AI Insight
EpiLink is a new computational method that identifies recently linked infectious disease cases from genomic data without requiring fixed genetic distance thresholds or labeled training data. The approach uses simulations to calculate a compatibility score between case pairs based on their genetic differences and sampling times, accounting for uncertainties in infection timing and mutation rates. When tested on synthetic data and real SARS-CoV-2 sequences from Boston's 2020 outbreak, EpiLink achieved clustering accuracy comparable to supervised machine learning models and successfully identified documented outbreak clusters from conferences and nursing facilities.
Why it matters
This tool addresses a critical gap in outbreak surveillance by providing an interpretable method to identify transmission clusters when labeled contact tracing data is unavailable or genetic thresholds are unreliable. EpiLink could be particularly valuable during rapidly evolving outbreaks and superspreading events where traditional fixed-threshold approaches struggle to distinguish true transmission links from coincidentally similar infections.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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.