Obesity may be the single most challenging example for a condition with causes and consequences at multiple levels and with multiple feedback loops among influencing factors. New approaches to modeling obesity prevalence are needed to fully understand the complexities associated with the relationship between obesity and the demographic, socio-economic and environmental factors.
We describe in this paper a computer simulation project that focuses on the causes of obesity-related health disparities. In particular, our project adopts the susceptible, infected, and recovered (SIR) framework and the categorization of population into normal, overweight, obese, and extremely obese subpopulations. This project is important to public health because the fully developed computer application provides a new, more comprehensive, decision support tool for policy makers than most existing applications. The implementation of policies that effectively combat obesity would improve the health and well-being of a high percentage of the population, including both adults and children. It will also greatly reduce associated economic costs to society such as health care expenses and loss of productivity.
Being written in open source, our computer application is entirely cross-platform, lowering the transmission costs in research and education. Free access to the source code allows a broader community to incorporate additional advances in generating research questions for specific goals, thus facilitating collaboration across disciplines.
Open source Spatiotemporal model Obesity
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This work is partially supported by the National Science Foundation under Grant No. 1416509, project titled “Spatiotemporal Modeling of Human Dynamics Across Social Media and Social Networks”. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.
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