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Capturing complex interactions in disease ecology with simplicial sets.

Matthew J Silk1,2, Mark Q Wilber3, Nina H Fefferman1,4,5

  • 1NIMBioS, University of Tennessee, Knoxville, Tennessee, USA.

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Summary
This summary is machine-generated.

Simplicial sets offer a novel way to study complex ecological interactions beyond simple pairs. This approach enhances understanding of disease spread and pathogen persistence in populations.

Keywords:
dose-responseecological networkepidemiological modelhigher-order interactionhypergraphmovement networksimplicial complexsocial network

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Area of Science:

  • Ecology
  • Epidemiology
  • Network Science

Background:

  • Network approaches are crucial for studying ecological interactions and infectious disease ecology.
  • Conventional dyadic network methods struggle to capture higher-order interactions.
  • Higher-order interactions are essential for understanding complex ecological and disease dynamics.

Purpose of the Study:

  • Introduce simplicial sets as a powerful tool for analyzing higher-order interactions in ecological and disease systems.
  • Demonstrate the benefits and applications of simplicial sets across various scientific domains.
  • Develop a novel method using simplicial sets to investigate pathogen persistence within host populations.

Main Methods:

  • Explain the mathematical framework of simplicial sets.
  • Illustrate the application of simplicial sets in social, transmission, movement/spatial, and ecological networks.
  • Present a new methodology for pathogen persistence analysis utilizing simplicial sets.

Main Results:

  • Simplicial sets provide a robust framework for modeling complex, multi-level interactions.
  • The novel approach effectively identifies mechanisms of pathogen persistence.
  • The study outlines specific metrics, generative models, and software for implementing simplicial sets.

Conclusions:

  • Simplicial sets represent a significant advancement for network analysis in ecology and epidemiology.
  • This methodology will enable answering key research questions regarding complex systems.
  • Further methodological developments will broaden the application of simplicial sets in scientific research.