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Inferring Sexual Network Bridging Using Genomics: A Simulation Study.

Madeleine C Kline1,2, David Helekal1,2, Kirstin I Oliveira Roster1,2

  • 1Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health.

Medrxiv : the Preprint Server for Health Sciences
|June 5, 2026
PubMed
Summary
This summary is machine-generated.

Pathogen genomics can infer sexual network bridging in sexually transmitted infections. However, unadjusted genomic data often overestimates bridging, especially with sparse sampling, impacting surveillance accuracy.

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Area of Science:

  • Epidemiology
  • Genomic epidemiology
  • Public health

Background:

  • Sexually transmitted infections (STIs) spread through interconnected networks, including men who have sex with men (MSM) and heterosexual populations.
  • Quantifying the bridging between these networks is crucial for effective STI surveillance and intervention strategies.

Purpose of the Study:

  • To evaluate the accuracy of pathogen genomics tools, specifically phylodynamic bridging metrics, in inferring sexual network bridging.
  • To assess the impact of different sampling schemes on the reliability of genomic bridging estimates.

Main Methods:

  • Simulations of gonorrhea transmission networks were used to generate data.
  • Ancestral state reconstruction was applied to infer phylodynamic bridging metrics.
  • Various sampling schemes, from comprehensive to sparse, were evaluated.

Main Results:

  • Phylodynamic bridging metrics successfully differentiated sexual network structures, even under biased sampling.
  • Accuracy of bridging estimates was highly dependent on sampling scheme and density.
  • Estimates from comprehensive sampling were only slightly overestimated (6.9%), while sparse sampling (5% of symptomatic MSM) led to overestimations exceeding 1000%.

Conclusions:

  • Pathogen genomics can infer sexual network bridging, but unadjusted inferences often lead to significant overestimation.
  • Sampling density and scheme critically influence the accuracy of genomic bridging estimates.
  • Contextual interpretation of genomic surveillance data is essential to avoid misinterpreting bridging dynamics.