Pseudo-time reconstruction for analyzing transmission direction in COVID-19 contact-tracing data

SARS-CoV-2, the virus responsible for COVID-19, emerged in late 2019 and rapidly spread worldwide. Inferring transmission direction between epidemiologically linked cases is an important component of outbreak investigation, yet symptom-onset-based heuristics can become unreliable when onset differences are small due to incubation variability, reporting noise, and asymptomatic infections. Using a detailed contact-tracing dataset, we develop Pseudo-Time Reconstruction for Epidemic (PTRE), a network-informed analytical framework that integrates symptom timing, individual-level information, and contact network structure to induce a relative ordering of cases. We apply this framework to regional COVID-19 contact-tracing data and evaluate directional discrimination across onset-gap regimes. We further validate PTRE through simulation studies with known ground-truth transmission directions on diverse network topologies. Our results show a regime-dependent pattern: PTRE aligns with onset-based ordering when temporal separation is large, while structural connectivity provides a complementary directional signal when onset differences are minimal. These findings highlight both the potential and the limitations of integrating network information to enhance directional discrimination under temporal ambiguity.