Advances in Contact Tracing: A Bayesian Framework to Improve Network and Transmission Models

Non‑pharmaceutical interventions such as contact tracing, quarantine, and targeted restrictions remain central to outbreak control. Yet, their success depends on understanding how pathogens spread through heterogeneous populations. Here, I highlight an innovative network‑informed Bayesian framework that integrates genomic and contact data across time to reconstruct transmission pathways more accurately (Xu et al. 2026). By modeling network structure as a prior, the approach captures individual‑level heterogeneity and resolves ambiguities that arise when genetic data are sparse . Notably, this framework captures genomic variation using mutation loci rather than complete genomes preserves evolutionary signal while greatly reducing computational demands, and also enables more efficient integration of key metadata. Together, these developments provide a critical step toward designing interventions that more precisely disrupt transmission while minimizing social and economic costs.