AUTHOR=Kim Byul Nim , Jo Junwoo , Oh Chunyoung , Moon Sanghyeok , Abdulali Arsen , Lee Sunmi 

TITLE=A novel approach to estimating Rt through infection networks: understanding regional transmission dynamics of COVID-19

JOURNAL=Frontiers in Public Health

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1586786

DOI=10.3389/fpubh.2025.1586786

ISSN=2296-2565

ABSTRACT=IntroductionThe effective reproduction number (Rt) is a key indicator for monitoring and controlling infectious diseases such as COVID-19, where transmission patterns can differ substantially across demographics, regions, and phases of the pandemic. In this study, we propose a novel, network-based approach to empirically estimate Rt using detailed transmission data from South Korea. By reconstructing infector–infectee pairs, our method incorporates local factors like mobility and social distancing, offering a more precise perspective than traditional methods.MethodsWe acquired infector–infectee pair data from the Korea Disease Control and Prevention Agency (KDCA) for 2020–2021 and built infection networks to derive empirical Rt. This framework allows us to examine regional differences and the effects of social distancing measures. We also compared our results with Cori's Rt, which employs incidence data and serial interval distributions, to highlight the advantages of an infection network-based strategy.ResultsOur empirical Rt uncovered three distinct patterns. Early in the outbreak, when case numbers were low, Rt remained near 1, indicating limited transmission. During superspreading events, our estimates showed sharper peaks than Cori's method, demonstrating higher sensitivity to sudden changes. As the Delta variant emerged, our Rt values converged with Cori's, underscoring the utility of network-based methods for capturing nuanced shifts during high-variability phases.DiscussionIncorporating infection networks into Rt estimation thus provides decision-makers with timely insights for targeted interventions. Empirically reconstructing infection networks and directly estimating Rt reveal real-time transmission dynamics often overlooked by aggregated approaches. This method can significantly improve outbreak forecasts, inform more precise public health policies, and strengthen pandemic preparedness.