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

Rahmatollah Beheshti, Mehdi Jalalpour, Thomas A. Glass

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Social networks as well as neighborhood environments have been shown to effect obesity-related behaviors including energy intake and physical activity. Accordingly, harnessing social networks to improve targeting of obesity interventions may be promising to the extent this leads to social multiplier effects and wider diffusion of intervention impact on populations. However, the literature evaluating network-based interventions has been inconsistent. Computational methods like agent-based models (ABM) provide researchers with tools to experiment in a simulated environment. We develop an ABM to compare conventional targeting methods (random selection, based on individual obesity risk, and vulnerable areas) with network-based targeting methods. We adapt a previously published and validated model of network diffusion of obesity-related behavior. We then build social networks among agents using a more realistic approach. We calibrate our model first against national-level data. Our results show that network-based targeting may lead to greater population impact. We also present a new targeting method that outperforms other methods in terms of intervention effectiveness at the population level.

Original languageEnglish (US)
Pages (from-to)211-218
Number of pages8
JournalSSM - Population Health
StatePublished - Dec 1 2017
Externally publishedYes


  • Agent-based modeling
  • Effectiveness
  • Influence maximization
  • Intervention targeting
  • Obesity
  • Simulation
  • Social networks

ASJC Scopus subject areas

  • Health(social science)
  • Health Policy
  • Public Health, Environmental and Occupational Health


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