Comparing methods of targeting obesity interventions in populations: An agent-based simulation

Area of Science:

• Public Health
• Computational Epidemiology
• Social Network Analysis

Background:

• Social and neighborhood environments influence obesity-related behaviors like diet and physical activity.
• Social network interventions may amplify obesity prevention efforts but evidence is inconsistent.
• Agent-based models (ABM) offer a platform for simulating and evaluating intervention strategies.

Purpose of the Study:

• To compare conventional and network-based targeting methods for obesity interventions using an agent-based model.
• To develop and evaluate a novel network-based targeting strategy for maximizing population-level impact.
• To assess the potential of social multiplier effects in obesity intervention diffusion.

Main Methods:

• Development of an agent-based model (ABM) adapted from a validated obesity behavior diffusion model.
• Construction of realistic social networks among simulated agents.
• Calibration of the ABM against national-level data.
• Comparison of random, risk-based, area-based, and novel network-based targeting strategies.

Main Results:

• Network-based targeting strategies demonstrated potentially greater population-level impact than conventional methods.
• The newly developed network-based targeting method outperformed existing strategies in intervention effectiveness.
• Simulation results suggest social networks can facilitate wider diffusion of intervention effects.

Conclusions:

• Network-based targeting is a promising approach for enhancing the reach and effectiveness of obesity interventions.
• Agent-based modeling provides a valuable tool for optimizing intervention design and targeting in public health.
• Further research into novel network-based strategies could significantly advance obesity prevention efforts.