Statistical methods for the estimation of contagion effects in human disease and health networks

Abstract

Contagion effects, sometimes referred to as spillover or influence effects, have long been central to the study of human disease and health networks. Accurate estimation and identification of contagion effects are important in terms of understanding the spread of human disease and health behavior, and they also have various implications for designing effective public health interventions. However, many challenges remain in estimating contagion effects and it is often unclear when it is difficult to correctly estimate contagion effects, or why a particular method would need to be applied. In this review I explain the challenges in estimating contagion effects, and how they can be framed as an omitted variable bias problem. I then discuss how such challenges have been addressed in randomized experiments and traditional statistical analyses, as well as several state-of-the-art statistical methods. Finally, I conclude by summarizing recent advancements and noting remaining challenges, as well as appropriate next steps.

Keywords

• Contagion effects
• Networks
• Omitted variable bias
• Stochastic actor-oriented models
• Instrumental variables
• Latent-space models