scholarly journals Modeling and Simulating Online Panic in an Epidemic Complexity System: An Agent-Based Approach

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Linjiang Guo ◽  
Yang Li ◽  
Dongfang Sheng
Keyword(s):  
Disease Outbreaks ◽  
Scale Free Network ◽  
Online Forums ◽  
Social Production ◽  
Social Responses ◽  
Agent Based ◽  
Scale Free ◽  
The Social ◽  
Free Network ◽  
The Impact

Following the outbreak of a disease, panic often spreads on online forums, which seriously affects normal economic operations as well as epidemic prevention procedures. Online panic is often manifested earlier than in the real world, leading to an aggravated social response from citizens. This paper conducts sentiment analysis on more than 80,000 comments about COVID-19 obtained from the Chinese Internet and identifies patterns within them. Based on this analysis, we propose an agent-based model consisting of two parts—a revised SEIR model to simulate an offline epidemic and a scale-free network to simulate the Internet community. This model is then used to analyze the effects of the social distancing policy. Assuming the existence of such a policy, online panic is simulated corresponding to different informatization levels. The results indicate that increased social informatization levels lead to substantial online panic during disease outbreaks. To reduce the economic impact of epidemics, we discuss different strategies for releasing information on the epidemic. Our conclusions indicate that announcing the number of daily new cases or the number of asymptomatic people following the peak of symptomatic infections could help to reduce the intensity of online panic and delay the peak of panic. In turn, this can be expected to keep social production more orderly and reduce the impact of social responses on the economy.

scholarly journals Evaluating the Impact of Name Resolution Dependence on the DNS

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Haiyan Xu ◽  
Zhaoxin Zhang ◽  
Jianen Yan ◽  
Xin Ma
Keyword(s):  
Large Scale ◽  
Scale Free Network ◽  
Security Measures ◽  
Scale Free ◽  
Domain Names ◽  
The Core ◽  
Complex Dependence ◽  
Random Failure ◽  
Free Network ◽  
The Impact

In the process of resolving domain names to IP addresses, there exist complex dependence relationships between domains and name servers. This paper studies the impact of the resolution dependence on the DNS through constructing a domain name resolution network based on large-scale actual data. The core nodes of the resolution network are mined from different perspectives by means of four methods. Then, both core attacks and random attacks on the network are simulated for further vulnerability analysis. The experimental results show that when the top 1% of the core nodes in the network are attacked, 46.19% of the domain names become unresolved, and the load of the residual network increases by nearly 195%, while only 0.01% of domain names fail to be resolved and the load increases with 18% in the same attack scale of the random mode. For these key nodes, we need to take effective security measures to prevent them from being attacked. The simulation experiment also proves that the resolution network is a scale-free network, which exhibits robustness against random failure and vulnerability against intentional attacks. These findings provide new references for the configuration of the DNS.

The epidemic network construction and immunization based on node strength

2018 ◽  
Vol 32 (26) ◽  
pp. 1850319 ◽  
Author(s):  
Fuzhong Nian ◽  
Longjing Wang ◽  
Zhongkai Dang
Keyword(s):  
Social Networks ◽  
Real World ◽  
Scale Free Network ◽  
Network Construction ◽  
The Real ◽  
Scale Free ◽  
Social Ability ◽  
The Social ◽  
Free Network

In this paper, a new spreading network was constructed and the corresponding immunizations were proposed. The social ability of individuals in the real human social networks was reflected by the node strength. The negativity and positivity degrees were also introduced. And the edge weights were calculated by the negativity and positivity degrees, respectively. Based on these concepts, a new asymmetric edge weights scale-free network which was more close to the real world was established. The comparing experiments indicate that the proposed immunization is priority to the acquaintance immunization, and close to the target immunization.

scholarly journals Effectiveness of Closure of Public Places with Time Delay in Disease Control

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Zhenggang Wang ◽  
Kwok Yip Szeto ◽  
Frederick Chi-Ching Leung
Keyword(s):  
Social Network ◽  
Time Delay ◽  
Disease Control ◽  
Sir Model ◽  
Scale Free Network ◽  
Public Places ◽  
Scale Free ◽  
The Social ◽  
Free Network ◽  
High Degree

SummaryA theoretical basis for the evaluation of the effciency of quarantine measure is developed in a SIR model with time delay. In this model, the effectiveness of the closure of public places such as schools in disease control, modeled as a high degree node in a social network, is evaluated by considering the effect of the time delay in the identification of the infected. In the context of the SIR model, the relation between the number of infectious individuals who are identified with time delay and then quarantined and those who are not identified and continue spreading the virus are investigated numerically. The social network for the simulation is modeled by a scale free network. Closure measures are applied to those infected nodes with high degrees. The effectiveness of the measure can be controlled by the present value of the critical degree K

Hub Agents Determine Collective Behavior

2015 ◽  
Vol 11 (02) ◽  
pp. 165-181
Author(s):  
Saori Iwanaga ◽  
Akira Namatame
Keyword(s):  
Social Network ◽  
Collective Behavior ◽  
Traffic Congestion ◽  
Scale Free Network ◽  
Collective Phenomena ◽  
Computing Technology ◽  
Scale Free ◽  
Cooperative Relationships ◽  
The Social ◽  
Free Network

There are growing interests for studying collective behavior including the dynamics of markets, the emergence of social norms and conventions and collective phenomena in daily life such as traffic congestion. In our previous work [Iwanaga and Namatame, Collective behavior and diverse social network, International Journal of Advancements in Computing Technology 4(22) (2012) 321–320], we showed that collective behavior in cooperative relationships is affected in the structure of the social network, the initial collective behavior and diversity of payoff parameter. In this paper, we focus on scale-free network and investigate the effect of number of interactions on collective behavior. And we found that choices of hub agents determine collective behavior.

Agent-Based Simulation of Rumor Propagation on Social Network Based on Active Immune Mechanism

2017 ◽  
Vol 5 (6) ◽  
pp. 571-584 ◽  
Author(s):  
Jianhong Chen ◽  
Qinghua Song ◽  
Zhiyong Zhou
Keyword(s):  
Social Network ◽  
Online Social Network ◽  
Propagation Model ◽  
Immune Mechanism ◽  
Scale Free Network ◽  
Propagation Process ◽  
Agent Based ◽  
Scale Free ◽  
Rumor Propagation ◽  
Free Network

AbstractTo simulate the rumor propagation process on online social network during emergency, a new rumor propagation model was built based on active immune mechanism. The rumor propagation mechanisms were analyzed and corresponding parameters were defined. BA scale free network and NW small world network that can be used for representing the online social network structure were constructed and their characteristics were compared. Agent-based simulations were conducted on both networks and results show that BA scale free network is more conductive to spreading rumors and it can facilitate the rumor refutation process at the same time. Rumors paid attention to by more people is likely to spread quicker and broader but for which the rumor refutation process will be more effective. The model provides a useful tool for understanding and predicting the rumor propagation process on online social network during emergency, providing useful instructions for rumor propagation intervention.

scholarly journals A new formulation of compartmental epidemic modelling for arbitrary distributions of incubation and removal times

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0244107
Author(s):  
Pilar Hernández ◽  
Carlos Pena ◽  
Alberto Ramos ◽  
Juan José Gómez-Cadenas
Keyword(s):  
Numerical Approach ◽  
Time Shift ◽  
Scale Free Network ◽  
Logistic Curve ◽  
Agent Based ◽  
Scale Free ◽  
Free Network ◽  
General Distributions ◽  
New Formulation ◽  
Good Agreement

The paradigm for compartment models in epidemiology assumes exponentially distributed incubation and removal times, which is not realistic in actual populations. Commonly used variations with multiple exponentially distributed variables are more flexible, yet do not allow for arbitrary distributions. We present a new formulation, focussing on the SEIR concept that allows to include general distributions of incubation and removal times. We compare the solution to two types of agent-based model simulations, a spatially homogeneous one where infection occurs by proximity, and a model on a scale-free network with varying clustering properties, where the infection between any two agents occurs via their link if it exists. We find good agreement in both cases. Furthermore a family of asymptotic solutions of the equations is found in terms of a logistic curve, which after a non-universal time shift, fits extremely well all the microdynamical simulations. The formulation allows for a simple numerical approach; software in Julia and Python is provided.

scholarly journals Stay with your community: Bridges between clusters trigger expansion of COVID-19

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242766
Author(s):  
Yukio Ohsawa ◽  
Masaharu Tsubokura
Keyword(s):  
Time Series ◽  
Real Life ◽  
Urban Life ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network ◽  
The Government ◽  
The Impact ◽  
Second Wave ◽  
Infection Spread

In this study, the spread of virus infection was simulated using artificial human networks. Here, real-space urban life was modeled as a modified scale-free network with constraints. To date, the scale-free network has been adopted for modeling online communities in several studies. However, in the present study, it has been modified to represent the social behaviors of people where the generated communities are restricted and reflect spatiotemporal constraints in real life. Furthermore, the networks have been extended by introducing multiple cliques in the initial step of network construction and enabling people to contact hidden (zero-degree) as well as popular (large-degree) people. Consequently, four findings and a policy proposal were obtained. First, “second waves” were observed in some cases of the simulations even without external influence or constraints on people’s social contacts or the releasing of the constraints. These waves tend to be lower than the first wave and occur in “fresh” clusters, that is, via the infection of people who are connected in the network but have not been infected previously. This implies that the bridge between infected and fresh clusters may trigger a new spread of the virus. Second, if the network changes its structure on the way of infection spread or after its suppression, a second wave larger than the first can occur. Third, the peak height in the time series of the number of infected cases depends on the difference between the upper bound of the number of people each member actually meets and the number of people they choose to meet during the period of infection spread. This tendency is observed for the two kinds of artificial networks introduced here and implies the impact of bridges between communities on the virus spreading. Fourth, the release of a previously imposed constraint may trigger a second wave higher than the peak of the time series without introducing any constraint so far previously, if the release is introduced at a time close to the peak. Thus, overall, both the government and individuals should be careful in returning to society where people enjoy free inter-community contact.

Research on new evolution model of scale-free network

2009 ◽  
Vol 29 (5) ◽  
pp. 1230-1232
Author(s):  
Hao RAO ◽  
Chun YANG ◽  
Shao-hua TAO
Keyword(s):  
Evolution Model ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network
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