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One pathogen does not an epidemic make: a review of interacting contagions, diseases, beliefs, and stories
Laurent Hébert-Dufresne1,2,3,4, Yong-Yeol Ahn5, Antoine Allard1,6
1Vermont Complex Systems Institute, University of Vermont, Burlington, VT USA.
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.
Insights
Contagion models across sciences often ignore interactions between spreading elements. This review explores how incorporating these interactions improves understanding and calls for a unified science of contagions.
Area of Science:
- * Utilizes interdisciplinary approaches in mathematical modeling and network theory.
- * Bridges natural sciences (epidemiology, genetics) and social sciences (sociology, economics).
Background:
- * Contagion models are widely applied across diverse scientific fields, from biology to computer science.
- * Current models often treat contagions in isolation, neglecting real-world interactions.
- * Interactions within and across hosts significantly impact contagion dynamics.
Purpose of the Study:
- * To review interaction mechanisms in social and biological contagions.
- * To survey existing models and frameworks for incorporating these interactions.
- * To identify challenges and future directions in contagion modeling.
Main Methods:
- * Comprehensive literature review of contagion models and interaction mechanisms.
- * Analysis of mathematical frameworks for modeling coupled spreading processes.
- * Synthesis of findings from natural and social science contagion studies.
Main Results:
- * Interactions between contagions are prevalent and significantly alter spreading dynamics.
- * Existing models often fail to capture these crucial interdependencies.
- * Challenges exist in inferring interactions and scaling complex models.
Conclusions:
- * A unified science of contagions is needed, integrating social and biological perspectives.
- * Future research should focus on developing scalable models that account for interactions.
- * Interdisciplinary collaboration is essential for advancing contagion science.
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