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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Partial Paradigm Hiding and Reusability in Hybrid Simulation Modeling Using the Frameworks Health-DS and I7-Anyenergy A. Djanatliev, P. Bazan, R. German, University of Erlangen-Nuremberg. Winter Simulation Conference, 2014.

Many complex real-world problems which are difficult to understand can be solved by discrete or continuous simulation techniques, such as Discrete-Event-Simulation, Agent-Based-Simulation or System Dynamics. In recently published literature, various multilevel and large-scale hybrid simulation examples have been presented that combine different approaches in common environments.
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A Tripartite Hybrid Model Architecture for Investigating Health and Cost Impacts and Intervention Tradeoffs for Diabetic End-stage Renal Disease Amy Gao, Nathaniel D. Osgood, Wenyi An, Roland F. Dyck. Winter Simulation Conference, 2014.

Like most countries, Canada faces rising rates of diabetes and diabetic ESRD, which adversely affect cost, morbidity/mortality and quality of life. These trends raise great challenges for financial, human resource and facility planning and place a premium on understanding tradeoffs between different intervention strategies. We describe here our hybrid simulation model built to inform such efforts.
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Reflections on Two Approaches to Hybrid Simulation in Healthcare Joe Viana, University of Southampton. Winter Simulation Conference, 2014.

Hybrid simulation, the combination of simulation paradigms to address a problem is becoming more popular as the problems we are presented with become more complex. This is evidenced by an increase in the number of hybrid papers published in specific domains and the number of hybrid simulation frameworks being produced across domains.
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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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The GAP-DRG Model: Simulation of Outpatient Care for Comparison of Different Reimbursement Schemes Patrick Einzinger, Niki Popper, Felix Breitenecker et al., Proceedings of the 2013 Winter Simulation Conference

In healthcare the reimbursement of medical providers is an important topic and can influence the overall outcome. We present the agent-based healthcare model, which allows a comparison of reimbursement schemes in outpatient care. It models patients and medical providers as agents. In the simulation of healthcare system, patients develop medical problems (i.e., diseases) and a need for medical services. This leads to utilization of medical providers. The reimbursement system receives information on the patients’ visits via its generic interface, which facilitates an easy replacement. We describe the assumptions of the model in detail and show how it makes extensive use of available Austrian routine care data for its parameterization. The model design is optimized for utilizing as much of these data as possible. However, many assumptions have to be simplifications. Further work and detailed comparisons with healthcare data will provide insight on which assumptions are valid descriptions of the real process.
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