Computational Modeling Approaches Linking Health and Social Sciences: Sensitivity of Social Determinants on the Patterns of Health Risk Behaviors and Diseases

Anuj Mubayi

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Developing health promotion programs that support healthy lifestyle behaviors require comprehensive understanding of mechanisms that drive such complex social systems. Policy makers can use models and theories to guide this process at the individuals, groups, and communities levels. Individuals can have multiple risky health behaviors including physical inactivity, unhealthy diets, smoking, and alcohol drinking that are often shaped by social and ecological factors. Collective understanding of these factors can provide ability to design and evaluate intervention programs that can change unhealthy or risky behaviors over long period of time. However, it is overwhelming task to optimize intervention based on only empirical and/or cross-sectional studies. Effective long lasting intervention needs a thorough understanding of the role of social and environmental mechanisms at multiple scales on the dynamics of health behaviors. Recent mathematical and computational methods developed in other fields, such as epidemiology and finance, can provide systematic and in-depth understanding of mechanisms. However, the use of such methods in social and behaviors sciences have been limited. In this chapter, some real life working examples of social health behaviors problems are provided which uses some cutting edge methods from dynamical systems and data mining to uncertainty quantification.

Original languageEnglish (US)
JournalHandbook of Statistics
DOIs
StateAccepted/In press - 2017

Keywords

  • Data mining
  • Dynamic models
  • Ecological models
  • Health risk behaviors
  • Sensitivity and uncertainty analysis
  • Social influences

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics

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