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.

Estimating Social Influence Effects in Networks Using A Latent Space Adjusted Approach in R

“…Similarities of behavior, state, and characteristics of two individuals in a network relationship can be caused by three primary mechanisms: influence, homophilous selection, or common socialenvironmental factors (Vanderweele and An, 2013). While it is possible to rule out some mechanisms through random treatment assignment or networks in experiments, entanglement among these different mechanisms makes it difficult to correctly estimate social influence effects from observational data (Xu, 2020). The challenges in estimation caused by entanglement among social influence effects and common social-environmental factors can be easily framed as an omitted variable bias problem (e.g., ignoring the group or environment individuals belong to when estimating the social influence model).…”

Measuring the impact of Wuhan’s COVID-19 lockdown on the growth enterprise market in China

“…China is reported to be the only major economy in the world to achieve positive growth in 2020, with a growth rate of 2.3 percent. 1 It is no denying that contagion effects have always been the focus of human disease and health network research [1][2] empirically analyzed three types networks of disease transmission, namely, temporary networks, static networks, and a fully connected topology. Results showed that time structure can affect disease transmission more than static network structure.…”