Session 8: Modeling Health Systems II

3:30 PM – 4:45 PM | Room 61 (Lower level)

Blood Alcohol Concentration Dynamics, Metabolism, and Decision Making

John D. Clapp
The Ohio State University

We use empirical findings from field and laboratory studies to build a mathematical model of the blood alcohol concentration dynamics in individuals that are in drinking events. We characterize these dynamics as the result of the interaction between a decision-making system and the metabolic process for alcohol. We show how this model allows us to analyze and predict previously observed behaviors, to design new approaches for the collection of data that improves the construction of the model, and to help with the design of interventions.

Participatory One Health Modeling

Raphaël Duboz and Aurélie Binot
CIRAD (French Agricultural Research Centre for International Development)
Bruce Wilcox
Mahidol University, Bangkok, Thailand
Panomsak Promburom
Chiang Mai University, Thailand
Carsten Richter

Participatory one health modeling (POHM) is a conceptual and methodological framework based on companion modeling for the management of integrated health issues, i.e. issues where societies, the environment, and health are strongly connected. Participatory modeling and associated tools and methodologies is the core of the approach. Several ongoing projects contribute to the development of the POHM. We present such activities and the participatory modeling tools we use to implement them.

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Assessing the Value of Complementarity in Addressing Transdisciplinary Problems

Patrick T. Hester
Old Dominion University

Most individuals are familiar with the well-known parable of the blind men and the elephant. For the unacquainted, it is a story of a group of blind men who each touch an (unknown to them) elephant to learn what it is. Each feels a different part of the elephant, such as its trunk, tusk, or ear, and then reports his findings back to the others. They then proceed to argue, with one stating “it is a rope” after touching its tail, while another states “no, it is a fan” after touching its ear, and so on. Each individual has a piece of the puzzle and yet none are entirely correct. Only in uniting the disparate perspectives can we begin to gain a complete understanding of this complex system.

Complementarity, the understanding that no single perspective or view of a system can provide complete knowledge of a system, is a necessary truth for understanding complex systems. It tells us that additional perspectives are unique and value-added and help by bringing additional insights in order to see a problem from a novel viewpoint. However, there is a large gap between understanding the value of complementarity and the successful implementation of such an approach. Individuals bring potentially incompatible worldviews to a problem, they may have varied levels of expertise about a problem, and they may not speak the same language (either literally or figuratively). In order to address these concerns, we can utilize cognitive mapping techniques to serve as a universal language among individuals in the assessment of a complex system. A case study is presented exploring the Ebola virus disease from an engineering, education, and global health perspective. Future areas for research are also addressed.