Obesity Prevention

Multi-model approach to analyzing and comparing the effects of possible policy interventions

Complex adaptive systems-of-systems are inherently multi-scale across several dimensions, including temporal, geographical, and organizational. We present a multimodel paradigm integrating a localized community-scale individual-based model (IBM) with a population scale system dynamics (SD) model to analyze long term results of potential policy interventions for obesity prevention.

The IBM uses a social network to simulate the spread of opinions relating to nutrition and physical activity (N&PA) behaviors such as dieting and exercise, and the effects of these opinions on individual actions.

The network structure uses a mixture of scale-free and uniformly random connections to represent a social network of relationships and interactions within a local community.

The N&PA related health behaviors of individuals change dynamically relative to endogenous influences within their social network and exogenous influences from industry-based advertising and public health-related counter-marketing and educational campaigns.

The outputs of the IBM, seen as changes in obesogenic behaviors, can be used as inputs to a SD model to calculate the resulting changes in mortality and morbidity over the ensuing decades

Symposia Paper

A Multi-scale Paradigm to Design Policy Options for Obesity Prevention: Exploring the Integration of Individual- Based Modeling and System Dynamics, 29th International Conference of the System Dynamics Society, July 2011, Washington, DC [PDF]

Presentation

A Multi-scale Paradigm to Design Policy Options for Obesity Prevention: Exploring the Integration of Individual-Based Modeling and System Dynamics, 29th International Conference of the System Dynamics Society, July 2011, Washington, DC [PDF]

Related Article

Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants, The Lancet, Volume 387, No. 10026, p1377-1396, 2 April 2016