Modeling microbiome dynamics on social networks: how between-host transmission shapes within-host evolution

Abstract

A social network perspective on the human microbiome is crucial for understanding how the interplay between within-host microbial dynamics and between-host transmission influences microbial community stability, host metabolism, and population health and nutrition. Here, we provide a mathematical and numerical analysis of human-associated microbial communities in dyadic social ties and within a small network. By utilizing a generalized Lotka-Volterra model, we simulate within-host dynamics while incorporating inter-host microbial sharing driven by social interactions. Furthermore, the study accounts for individual variability in microbial behavior across different hosts. Our work identifies the conditions under which microbial sharing can have long-lasting effects, offering insights into how social networks shape the human microbiome.

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BibTeX RIS

BibTeX

@misc{shu2026modeling,
  title = {{Modeling microbiome dynamics on social networks: how between-host transmission shapes within-host evolution}},
  author = {Longmei Shu and Feng Fu},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.00320},
  eprint = {2609.00320},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.00320},
  note = {CC BY 4.0},
}

RIS

TY  - UNPB
TI  - Modeling microbiome dynamics on social networks: how between-host transmission shapes within-host evolution
AU  - Longmei Shu
AU  - Feng Fu
PY  - 2026
DA  - 2026/09/01
PB  - arXiv
DO  - 10.48550/arxiv.2609.00320
UR  - https://arxiv.org/abs/2609.00320
AB  - A social network perspective on the human microbiome is crucial for understanding how the interplay between within-host microbial dynamics and between-host transmission influences microbial community stability, host metabolism, and population health and nutrition. Here, we provide a mathematical and numerical analysis of human-associated microbial communities in dyadic social ties and within a small network. By utilizing a generalized Lotka-Volterra model, we simulate within-host dynamics while incorporating inter-host microbial sharing driven by social interactions. Furthermore, the study accounts for individual variability in microbial behavior across different hosts. Our work identifies the conditions under which microbial sharing can have long-lasting effects, offering insights into how social networks shape the human microbiome.
ER  -

Abstract

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