Mathematical Modeling of Obesity: Integrating Scientific Evidence for Practical Use

Obesity prevalence has been increasing continuously around the world. Because obesity causes serious chronic diseases such as type 2 diabetes, cardiovascular disease, and some kinds of cancer, it is a major public health issue. Obesogenic factors are manifold and complexly intertwined, including low physical activity, excess energy intake, and genetics. Scientists are investigating mechanisms of developing obesity from different perspectives, from the molecular level to the population level. Mathematical and computational approaches are seeing broader use to integrate new findings and to interpret the impact of these factors on individuals and populations. In this session, speakers will discuss applications of mathematics in integrating scientific evidence for practical use. These include modeling the obesity epidemic from the social contagion perspective to compare the different types of obesity intervention, the introduction of a dynamic energy balance model to predict long- and short-term weight change, and modeling energy intake changes. Speakers will discuss how mathematics can be used to integrate scientific evidence and help solve important questions in obesity research and challenges faced as examples for other research fields, including mathematical modeling.