Endogenous competition and the under-realized diffusion in social networks

• In Event: 40416 - New approaches to social network theory and methods

Abstract

A central theme in social network analysis is diffusion—the spread of diseases, information, and behaviors through social ties. Originally introduced by demographers and widely applied by epidemiologists, the basic reproduction number (𝑹0) and its derivations serve as foundational metrics for diffusion processes. Using infectious disease diffusion as an example, this paper describes a mechanism overlooked in most conventional analyses, in which a disease can endogenously “compete” with itself when multiple infectious individuals race to infect the same susceptible individual, thereby reducing the effective reproductive rate. Utilizing an empirically-calibrated network epidemiological model of wild-type COVID-19 diffusion in its early pandemic, we show that the mechanism would be expected to reduce its reproductive rate by an average of 39%. Simulation experiments further identify several different types of endogenous competition mechanisms and their relative effect sizes. We highlight the incorporation of endogenous competition mechanism as a necessary step in realistically modeling diffusion processes.

Authors

• Peng Huang, University of Georgia

• Zack W. Almquist, University of Washington

• Carter T. Butts, University of California-Irvine