Identifying recently linked infections from pathogen genome sequences is central to infectious disease surveillance, yet many clustering approaches rely on fixed genetic distance thresholds whose relationship to transmission is often unclear. This limitation is especially important in rapidly growing outbreaks and superspreading events, where many cases may be sampled close together in time and share little genetic variation, making true transmission links difficult to distinguish from other closely related infections. Supervised models can improve discrimination, but they require labelled transmission data that are rarely available during outbreak response.
We developed EpiLink, a threshold-free method that estimates whether two cases are compatible with recent transmission. Here, compatibility means how well the observed genetic distance and sampling-time difference between two cases fit what would be expected if they were linked by defined recent transmission scenarios. EpiLink simulates plausible recent transmission histories while accounting for uncertainty in infection timing, testing delay, and mutation accumulation, then assigns higher scores to pairs whose observed differences are typical of those simulations.
EpiLink was evaluated using both synthetic and empirical SARS-CoV-2 outbreak data from the 2020 Boston epidemic. Two EpiLink variants were compared to a logistic regression model trained on labelled transmission data. One EpiLink variant assumed deterministic mutation accumulation, with genetic differences proportional to elapsed evolutionary time; the other accounted for stochasticity by sampling mutation counts from a Poisson distribution. The logistic regression model performed better at distinguishing linked from unlinked pairs, but EpiLink achieved comparable clustering accuracy. In the Boston data, EpiLink recovered clusters enriched for documented conference and skilled nursing facility outbreaks. EpiLink thus provides an interpretable, simulation-based approach for identifying recent transmission clusters when fixed thresholds are difficult to justify and labelled transmission data are unavailable.
Author summary
Grouping infectious disease cases into transmission clusters is a routine part of outbreak surveillance, but many methods rely on fixed genetic distance cut-offs that can be hard to interpret, especially when transmission is rapid and pathogen diversity is low. We developed EpiLink, which instead asks how consistent the observed genetic and sampling-time differences between two cases are with recent transmission. EpiLink simulates plausible transmission histories and scores each pair according to how typical its observed differences are within the simulated distributions. In simulated SARS-CoV-2 outbreaks, EpiLink nearly matched the clustering accuracy of a supervised model trained on labelled transmission pairs, without requiring labelled data. We found a practical trade-off: deterministic configurations performed best when model assumptions were well met, while configurations incorporating uncertainty were more robust when assumptions were misspecified. Applied to SARS-CoV-2 data from the 2020 Boston epidemic, EpiLink recovered clusters enriched for known outbreaks at a conference and skilled nursing facility. EpiLink offers a practical and interpretable approach for transmission clustering when labelled data are unavailable.