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In this paper, we propose a novel model with dynamically adjusted confidence level of others to investigate the propagation of public opinion on whether to buy chicken in the case of avian influenza infection in humans. We study how people adjust their confidence level in other people's opinions according to their perceived infection risk and how the opinion evolution and epidemic spreading affect each other on different complex networks by taking into account the spreading feature of avian influenza, that is, only people who buy chicken are possible to be infected. The simulation results show that in a closed system, people who support buying chicken and people who are infected can achieve a dynamic balance after a few time-steps, and the final stable state is mainly dependent on the level of people's risk perception, rather than the initial distribution of the different opinions. Our results imply that in the course of the epidemic spread, transparent and timely announcement of the number of infections and the risk of infection can help people take the right self-protection actions, and thus help control the spread of avian influenza. [ABSTRACT FROM AUTHOR]

The world is still in heightened awareness of the potential threat of another influenza pandemic, although it has been 5 years since the 2009 influenza A (H1N1) pandemic. Evaluation of the adopted intervention strategies for handling the 2009 H1N1 pandemic is helpful for dealing with future outbreaks. In this paper we developed a hybrid model combining meta-population and agent-based models to evaluate the containment and mitigation strategies (e.g., contact tracing and quarantine of contacts at assembly sites in the early phase, more medical institutions to detect cases and treat serious patients in the developing phase, and rapid vaccines delivered to students first) for an H1N1 pandemic that were adopted in China. We find that the presented model can retrospectively fit relatively well to the spreading progress through comparison of the simulation results with actual infections data, and it can be used for practical application in evaluating the containment and mitigation strategies for an influenza pandemic if model validation and parameter estimation can be conducted by using actual data. The results will contribute to understanding the spread of viruses and the control of infectious diseases, and to helping government officials create policies on handling an influenza pandemic, especially beneficial to a large and diverse country such as the People’s Republic of China. [ABSTRACT FROM PUBLISHER]

• A probabilistic analysis model of an oil pipeline network accident based on Event-Evolution-Bayesian (EEB) model is proposed. • The EEB model can be used to obtain the probabilities of accident consequences. • The probability analysis result is comprehensive and accurate because the EEB model can refer most of the risk factors. • The EEB model is helpful for risk analysis, decision-making and effect analysis. Over the past several decades, the high frequency of oil pipeline accidents has drawn substantial attention around the world. Many oil pipeline accident analysis models have been established based on the event tree method, the Bayesian network method and Computational Fluid Dynamics (CFD) simulation models. Considering the disadvantages of current models for comprehensively representing the incident evolution process and quantitative analysis for consequences, this paper proposes a probabilistic analysis model for oil pipeline accidents that integrates three methods the event tree (E), the incident evolution diagram (E) and the Bayesian network (B). Therefore, the model is called the "EEB model". The EEB model can identify the initial event and secondary events, illustrate the accident evolution path, identify the key influencing factors, analyze their effects, and calculate the probabilities of different consequences of oil pipeline network accidents. Compared with other models, the EEB model considers more factors, such as key environmental conditions and the emergency response. Probabilistic analysis of different consequences, including casualties, economic losses, environmental pollution and the influence on social order, can be obtained. For a general scenario of an oil pipeline network accident, the probabilities for different consequences are 71.3% for "less than 5 persons affected", 68.2% for "less than 10 million RMB lost", 50.4% for "less than 1 km2 of water pollution" and 59.5% for "influence on social order of less than 100 persons". The risk for the accident can be estimated by assuming the probability of the initial event as P. The model also denotes the emergency targets to be achieved and the response missions to be executed. Based on this information, a response plan can be developed for decision making. Since the incident evolution process is complex, the effects of the influencing factors should be analyzed. The EEB model highlights the significant influences of the water area (e.g., the probability of "10–50 km2 of water pollution" decreases from 38.7% for "near and large" water bodies to 17.4% for "far and small" water bodies) and the emergency response (e.g., the probability of "50–100 million RMB economic loss" increases from 11.5% for an "effective" response to 29.3% for a "poor" response). The probabilistic analysis obtained by the EEB is more comprehensive than those of other models, and the results can be used for risk analysis, decision making and effect analysis of oil pipeline networks. [ABSTRACT FROM AUTHOR]

We report the results of a series of simulations of a susceptible-infected-recovered epidemic model in heterogeneous spatial metapopulation networks with quantitative knowledge of human traveling statistics that human travel behavior obeys scaling laws in the sense of geographical distance and period of waiting time. By tuning the edge length distribution of the spatial metapopulation network, we can conveniently control the distribution of human travel distance. The simulation results show that the occurrence probability of global outbreaks is significantly dependent on the characteristic travel distance, the characteristic waiting time, and the memory effects of human travel. We also present some preliminary results on the effects of travel restrictions in epidemic control.