A novel approach to estimating Rt through infection networks

Understanding regional transmission dynamics of Covid-19

Bibliographic Data

ID22079929
AuthorsByul Nim Kim (Kyung Hee University), Jeongwon Jo (0000-0003-1318-3937, Kyung Hee University), Junwoo Jo, Chunyoung Oh (0009-0001-7505-4754, Chonnam National University), Sanghyeok Moon, S I Moon (Kyung Hee University), Arsen Abdulali (0000-0001-6874-7782, University of Cambridge), Sunmi Lee (0000-0003-1126-0660, Kyung Hee University, corresponding author)
Year2025
Volume13
Pages1586786-1586786
Publication date2025-06-18
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2025.1586786
PMID40606074
OpenAlexW4411394819
LanguageEN
References cited42

Introduction The effective reproduction number ( R t ) 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 R t 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 R t . This framework allows us to examine regional differences and the effects of social distancing measures. We also compared our results with Cori's R t , which employs incidence data and serial interval distributions, to highlight the advantages of an infection network-based strategy. Results Our empirical R t uncovered three distinct patterns. Early in the outbreak, when case numbers were low, R t 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 R t 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 R t estimation thus provides decision-makers with timely insights for targeted interventions. Empirically reconstructing infection networks and directly estimating R t 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

2019-20 coronavirus outbreak · Biology · Computational biology · Disease · Outbreak · Pathology · Physics · Telecommunications · Computer Science · COVID-19 epidemiological studies · Influenza Virus Research Studies · Medicine · SARS-CoV-2 and COVID-19 Research · Virology

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