Abstract
Predicting what factors promote or protect populations from infectious disease is a fundamental epidemiological challenge. Social networks, where nodes represent hosts and edges represent direct or indirect contacts between them, are key to quantifying these aspects of infectious disease dynamics. However, understanding the complex relationships between network structure and epidemic parameters in predicting spread has been out of reach. Here we draw on advances in spectral graph theory and interpretable machine learning, to build predictive models of pathogen spread on a large collection of empirical networks from across the animal kingdom. Using a small set of network spectral properties, we were able to predict pathogen spread with remarkable accuracy for a wide range of transmissibility and recovery rates. We validate our findings using well studied host-pathogen systems and provide a flexible framework for animal health practitioners to assess the vulnerability of a particular network to pathogen spread.
Competing Interest Statement
The authors have declared no competing interest.
