One pathogen does not an epidemic make: A review of interacting contagions, diseases, beliefs, and stories
Laurent Hébert-Dufresne, Yong-Yeol Ahn, Antoine Allard, Vittoria Colizza, Jessica W. Crothers, Peter Sheridan Dodds, Mirta Galesic, Fakhteh Ghanbarnejad, Dominique Gravel, Ross A. Hammond, Kristina Lerman, Juniper Lovato, John J. Openshaw, S. Redner, Samuel V. Scarpino, Guillaume St-Onge, Timothy R. Tangherlini, Jean-Gabriel Young
TL;DR
The paper argues that contagions—biological pathogens, social memes, beliefs, and stories—interact across hosts, environments, and cognitive systems, invalidating the siloed approach of studying them in isolation. It synthesizes insights from the physics of interacting contagions, social contagion data, belief dynamics, and ecological perspectives to show how synergy, competition, and multi-scale coupling shape epidemic and information-diffusion outcomes. It highlights key mechanisms such as synergistic transmission, multilayer and higher-order interactions, and narrative networks, and discusses challenges in inferring interactions from partial data while stressing the need for unified, interdisciplinary frameworks. By proposing an ecosystem view of contagions and calling for data-integrative, theory-informed approaches, the work aims to improve understanding and forecasting of complex, real-world spreading processes and to bridge social and biological perspectives.
Abstract
From pathogens and computer viruses to genes and memes, contagion models have found widespread utility across the natural and social sciences. Despite their success and breadth of adoption, the approach and structure of these models remain surprisingly siloed by field. Given the siloed nature of their development and widespread use, one persistent assumption is that a given contagion can be studied in isolation, independently from what else might be spreading in the population. In reality, countless contagions of biological and social nature interact within hosts (interacting with existing beliefs, or the immune system) and across hosts (interacting in the environment, or affecting transmission mechanisms). Additionally, from a modeling perspective, we know that relaxing these assumptions has profound effects on the physics and translational implications of the models. Here, we review mechanisms for interactions in social and biological contagions, as well as the models and frameworks developed to include these interactions in the study of the contagions. We highlight existing problems related to the inference of interactions and to the scalability of mathematical models and identify promising avenues of future inquiries. In doing so, we highlight the need for interdisciplinary efforts under a unified science of contagions and for removing a common dichotomy between social and biological contagions.