Chung-Yuan Huang

A Novel Small-World Model: Using Social Mirror Identities for Epidemic Simulations

The authors propose a small-world network model that combines cellular automata with the social mirror identities of daily-contact networks for purposes of performing epidemiological simulations. The social mirror identity concept was established to integrate human long-distance movement and daily visits to fixed locations. After showing that the model is capable of displaying such small-world effects as low degree of separation and relatively high degree of clustering on a societal level, the authors offer proof of its ability to display R 0 properties—considered central to all epidemiological studies. To test their model, they simulated the 2003 severe acute respiratory syndrome (SARS) outbreak.

Parameter adaptation within co-adaptive learning classifier systems

The authors propose a co-adaptive approach to controlling parameters for coevolution-based learning classifier systems. By taking advantage of the on-line incremental learning capability of such systems, solutions can be produced that completely cover a target problem. The system combines the advantages of both adaptive and self-adaptive parameter-control approaches. Using a coevolution model means that two learning classifier systems can operate in parallel to simultaneously solve target and parameter-setting problems. Furthermore, the approach needs very little time to become efficient in terms of latent learning, since it only requires small amounts of information on performance metrics during early run-time stages. Our experimental results show that the proposed system outperforms comparable models regardless of a problem’s stationary/non-stationary status.

A computer virus spreading model based on resource limitations and interaction costs

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.

Mining Bridge and Brick Motifs From Complex Biological Networks for Functionally and Statistically Significant Discovery

A major task for postgenomic systems biology researchers is to systematically catalogue molecules and their interactions within living cells. Advancements in complex-network theory are being made toward uncovering organizing principles that govern cell formation and evolution, but we lack understanding of how molecules and their interactions determine how complex systems function. Molecular bridge motifs include isolated motifs that neither interact nor overlap with others, whereas brick motifs act as network foundations that play a central role in defining global topological organization. To emphasize their structural organizing and evolutionary characteristics, we define bridge motifs as consisting of weak links only and brick motifs as consisting of strong links only, then propose a method for performing two tasks simultaneously, which are as follows: 1) detecting global statistical features and local connection structures in biological networks and 2) locating functionally and statistically significant network motifs. To further understand the role of biological networks in system contexts, we examine functional and topological differences between bridge and brick motifs for predicting biological network behaviors and functions. After observing brick motif similarities between E. coli and S. cerevisiae, we note that bridge motifs differentiate C. elegans from Drosophila and sea urchin in three types of networks. Similarities (differences) in bridge and brick motifs imply similar (different) key circuit elements in the three organisms. We suggest that motif-content analyses can provide researchers with global and local data for real biological networks and assist in the search for either isolated or functionally and topologically overlapping motifs when investigating and comparing biological system functions and behaviors.

Bridge and brick network motifs: Identifying significant building blocks from complex biological systems

OBJECTIVE A major focus in computational system biology research is defining organizing principles that govern complex biological network formation and evolution. The task is considered a major challenge because network behavior and function prediction requires the identification of functionally and statistically important motifs. Here we propose an algorithm for performing two tasks simultaneously: (a) detecting global statistical features and local connection structures in biological networks, and (b) locating functionally and statistically significant network motifs. METHODS AND MATERIAL Two gene regulation networks were tested: the bacteria Escherichia coli and the yeast eukaryote Saccharomyces cerevisiae. To understand their structural organizing principles and evolutionary mechanisms, we defined bridge motifs as composed of weak links only or of at least one weak link and multiple strong links, and defined brick motifs as composed of strong links only. RESULTS After examining functional and topological differences between bridge and brick motifs for predicting biological network behaviors and functions, we found that most genetic network motifs belong to the bridge category. This strongly suggests that the weak-tie links that provide unique paths for signal control significantly impact the signal processing function of transcription networks. CONCLUSIONS Bridge and brick motif content analysis can provide researchers with global and local views of individual real networks and help them locate functionally and topologically overlapping or isolated motifs for purposes of investigating biological system functions, behaviors, and similarities.

Teaching through Simulation: Epidemic Dynamics and Public Health Policies

A growing number of epidemiologists are now working to refine computer simulation methods for diseases as a strategy for helping public policy decision-makers assess the potential efficacies of tactics in response to newly emerging epidemics. These efforts spiked after the SARS outbreak of 2002– 2003. Here we describe our attempt to help novice researchers understand epidemic dynamics with the help of the cellular automata with social mirror identity model (CASMIM), a small-world epidemiological simulation system created by Huang et al. in 2004. Using the SARS scenario as a teaching example, we designed three sets of instructional experiments to test our assumptions regarding (i) simulating epidemic transmission dynamics and associated public health policies, (ii) assisting with understanding the properties and efficacies of various public health policies, (iii) constructing an effective, low-cost (in social and financial terms) and executable suite of epidemic prevention strategies, and (iv) reducing the difficulties and costs associated with learning epidemiological concepts. With the aid of the proposed simulation tool, novice researchers can create various scenarios for discovering epidemic dynamics and for exploring applicable combinations of prevention or suppression strategies. Results from an evaluative test indicate a significant improvement in the ability of a group of college students with little experience in epidemiology to understand epidemiological concepts.

Collective opinion and attitude dynamics dependency on informational and normative social influences

In a continuous opinion dynamics model using a bounded confidence assumption, individuals can only influence each other’s opinions when those opinions are sufficiently close. However, we often observe real-world cases in which opinions are very different, yet individuals feel compelled to change their ideas to conform with their peers or superiors (or in rare cases, are willing to change them voluntarily). In other words, individuals tend to consider the practical value of conformity and worry about rejection if they do not adopt the opinions of the majority. To explore the influences of private acceptance of informational social influences and public compliance with normative social influences on collective opinion and attitude dynamics, we have created a model in which attitude and opinion respectively represent an agent’s private and expressed thoughts. Results from a series of simulation experiments indicate that our simplified model is as valid as previous opinion dynamics models also based on the bounded confidence assumption, but with different dynamics and outcomes regarding group opinion and attitude. To demonstrate our proposed model’s potential value and applications, we briefly discuss two issues of import to sociologists: pluralistic ignorance formation and destruction and minority influence.

A Network-based Simulation Architecture for Studying Epidemic Dynamics

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.

Identifying Super-Spreader Nodes in Complex Networks

Identifying the most influential individuals spreading information or infectious diseases can assist or hinder information dissemination, product exposure, and contagious disease detection. Hub nodes, high betweenness nodes, high closeness nodes, and high -shell nodes have been identified as good initial spreaders, but efforts to use node diversity within network structures to measure spreading ability are few. Here we describe a two-step framework that combines global diversity and local features to identify the most influential network nodes. Results from susceptible-infected-recovered epidemic simulations indicate that our proposed method performs well and stably in single initial spreader scenarios associated with various complex network datasets.

Optimal Installation Locations for Automated External Defibrillators in Taipei 7-Eleven Stores: Using GIS and a Genetic Algorithm with a New Stirring Operator

Immediate treatment with an automated external defibrillator (AED) increases out-of-hospital cardiac arrest (OHCA) patient survival potential. While considerable attention has been given to determining optimal public AED locations, spatial and temporal factors such as time of day and distance from emergency medical services (EMSs) are understudied. Here we describe a geocomputational genetic algorithm with a new stirring operator (GANSO) that considers spatial and temporal cardiac arrest occurrence factors when assessing the feasibility of using Taipei 7-Eleven stores as installation locations for AEDs. Our model is based on two AED conveyance modes, walking/running and driving, involving service distances of 100 and 300 meters, respectively. Our results suggest different AED allocation strategies involving convenience stores in urban settings. In commercial areas, such installations can compensate for temporal gaps in EMS locations when responding to nighttime OHCA incidents. In residential areas, store installations can compensate for long distances from fire stations, where AEDs are currently held in Taipei.