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

Introduction: The effective reproduction number ( ) 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 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.

Methods: We acquired infector-infectee pair data from the Korea Disease Control and Prevention Agency (KDCA) for 2020-2021 and built infection networks to derive empirical . This framework allows us to examine regional differences and the effects of social distancing measures. We also compared our results with Cori's , which employs incidence data and serial interval distributions, to highlight the advantages of an infection network-based strategy.

Results: Our empirical uncovered three distinct patterns. Early in the outbreak, when case numbers were low, 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 values converged with Cori's, underscoring the utility of network-based methods for capturing nuanced shifts during high-variability phases.

Discussion: Incorporating infection networks into estimation thus provides decision-makers with timely insights for targeted interventions. Empirically reconstructing infection networks and directly estimating 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.