Agent-based population model used to identify and evaluate dog population management strategies L. Kisiel, A. Jones-Bitton, A.L. Greer; Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON Canada; CEFUTREMA

Developing countries are faced with finding novel and humane ways to permanently reduce and control their dog population. Agent-based models developed to describe dog populations represent a unique, platform for using computer based simulation to identify control strategies with the greatest potential for success, aid in the design of more effective control measures, and provide a means to evaluate the success of different interventions.
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The Effect of Cellular Interactions on Cancer Cell Growth Using Evolutionary Game Theory Mihir Paithane, Student, Science, Technology, Engineering, and Math (STEM) Center at Mills E. Godwin High School

In this experiment, game theory was used to assess the interactions between three cell phenotypes usually found in cancer. The three defined cells were autonomous growth cells, invasive and motile malignant cells, and cells that performed anaerobic glycolysis. Based on preset variables in the payoff matrix, analytical equations were deduced that allowed for the analysis of the proportion of autonomous growth and malignant cells in a tumor. AnyLogic was also used to simulate the interactions between cancerous and normal cells.
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Towards Closed Loop Modeling: Evaluatng The Prospects for Creating Recurrently Regrounded Aggregate Simulation Models Using Particle Filtering Nathaniel Osgood, Juxin Liu, University of Saskatchewan 106 Wiggins Road, University of Saskatchewan Saskatoon. Winter Simulation Conference, 2014

Public health agencies traditionally rely heavily on epidemiological reporting for notifiable disease control, but increasingly apply simulation models for forecasting and to understand intervention tradeoffs. Unfortunately, such models traditionally lack capacity to easily incorporate information from epidemiological data feeds.
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An Agent-Based Explanation for 20th Century Living Situation Changes in America’s Severely and Persistently Mentally Ill Population Kyle L. Johnson, Dr. Dimitris Alevras, IBM Global Business Services; Dr. John Docherty, Dr. Erin Falconer, Otsuka Medical Affairs. AnyLogic Conference 2014

The largest public mental health facility in the United States is not a hospital; it is the Los Angeles County Jail. This paper describes an agent-based approach to explaining why prisons and jails house so many of America’s most seriously mentally ill. It traces this fact to the differing ways in which various housing situations react to mental illness and to legislation passed in the 1960’s, which allocated public funding away from state mental hospitals.
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Comparison between Individual-based and Aggregate Models in the context of Tuberculosis Transmission Tian, Y. and Osgood, N., Proceedings, The 29 th International conference of the System Dynamics Society. July 2011, Washington, D.C.

The desire to better understand the transmission of infectious disease in the real world has motivated the representation of epidemic diffusion in the context of quantitative simulation. In recent decades, both individual-based (such as Agent-Based) models and aggregate models (such as System Dynamics) are widely used in epidemiological modeling. This paper compares the difference between system dynamics models and agent-based models in the context of Tuberculosis (TB) transmission, considering smoking as a risk factor.
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Representing Progression and Interactions of Comorbidities in Aggregate and Individual-Based Systems Models Osgood, N. Proceedings, The 27th International Conference of the System Dynamics Society, July 2009, Albuquerque

Healthcare simulation models have attracted significant offered important insights in to health policy selection. More complete accounting for the cost and health implications of upstream interventions is hindered by the need to consider impact on, and interactions between, multiple comorbidities. Within this paper, we explore several distinct approaches for representing comorbidities, some of them at the aggregate level, and some of them at the individual level. All of these representations have the virtue of being declarative, in that they allow the user to focus on what is to be characterized, rather than how it is to be implemented. Our exploration suggests that while several aggregate representations of comorbidities are possible, they suffer from a variety of shortcomings, ranging from low fidelity to combinatorial blowup. While individual-level representations impose a heavy performance load, greater difficulties in calibration and less rapid analysis, such representations do offer greater transparency, modifiability, scalability, and modularity, and ease of representing transmission and influence networks. With much to recommend each approach, further research is needed to shed additional light on the tradeoffs and identify situations where one representation is preferable to another.
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Agent-Based Simulation of a Tuberculosis Epidemic Parastu Kasaie, David W. Dowdy, W. David Kelton, Proceedings of the 2013 Winter Simulation Conference

We propose an epidemic agent-based simulation model for disease (TB) transmission dynamics study and to find out the role of various contact networks. Our model simulates the TB epidemic course across a single population and uses a hierarchical network of contacts in three levels, typical to the transmission of airborne diseases (Mossong et al. 2005). Parameters are chosen from the literature, and the model is calibrated to a setting of high TB incidence. We use our model to study the transmission dynamics at an individual level with regard to the timing and distribution of secondary infections from a single source. The average time for disease diffusion to reach 50% of infections at an individual level is estimated, and the timing patterns are compared among different networks. We perform sensitivity analysis of results with regard to multiple parameter values, and discuss the implications for TB control policy.
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A Modular Simulation Model For Assessing Interventions For Abdominal Aortic Aneurysms Christoph Urach, Günther Zauner, Gottfried Endel, Ingrid Wilbacher, Felix Breitenecker, Proceedings of the 2013 Winter Simulation Conference

This paper discusses the development of an individual based simulation of interventions for better treatment of patients with abdominal aortic aneurysms (AAA). The interdisciplinary subject required collaboration of medical doctors, Health Technology Assessment (HTA) experts and modelers.
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Prospective Healthcare Decision-Making By Combined System Dynamics, Discrete-Event And Agent-Based Simulation Anatoli Djanatliev, Reinhard German, Proceedings of the 2013 Winter Simulation Conference

Prospective Health Technology Assessment allows early decision making for innovative health care technologies. In our recent publications a hybrid simulation approach with System Dynamics and Agent-Based Modeling has been presented. This paper presents a mechanism to generate agents dynamically from SD models and extends the previously presented hybrid approach by process-oriented Discrete Event Simulation for hospital modeling.
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Large Scale Healthcare Modeling by Hybrid Simulation Techniques using AnyLogic Anatoli Djanatliev and Reinhard German, Computer Networks and Communication Systems Department of Computer Science 7. Proceedings of the 6th International ICST Conference on Simulation Tools and Techniques

This paper describes a methodical and practical approach of hybrid model creation using the simulation tool AnyLogic. We focus on general modeling aspects and on advanced techniques using a Level-Based Architecture that help to develop large scale hybrid simulation models. An implementation of a stroke therapy use-case and its simulation results will be discussed. Finally, some practical ideas for validation will be outlined, as we experienced during the stroke use-case development.
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