The spectral underpinnings of pathogen spread on animal networks

Nicholas M. Fountain-Jones, Mathew Silk, Raima Carol Appaw, Rodrigo Hamede, Julie Rushmore, Kimberly Vanderwaal, Meggan E. Craft, Scott Carver, Michael Charleston

Research output: Contribution to journalArticlepeer-review

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 important in quantifying these aspects of infectious disease dynamics. However, how network structure and epidemic parameters interact in empirical networks to promote or protect animal populations from infectious disease remains a challenge. Here we draw on advances in spectral graph theory and machine learning to build predictive models of pathogen spread on a large collection of empirical networks from across the animal kingdom. We show that the spectral features of an animal network are powerful predictors of pathogen spread for a variety of hosts and pathogens and can be a valuable proxy for the vulnerability of animal networks to pathogen spread. We validate our findings using interpretable machine learning techniques and provide a flexible web application for animal health practitioners to assess the vulnerability of a particular network to pathogen spread.

Original languageEnglish (US)
Article number20230951
JournalProceedings of the Royal Society B: Biological Sciences
Volume290
Issue number2007
DOIs
StatePublished - Sep 20 2023

Bibliographical note

Publisher Copyright:
© 2023 The Authors.

Keywords

  • disease simulation models
  • graph theory
  • machine learning
  • wildlife

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't

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