceptable group social events is set 100, the 
number of maximum acceptable infections is set 100 
and the number of maximum acceptable deaths is set 50. 

The number of residents infected by rumors is 
connected with the spreading rate of rumors, the number 
of rumors and the effectiveness of rumor processing. 
The increase rate of residents infected by rumors 
=probability of rumor spreading* diffusion coefficient 
of rumor * (1+0.002* the resident number of infected by 
rumor/ (1+ the resident number of infected by rumor)).  
Decreasing the number of residents infected by rumors 
is mainly achieved by eliminating the rumor influence 
by clarifications. The effectiveness of rumor refuting is 
mainly concerned with the rumor clarification rate and 
the trust degree of residents for clarification 
information. The rumor clarification rate refers to the 
ratio between the number of rumors clarified and the 
number of spreading rumors, the greater the rate is, the 
more people will be affected. The trust degree of 
clarification information mainly lies in the effectiveness 
of epidemic prevention and control measures, namely, 
the more effective the epidemic prevention and control 
measures is, the greater the trust degree of residents 
have. The decrease rate of the number of residents 
infected by rumors = rumor clarification rate (the level 
of public opinion processing) * the resident number of 
infected by rumor *the trust degree of clarification 
information. The frequency of mass incidents is the 
main criteria to evaluate the control level of social risks, 
which is mainly related to the number of residents 
infected by rumors. According to the survey, we find 
the threshold value of mass incident outbreak is 30000 
persons average, meanly that it will break out once the 
number of residents infected by rumors exceeds the 
threshold. 

3.3. Social risk control effect under different 
conditions 

In initial state, supposing the number of farms with 
epidemic outbreak is 1, the simulation step size is 1 day 
and the end time is 100 days, the system dynamics 
model is then used to simulate the change of social risks 
under different medical treatment level, epidemic 
quarantine control level and effectiveness of public 
opinion handling. According to the actual prevention 
and control situation of avian influenza in Shanghai, in 
the situation 0, the three exogenous variables, including 
the recovery rate, source processing efficiency and 
rumors clarification rate, are assigned as 0.66, 0.6 and 
0.7 respectively, which are then used in the system 
dynamics model for simulation. As the simulation 

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results are basically consistent with actual data, this 
model is proven to be effective and the 3 exogenous 
variables are set as the initial parameters. 

3.3.1. Impact of medical treatment ability on risk 
society control 

On the premise that other conditions remains 
unchanged, the medical treatment level is improved, 

with the recovery rate grew by 10% and 20% 
respectively. That is to say, in situation 1, the recovery 
rate = 0.73; in situation 2, the recovery rate = 0.89; in 
situation 0, the recovery rate is the initial parameter. 
The simulation results are shown in the Fig. 4 to Fig. 6 
below. 

As shown in Fig. 4 to Fig.6, with the improvement 
of medical treatment level, the recovery rate of 
infections was improved, the psychological fear index 

 
Fig.4. Impact of different medical treatment level on residents fear index 

 
Fig.5. Impact of different medical treatment level on the number of residents infected by rumor. 

 
 Fig.6. Impact of different medical treatment level on the frequency of mass incidents. 

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of residents leveled off after rising to a certain degree. 
Compared with the initial stage, the number of residents 
affected by rumors shows a downward trend, the 
frequency of mass incidents is fewer, indicating a more 
obvious effectiveness in social risk control. 

3.3.2. Impact of the quarantine and control level of an 
epidemic on society risk control 

On the premise that other conditions remains 
unchanged, the control level of an epidemic is 
improved, the processing efficiency of infection source 
is increased by 10% and 20% respectively. That is to 

 
Fig.7. Impact of different quarantine and control level on residents fear index 

 
Fig.8. Impact of different quarantine and control level on the number of residents infected by rumor. 

 

 
 Fig.9. Impact of different quarantine and control level on the frequency of mass incidents. 

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say, the processing efficiency of infection source is set 
as 0.66 in situation 3,0.72 in situation 4and the initial 
parameters in situation 0. The simulation results are 
shown in Fig. 7 to 9 below. 

As shown in Fig. 7 to Fig. 9, with the improvement 
of processing efficiency of infection source, the rise of 
residents fear index gradually leveled off and the 
number of residents infected by rumors is on the 
decrease, thus group social events will not occur 
basically. 
 
 

3.3.3. Impact of the rumor processing level on risk 
society control 

On the premise that other conditions remains unchanged, 
the efficiency of public opinion processing is improved, 
with the rumor clarification rate increased by 10% and 
20% respectively. That is to say, the rumor processing 
efficiency is 0.77 in situation 5, 0.84 in situation 6 and 
is the initial parameter in situation 0. The simulation 
results are shown in Fig. 10 to Fig. 12. 

As the rumor clarification rate rises, the inflection 
point of resident fear index becomes low gradually, the 
increase rate of the number of residents infected by 

 
Fig.10. Impact of different rumor processing level on residents fear index. 

 
Fig.11. Impact of different rumor processing level on the number of residents infected by rumo. 

 

 
 Fig.12. Impact of different rumor processing level on the frequency of mass incidents. 

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rumor is slowing, and the frequency of less mass 
incidents is decreasing. The above results indicate that 
social risks can be controlled, but the control effect is 
still not ideal. 

3.3.4. Impact of coordination strategy on social risk 
control 

Collaborative strategy is to control the social risks 
caused by epidemic through improving the medical 
treatment capacity, quarantine level and rumor 
clarification level at the same time. Suppose that the 

above three aspects are increased by 10% and 20%, 
respectively. That's to say, in situation 7, the recovery 
rate = 0.73, the processing efficiency of infection source 
= 0.66, the rumor clarification rate = 0.77; in situation 7, 
the recovery rate = 0.8, the processing efficiency of 
infection source =0.71, and the rumor clarification rate= 
0.84; in situation 0, they are all the initial parameters. 
The simulation results are shown in Fig. 13 to Fig. 15. 

As shown in Fig. 13 to 15, the higher level the 
ability of collaborative strategy, the lower the increase 
rate of resident fear and the number of residents infected 

 
Fig.13. Impact of different collaborative strategies on residents fear index. 

 
Fig.14. Impact of different collaborative strategies on the number of residents infected by rumor. 

 

 
 Fig.15. Impact of different collaborative strategies on the frequency of mass incidents. 

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by rumors and the more effective the social risks 
control. Compared with the coping strategy aiming at 
improving a single aspect, the collaborative strategy can 
realize effective control over all indexes related with 
social risks, thus obtaining more obvious control effects. 
Therefore, in the actual operation, collaborative strategy 
should be given priority to cope with social risks caused 
by severe emergent epidemics. 

4. Conclusion 

This paper is mainly aimed at studying the key 
components, generation and evolution process of social 
risks caused by severe emergent epidemic, and 
simulating the effect of social risk control with different 
strategies by means of the system dynamics model. The 
simulation results indicated that it is effective to control 
social risks through improving the medical treatment 
ability, strengthening the quarantine control level and 
enhancing the effectiveness of public opinion handling, 
especially when the above-mentioned three aspects are 
strengthened at the same time. 

Acknowledgements 

This Paper was supported by the Key Program of Nation- 
al Social Science Foundation of China (No. 12AZD109)
and  the National Natural Science Foundation of China 
(No. 71171202). 

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65


	1. Introduction
	2. Society Risk Evolution Mechanism in Severe Emergency Infectious Disease
	3. Running Head Society Risk Control Strategies of Severe Emergent Infectious Disease
	3.1. Key elements of the strategy
	3.2.  A system dynamics model over emergency infectious disease epidemic situation of social risk control effect
	3.3. Social risk control effect under different conditions
	3.3.1. Impact of medical treatment ability on risk society control


	4. Conclusion
	Acknowledgements
	References