Yu-Shiuan Tsai

Influences of Resource Limitations and Transmission Costs on Epidemic Simulations and Critical Thresholds in Scale-Free Networks

Critical thresholds represent one of the most important diffusion indicators of epidemic outbreaks. However, we believe that recent studies have overemphasized ways that the power-law connectivity distribution features of social networks affect epidemic dynamics and critical thresholds. As a result, two important factors have been overlooked: resource limitations and transmission costs associated with social interactions and daily contact. Here we present our results from the simultaneous application of mean-field theory and an agent-based network simulation approach for analyzing the effects of resources and costs on epidemic dynamics and critical thresholds. Our main findings are: (a) a significant critical threshold does exist when resources and costs are taken into consideration, and it has a lower bound whenever contagion events occur in scale-free networks; (b) when transmission costs increase or individual resources decrease, critical contagion thresholds in scale-free networks grow linearly and steady density curves shrink linearly; (c) regardless of whether the resources of individuals obey delta, uniform, or normal distributions, they have the same critical thresholds and epidemic dynamics as long as the average value of usable resources remains the same across different scale-free networks; and (d) the spread of epidemics in scale-free networks remains controllable as long as resources are properly restricted and intervention strategy investments are significantly increased.

Integrating epidemic dynamics with daily commuting networks: building a multilayer framework to assess influenza A (H1N1) intervention policies

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.

Simulations for epidemiology and public health education

Recent and potential outbreaks of infectious diseases are triggering interest in predicting epidemic dynamics on a national scale and testing the efficacies of different combinations of public health policies. Network-based simulations are proving their worth as tools for addressing epidemiology and public health issues considered too complex for field investigations and questionnaire analyses. Universities and research centres are therefore using network-based simulations as teaching tools for epidemiology and public health education students, but instructors are discovering that constructing appropriate network models and epidemic simulations are difficult tasks in terms of individual movement and contact patterns. In this paper we will describe (a) a four-category framework (based on demographic and geographic properties) to discuss ways of applying network-based simulation approaches to undergraduate students and novice researchers; (b) our experiences simulating the transmission dynamics of two infectious disease scenarios in Taiwan (HIV and influenza); (c) evaluation results indicating significant improvement in student knowledge of epidemic transmission dynamics and the efficacies of various public health policy suites; and (d) a geospatial modelling approach that integrates a national commuting network as well as multi-scale contact structures.

Analyzing the Patterns of Space-Time Distances for Tracking the Diffusion of an Epidemic

Understanding the dynamics of how infectious diseases spread in time and space is the primary concern of epidemic control and prevention. Spatial methods of tracking the possible sources of infection have not been well developed. The objective of this study is to propose an innovative methodology that combines exploratory spatial-temporal analysis and network topological analysis to identify diffusion patterns and track possible sources of an epidemic. The methodology is composed of two stages. The first stage involves establishing case-to-case distances in space and time. Using a diagram of space-time distances, the space-time clustering of cases can be identified. In the second stage, the network topology of space-time distances is further analyzed. We use two network indicators, degree centrality and network clustering coefficient, to measure the risk of epidemic diffusion. The feasibility of our proposed methodology is assessed by a case study on a dengue epidemic in Kaohsiung, Taiwan. . Our results show that this method can be used to detect the possible origin of an epidemic and to differentiate patterns of spatial diffusion. Spatial-temporal transitions in epidemic progression from local to large-scale transmission are also determined. This study contributes a methodology on modeling spatial-temporal epidemic dynamics.

FLUed: A Novel Four-Layer Model for Simulating Epidemic Dynamics and Assessing Intervention Policies

From the 2003 severe acute respiratory syndrome (SARS) epidemic, to the 2009 swine-origin influenza A (H1N1) pandemic, to the projected highly pathogenic avian influenza A event, emerging infectious diseases highlight the importance of computational epidemiology to assess potential intervention policies. Hence, an important and timely research goal is a general-purpose and extendable simulation model that integrates two major epidemiological factors—age group and population movement—and substantial amounts of demographic, geographic, and epidemiologic data. In this paper, we describe a model that we have named FLUed for Four-layer Universal Epidemic Dynamics that integrates complex daily commuting network data into multiple age-structured compartmental models. FLUed has four contact structures for simulating the epidemic dynamics of emerging infectious diseases, assessing the potential efficacies of various intervention policies, and identifying the potential impacts of spatial-temporal epidemic trends on specific populations. We used data from the seasonal influenza A and 2009 swine-origin influenza A (H1N1) epidemics to validate model reliability and suitability and to assess the potential impacts of intervention policies and variation in initial outbreak areas for novel/seasonal influenza A in Taiwan. We believe that the FLUed model represents an effective tool for public health agencies responsible for initiating early responses to potential pandemics.

Epidemic Dynamics and Thresholds in Agent-Based Simulations under Realistic Resources and Cost Conditions

Critical threshold is one of the most important epidemiological indicators of whether or not an epidemic outbreak has occurred. Recent studies have been overly focused on ways that the power-law connectivity distribution features of social networks affect epidemic dynamics and spreading situations. Two important factors have been overlooked as a result: resource limitations and transmission costs associated with face-to-face interactions and daily contact. Our two main findings are: (a) a significant critical threshold does exist when resources and costs are taken into consideration, and that threshold has a lower bound whenever contagion events occur in scale-free social networks; and (b) the spread of epidemics in scale-free social networks remains controllable as long as resources are properly restricted and consumed costs in the form of public health strategies are significantly increased.

Response to Wilson's note on “Influences of resource limitations and transmission costs on epidemic simulations and critical thresholds in scale-free networks”

James R. Wilson points out what he describes as flaws in our proof in ‘Influences of Resource Limitations and Transmission Costs on Epidemic Simulations and Critical Thresholds in Scale-free Networks’ (Simulation 85(3): 205—219) and offers an alternative steady-state behavior derivation based on our epidemic simulation model. In this response we will explain our definitions for the terms used in our paper and the derivation process for our analysis, then compare and contrast our mathematical model with that proposed by Wilson. We suggest that more compartmental models can be used to support our argument that increasing transmission costs or decreasing individual resources increases the critical threshold of a contagion event in a scale-free network.

A Multilayer Framework to Assess Influenza Intervention Policies

The authors 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. 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 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, 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.

Resources, costs and epidemic thresholds in scale-free social networks

Whether or not a critical threshold exists when epidemic diseases are spread in complex networks is a problem attracting attention from researchers in several disciplines. In 2001, Pastor-Satorras and Vespignani used a computational simulations approach to show that epidemic diseases which spread through scale-free social networks do not have positive critical thresholds. In other words, even if a disease has almost no chance of being transmitted from one person to another, it can still spread throughout a scale-free network. However, they ignored two key factors that have a large impact on epidemic dynamics: economic resource limitations and transmission costs. Every infection event entails tangible or intangible costs in terms of time, energy, or money to the carrier, recipient, or both. Here we apply an agent-based modeling and network-oriented computer simulation approach to analyze the influences of resource limitations and transmission costs on epidemic dynamics and critical thresholds in scale-free networks. Our results indicate that when those resources and costs are taken into consideration, the epidemic dynamics of scale-free networks are very similar to those of homogeneous networks, including the presence of significant critical thresholds. It is hoped that our data will help epidemiologists, public health professionals, and computer scientists working with core questions of epidemic diseases, estimates of epidemic dynamics and spreading, and effective public health policies and immunization strategies.