Capturing complex interactions in disease ecology with simplicial sets

Network approaches have revolutionized the study of ecological interactions. Social, movement and ecological networks have all been integral to studying infectious disease ecology. However, conventional (dyadic) network approaches are limited in their ability to capture higher-order interactions. We present simplicial sets as a tool that addresses this limitation. First, we explain what simplicial sets are. Second, we explain why their use would be beneficial in different subject areas. Third, we detail where these areas are: social, transmission, movement/spatial and ecological networks and when using them would help most in each context. To demonstrate their application, we develop a novel approach to identify how pathogens persist within a host population. Fourth, we provide an overview of how to use simplicial sets, highlighting specific metrics, generative models and software. Finally, we synthesize key research questions simplicial sets will help us answer and draw attention to methodological developments that will facilitate this.

Automated summary

Network approaches have revolutionized the study of ecological interactions, but conventional methods have limitations in capturing higher-order interactions. Simplicial sets provide a solution, allowing for a more comprehensive analysis of social, transmission, movement/spatial, and ecological networks. This article presents the concept of simplicial sets, their benefits in various subject areas, and a novel approach to studying pathogen persistence. Specific metrics, generative models, and software are also highlighted.