9 articles matched your search for
Data-Driven Simulation, Epidemiology, Network-Based Simulation, SARS
Journal of Artificial Societies and Social Simulation 6 (3) 8
Kyeywords: Agent-based models, Ethnography, Epidemiology, Drug Use, Netlogo
Abstract: In this tongue-in-cheek commentary the author takes a serious look at the problem of translating ethnographic conclusions into simple functions as a means to the end of building an agent-based simulation in the Netlogo language. Specifically, the goal is to take the simple fact that stories about illicit drugs have a lot to do with whether or not they will be used and see if an agent-based model can produce an epidemic incidence curve under the appropriate conditions. This commentary has less to do with the model and more to do with figuring out what kinds of numbers make sense. Based on the principle that mathematical ignorance is bliss, the author concludes that the most important thing is that number construction reflects the differences that make a difference in the ethnographic work, where the discovery of what the significant differences in fact were was a major result of the research. Support by NIH/NIDA grant DA 10736 is gratefully acknowledged.
Chung-Yuan Huang, Chuen-Tsai Sun, Ji-Lung Hsieh and Holin Lin
Journal of Artificial Societies and Social Simulation 7 (4) 2
Kyeywords: SARS, Epidemiological Model, Cellular Automata, Mirror Identity, Small World Network, Public Health Policy
Abstract: The authors propose a novel small-world model that makes use of cellular automata with the mirror identities of daily-contact social networks to simulate epidemiological scenarios. We established the mirror identity concept (a miniature representation of frequently visited places) to acknowledge human long-distance movement and geographic mobility. Specifically, the model was used to a) simulate the dynamics of SARS transmission in Singapore, Taipei, and Toronto and b) discuss the effectiveness of the respective public health policies of those cities. We believe the model can be applied to influenza, enteroviruses, AIDS, and other contagious diseases according to the various needs of health authorities.
Jill Bigley Dunham
Journal of Artificial Societies and Social Simulation 9 (1) 3
Kyeywords: Epidemiology, Social Networks, Agent-Based Simulation, MASON Toolkit
Abstract: This paper outlines the design and implementation of an agent-based epidemiological simulation system. The system was implemented in the MASON toolkit, a set of Java-based agent-simulation libraries. This epidemiological simulation system is robust and extensible for multiple applications, including classroom demonstrations of many types of epidemics and detailed numerical experimentation on a particular disease. The application has been made available as an applet on the MASON web site, and as source code on the author\'s web site.
Teruhiko Yoneyama, Sanmay Das and Mukkai Krishnamoorthy
Journal of Artificial Societies and Social Simulation 15 (1) 5
Kyeywords: Data-Driven Simulation, Epidemiology, Network-Based Simulation, SARS
Abstract: Pandemics can cause immense disruption and damage to communities and societies. Thus far, modeling of pandemics has focused on either large-scale differential equation models like the SIR and the SEIR models, or detailed micro-level simulations, which are harder to apply at a global scale. This paper introduces a hybrid model for pandemics that considers both global and local spread of infection. We hypothesize that the spread of an infectious disease between regions is significantly influenced by global traffic patterns and that the spread within a region is influenced by local conditions. Thus we model the spread of pandemics considering the connections between regions for the global spread of infection and population density based on the SEIR model for the local spread of infection. We validate our hybrid model by carrying out a simulation study for the spread of the SARS pandemic of 2002-2003 using available data on population, population density, and traffic networks between different regions. While it is well-known that international relationships and global traffic patterns significantly influence the spread of pandemics, our results show that integrating these factors into relatively simple models can greatly improve the results of modeling disease spread.
Elizabeth Hunter, Brian Mac Namee and John D. Kelleher
Journal of Artificial Societies and Social Simulation 20 (3) 2
Kyeywords: Agent-Based, Epidemiology, Infectious Disease, Simulation, Model, Taxonomy
Abstract: Agent-based simulation modelling has been used in many epidemiological studies on infectious diseases. However, because agent based modelling is a field without any clear protocol for developing simulations the researcher is given a high amount of flexibility. This flexibility has led to many different forms of agent-based epidemiological simulations. In this paper we review the existing literature on agent-based epidemiological simulation models. From our literature review we identify key similarities and differences in the exisiting simulations. We then use these similarities and differences to create a taxonomy of agent-based epidemiological models and show how the taxonomy can be used.
Serge Wiltshire, Asim Zia, Christopher Koliba, Gabriela Bucini, Eric Clark, Scott Merrill, Julie Smith and Susan Moegenburg
Journal of Artificial Societies and Social Simulation 22 (2) 8
Kyeywords: Agent-Based Modeling, Network Analytics, Computational Epidemiology, Evolutionary Computation, Livestock Production
Abstract: We developed an agent-based susceptible/infective model which simulates disease incursions in the hog production chain networks of three U.S. states. Agent parameters, contact network data, and epidemiological spread patterns are output after each model run. Key network metrics are then calculated, some of which pertain to overall network structure, and others to each node's positionality within the network. We run statistical tests to evaluate the extent to which each network metric predicts epidemiological vulnerability, finding significant correlations in some cases, but no individual metric that serves as a reliable risk indicator. To investigate the complex interactions between network structure and node positionality, we use a genetic programming (GP) algorithm to search for mathematical equations describing combinations of individual metrics — which we call "meta-metrics" — that may better predict vulnerability. We find that the GP solutions — the best of which combine both global and node-level metrics — are far better indicators of disease risk than any individual metric, with meta-metrics explaining up to 91% of the variability in agent vulnerability across all three study areas. We suggest that this methodology could be applied to aid livestock epidemiologists in the targeting of biosecurity interventions, and also that the meta-metric approach may be useful to study a wide range of complex network phenomena.
Elizabeth Hunter, Brian Mac Namee and John Kelleher
Journal of Artificial Societies and Social Simulation 23 (4) 14
Kyeywords: Hybrid, Agent-Based, Equation Based, Infectious Disease, Simulation, Epidemiology
Abstract: Both agent-based models and equation-based models can be used to model the spread of an infectious disease. Equation-based models have been shown to capture the overall dynamics of a disease outbreak while agent-based models are able to capture heterogeneous characteristics of agents that drive the spread of an outbreak. However, agent-based models are computationally intensive. To capture the advantages of both the equation-based and agent-based models, we create a hybrid model where the disease component of the hybrid model switches between agent-based and equation-based. The switch is determined using the number of agents infected. We first test the model at the town level and then the county level investigating different switch values and geographic levels of switching. We find that a hybrid model is able to save time compared to a fully agent-based model without losing a significant amount of fidelity.
Fabian Lorig, Emil Johansson and Paul Davidsson
Journal of Artificial Societies and Social Simulation 24 (3) 5
Kyeywords: SARS-CoV-2, Transmission Processes, Effects of Interventions, Non-Pharmaceutical Interventions, Literature Study, PRISMA
Abstract: When planning interventions to limit the spread of Covid-19, the current state of knowledge about the disease and specific characteristics of the population need to be considered. Simulations can facilitate policy making as they take prevailing circumstances into account. Moreover, they allow for the investigation of the potential effects of different interventions using an artificial population. Agent-based Social Simulation (ABSS) is argued to be particularly useful as it can capture the behavior of and interactions between individuals. We performed a systematic literature review and identified 126 articles that describe ABSS of Covid-19 transmission processes. Our review showed that ABSS is widely used for investigating the spread of Covid-19. Existing models are very heterogeneous with respect to their purpose, the number of simulated individuals, and the modeled geographical region as well as how they model transmission dynamics, disease states, human behavior, and interventions. To this end, a discrepancy can be identified between the needs of policy makers and what is implemented by the simulation models. This also includes how thoroughly the models consider and represent the real-world, e.g., in terms of factors that affect the transmission probability or how humans make decisions. Shortcomings were also identified in the transparency of the presented models, e.g., in terms of documentation or availability, as well as in their validation, which might limit their suitability for supporting decision-making processes. We discuss how these issues can be mitigated to further establish ABSS as powerful tool for crisis management.
Daniele Baccega, Simone Pernice, Pietro Terna, Paolo Castagno, Giovenale Moirano, Lorenzo Richiardi, Matteo Sereno, Sergio Rabellino, Milena Maria Maule and Marco Beccuti
Journal of Artificial Societies and Social Simulation 25 (3) 2
Kyeywords: Agent-Based Simulation, SARS-CoV-2, Non-Pharmaceutical Interventions, Surveillance Testing, School
Abstract: Many governments enforced physical distancing measures during the COVID-19 pandemic to avoid the collapse of often fragile and overloaded health care systems. Following the physical distancing measures, school closures seemed unavoidable to keep the transmission of the pathogen under control, given the potentially high-risk of these environments. Nevertheless, closing schools was considered an extreme and the last resort of governments, and so various non-pharmaceutical interventions in schools were implemented to reduce the risk of transmission. By means of an agent-based model, we studied the efficacy of active surveillance strategies in the school environment. Simulations settings provided hypothetical although realistic scenarios which allowed us to identify the most suitable control strategy to avoid massive school closures while adapting to contagion dynamics. Reducing risk by means of public policies explored in our study is essential for both health authorities and school administrators.