Browsing by Author "Wen, Tzai-Hung"

Now showing 1 - 6 of 6

A computer virus spreading model based on resource limitations and interaction costs
(2013-03-01)
Huang, Chung-Yuan
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Lee, Chun-Liang
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Wen, Tzai-Hung
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Sun, Chuen-Tsai
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資訊工程學系
Computer viruses are major threats to Internet security and privacy, therefore many researchers are addressing questions linked to virus propagation properties, spreading models, epidemic dynamics, tipping points, and control strategies. We believe that two important factors - resource limitations and costs - are being overlooked in this area due to an overemphasis on power-law connectivity distributions of scale-free networks affecting computer virus epidemic dynamics and tipping points. The study show (a) a significant epidemic tipping point does exists when resource limitations and costs are considered, with the tipping point exhibiting a lower bound; (b) when interaction costs increase or usable resources decrease, epidemic tipping points in scale-free networks grow linearly while density curves decrease linearly; (c) regardless of whether Internet user resources obey delta, uniform, or normal distributions, they retain the same epidemic dynamics and tipping points as long as the average value of those resources remains unchanged across different scale-free networks; (d) it is possible to control the spread of a computer virus in a scale-free network if resources are restricted and if costs associated with infection events are significantly increased through the use of a throttling strategy. (C) 2012 Elsevier Inc. All rights reserved.
A Multilayer Epidemiological Model Integrating Daily Commuting Network
(2010)
Tsai, Yu-Shiuan
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Huang, Chung-Yuan
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Wen, Tzai-Hung
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Sun, Chuen-Tsai
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資訊工程學系
To assess the impacts of various interventions on epidemic dynamics, the authors propose an integrated epidemiological simulation framework called Multilayer Epidemic Dynamics Simulator (MEDSim), which combines four contact structures: within age group, between age groups, daily commute, and nationwide interaction. We use the daily commuting network to simulate outbreak locations and intervention scenarios for the 2009 A/H1N1 influenza epidemic in Taiwan. Our three main findings are: (a) lower transmission rates and earlier intervention activation times did not reduce total numbers of infected cases, but did delay peak times; (b) when transmission rate was decreased by a minimum of 70%, significant slowing-down effects were observed when interventions were activated before the 50th new case (c) peaks times were achieved more quickly when initial outbreaks occurred in urban rather than rural areas. The innovative simulation framework reflects the dynamic processes of epidemics under different intervention scenarios, thus clarifying the effects of complex contact structures on disease transmission dynamics.
A Network-based Simulation Architecture for Studying Epidemic Dynamics
(2010-05-01)
Huang, Chung-Yuan
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Tsai, Yu-Shiuan
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Wen, Tzai-Hung
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資訊工程學系
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Department of Computer Science
Epidemiology instructors are collaborating with computer scientists to construct network-based simulations to recreate infectious disease transmission dynamics and to examine intervention strategy efficacies. Here we propose an architecture based on demographic and geographic properties to support the learning efforts of undergraduate students and novice researchers, and describe our simulations of HIV, SARS, and influenza transmission dynamics in Taiwan. Our findings are offered to support the construction of new network-based epidemic simulations.
A Novel Private Attitude and Public Opinion Dynamics Model for Simulating Pluralistic Ignorance and Minority Influence
(2014-06-30)
Huang, Chung-Yuan
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Wen, Tzai-Hung
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交大名義發表
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National Chiao Tung University
Pluralistic ignorance, a well-documented socio-psychological conformity phenomenon, involves discrepancies between private attitude and public opinion in certain social contexts. However, continuous opinion dynamics models based on a bounded confidence assumption fail to accurately model pluralistic ignorance because they do not address scenarios in which non-conformists do not need to worry about holding and expressing conflicting opinions. Such scenarios reduce the power of continuous opinion dynamics models to explain why certain groups doubt or change their opinions in response to minority views. To simulate the effects of (a) private acceptance of informational social influence and (b) public compliance with normative social influence on pluralistic ignorance and minority influences, we have created an agent-based simulation model in which attitude and opinion respectively represent an agent\'s private and expressed thoughts. Results from a series of simulation experiments indicate model validity equal to or exceeding those of existing opinion dynamics models that are also based on the bounded confidence assumption, but with different dynamics and outcomes in terms of collective opinion and attitude. The results also support the use of our proposed model for computational social psychology applications.
EpiRank: Modeling Bidirectional Disease Spread in Asymmetric Commuting Networks
(2019-04-01)
Huang, Chung-Yuan
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Chin, Wei-Chien-Benny
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Wen, Tzai-Hung
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Fu, Yu-Hsiang
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Tsai, Yu-Shivan
Commuting network flows are generally asymmetrical, with commuting behaviors bi-directionally balanced between home and work locations, and with weekday commutes providing many opportunities for the spread of infectious diseases via direct and indirect physical contact. The authors use a Markov chain model and PageRank-like algorithm to construct a novel algorithm called EpiRank to measure infection risk in a spatially confined commuting network on Taiwan island. Data from the country's 2000 census were used to map epidemic risk distribution as a commuting network function. A daytime parameter was used to integrate forward and backward movement in order to analyze daily commuting patterns. EpiRank algorithm results were tested by comparing calculations with actual disease distributions for the 2009 H1N1 influenza outbreak and enterovirus cases between 2000 and 2008. Results suggest that the bidirectional movement model outperformed models that considered forward or backward direction only in terms of capturing spatial epidemic risk distribution. EpiRank also outperformed models based on network indexes such as PageRank and HITS. According to a sensitivity analysis of the daytime parameter, the backward movement effect is more important than the forward movement effect for understanding a commuting network's disease diffusion structure. Our evidence supports the use of EpiRank as an alternative network measure for analyzing disease diffusion in a commuting network.
Integrating epidemic dynamics with daily commuting networks: building a multilayer framework to assess influenza A (H1N1) intervention policies
(2011-05-01)
Tsai, Yu-Shiuan
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Huang, Chung-Yuan
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Wen, Tzai-Hung
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Sun, Chuen-Tsai
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Yen, Muh-Yong
We describe an innovative simulation framework that combines daily commuting network data with a commonly used population-based transmission model to assess the impacts of various interventions on epidemic dynamics in Taiwan. Called the Multilayer Epidemic Dynamics Simulator (MEDSim), our proposed framework has four contact structures: within age group, between age groups, daily commute, and nationwide interaction. To test model flexibility and generalizability, we simulated outbreak locations and intervention scenarios for the 2009 swine-origin influenza A (H1N1) epidemic. Our results indicate that lower transmission rates and earlier intervention activation times did not reduce total numbers of infected cases, but did delay peak times. When the transmission rate was decreased by a minimum of 70%, significant epidemic peak delays were observed when interventions were activated before new case number 50; no significant effects were noted when the transmission rate was decreased by less than 30%. Observed peaks occurred more quickly when initial outbreaks took place in urban rather than rural areas. According to our results, the MEDSim provides insights that reflect the dynamic processes of epidemics under different intervention scenarios, thus clarifying the effects of complex contact structures on disease transmission dynamics.