scholarly journals Effect of human behavior on the evolution of viral strains during an epidemic

2021 ◽  
Author(s):  
Asma Azizi ◽  
Natalia L. Komarova ◽  
Dominik Wodarz
Keyword(s):  
New Orleans ◽  
Human Behavior ◽  
Behavioral Changes ◽  
Disease Avoidance ◽  
Agent Based ◽  
Scale Free ◽  
Behavioral Traits ◽  
The Social ◽  
Scale Free Networks ◽  
Cross Immunity

AbstractIt is well known in the literature that human behavior can change as a reaction to disease observed in others, and that such behavioral changes can be an important factor in the spread of an epidemic. It has been noted that human behavioral traits in disease avoidance are under selection in the presence of infectious diseases. Here we explore a complimentary trend: the pathogen itself might experience a force of selection to become less “visible”, or less “symptomatic”, in the presence of such human behavioral trends. Using a stochastic SIR agent-based model, we investigated the co-evolution of two viral strains with cross-immunity, where the resident strain is symptomatic while the mutant strain is asymptomatic. We assumed that individuals exercised self-regulated social distancing (SD) behavior if one of their neighbors was infected with a symptomatic strain. We observed that the proportion of asymptomatic carriers increased over time with a stronger effect corresponding to higher levels of self-regulated SD. Adding mandated SD made the effect more significant, while the existence of a time-delay between the onset of infection and the change of behavior reduced the advantage of the asymptomatic strain. These results were consistent under random geometric networks, scale-free networks, and a synthetic network that represented the social behavior of the residents of New Orleans.

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.

Modeling the Social Structure of Online Discussion Groups: Implications for Controlling Viral Marketing

2007 ◽  
Vol 22 (3) ◽  
pp. 27-41
Author(s):  
Alexandre Steyer ◽  
Renaud Garcia-Bardidia ◽  
Pascale Quester
Keyword(s):  
Social Structure ◽  
Information Dissemination ◽  
Online Discussion ◽  
Viral Marketing ◽  
Discussion Groups ◽  
The Internet ◽  
Scale Free ◽  
Forecasting Methods ◽  
The Social ◽  
Scale Free Networks

This research examines the process by which information diffuses within newsgroups on the Internet. Our results empirically demonstrate that these newsgroups are scale-free networks where the potential for information dissemination is important, albeit somewhat unpredictable. This leads us to reconsider the econometric foundations of forecasting methods typically used by marketers.

Agent-based simulation of the dynamics of malware propagation in scale-free networks

SIMULATION ◽  
2016 ◽  
Vol 92 (7) ◽  
pp. 709-722 ◽  
Author(s):  
Soodeh Hosseini ◽  
Mohammad Abdollahi Azgomi ◽  
Adel Rahmani Torkaman
Keyword(s):  
Agent Based Simulation ◽  
Agent Based ◽  
Scale Free ◽  
Malware Propagation ◽  
Scale Free Networks

scholarly journals Agent-based evolving network modeling: a new simulation method for modeling low prevalence infectious diseases

2021 ◽  
Author(s):  
Matthew Eden ◽  
Rebecca Castonguay ◽  
Buyannemekh Munkhbat ◽  
Hari Balasubramanian ◽  
Chaitra Gopalappa
Keyword(s):  
Infectious Diseases ◽  
Network Modeling ◽  
Compartmental Modeling ◽  
Agent Based ◽  
Network Generation ◽  
Scale Free ◽  
Individual Level ◽  
Scale Free Networks ◽  
The Individual ◽  
Low Prevalence

AbstractAgent-based network modeling (ABNM) simulates each person at the individual-level as agents of the simulation, and uses network generation algorithms to generate the network of contacts between individuals. ABNM are suitable for simulating individual-level dynamics of infectious diseases, especially for diseases such as HIV that spread through close contacts within intricate contact networks. However, as ABNM simulates a scaled-version of the full population, consisting of all infected and susceptible persons, they are computationally infeasible for studying certain questions in low prevalence diseases such as HIV. We present a new simulation technique, agent-based evolving network modeling (ABENM), which includes a new network generation algorithm, Evolving Contact Network Algorithm (ECNA), for generating scale-free networks. ABENM simulates only infected persons and their immediate contacts at the individual-level as agents of the simulation, and uses the ECNA for generating the contact structures between these individuals. All other susceptible persons are modeled using a compartmental modeling structure. Thus, ABENM has a hybrid agent-based and compartmental modeling structure. The ECNA uses concepts from graph theory for generating scale-free networks. Multiple social networks, including sexual partnership networks and needle sharing networks among injecting drug-users, are known to follow a scale-free network structure. Numerical results comparing ABENM with ABNM estimations for disease trajectories of hypothetical diseases transmitted on scale-free contact networks are promising for application to low prevalence diseases.

The Social Development Model

2018 ◽  
pp. 223-247 ◽  
Author(s):  
Christopher Cambron ◽  
Richard F. Catalano ◽  
J. David Hawkins
Keyword(s):  
General Theory ◽  
Social Development ◽  
Human Behavior ◽  
Explanatory Power ◽  
Development Model ◽  
Antisocial Behaviors ◽  
Future Directions ◽  
Social Development Model ◽  
The Social ◽  
Empirical Tests

This chapter presents an overview of the social development model (SDM)—a general theory of human behavior that integrates research on risk and protective factors into a coherent model. The goal of this synthesis is to provide more explanatory power than its component theories. This chapter first specifies the model constructs and their hypothesized relationships to prosocial and antisocial behaviors. It then provides a synthesis of what has been learned from empirical tests of social development hypotheses for predicting pro- and antisocial behaviors. This chapter also highlights interventions derived from the SDM and summarizes their impact on pro- and antisocial behaviors. Finally, the chapter concludes by presenting future directions for SDM-based research.

scholarly journals Dynamics analysis of an online gambling spreading model on scale-free networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yu Kong ◽  
Tao Li ◽  
Yuanmei Wang ◽  
Xinming Cheng ◽  
He Wang ◽  
...  
Keyword(s):  
Network Topology ◽  
Negative Impact ◽  
Online Gambling ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Scale Free ◽  
Spreading Model ◽  
Spreading Dynamics ◽  
Scale Free Networks ◽  
Gambling Policy

AbstractNowadays, online gambling has a great negative impact on the society. In order to study the effect of people’s psychological factors, anti-gambling policy, and social network topology on online gambling dynamics, a new SHGD (susceptible–hesitator–gambler–disclaimer) online gambling spreading model is proposed on scale-free networks. The spreading dynamics of online gambling is studied. The basic reproductive number $R_{0}$ R 0 is got and analyzed. The basic reproductive number $R_{0}$ R 0 is related to anti-gambling policy and the network topology. Then, gambling-free equilibrium $E_{0}$ E 0 and gambling-prevailing equilibrium $E_{ +} $ E + are obtained. The global stability of $E_{0}$ E 0 is analyzed. The global attractivity of $E_{ +} $ E + and the persistence of online gambling phenomenon are studied. Finally, the theoretical results are verified by some simulations.

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