Work

Enhancing time-varying reproduction number estimates for COVID-19 with behavior and surveillance data in South Korea, 2020–2022

Year: 2026

Type: article

Abstract: BACKGROUND: Accurate estimation of the time-varying effective reproduction number, R(t), is essential for interpreting transmission dynamics and informing public health actions. Incidence-based approaches can be biased when behavioral change and surveillance performance alter realized infectiousness and the timing of observed cases. METHODS: We developed a behavior- and surveillance-informed framework tailored to the Korean context (Feb 2020–Jan 2022). National epidemiological data (20,155 linked infector–infectee pairs after quality control) and Google mobility indicators were used to construct setting-specific behaviors—residential mobility as a proxy for household contact duration and a composite non-residential signal for non-household activity. Infection-to-diagnosis delays were incorporated via a surveillance-adjusted generation-interval kernel that links recent incidence to current infectiousness. A context-specific transmission measure was mapped to R(t) and connected to daily cases using a count model that accounts for reporting variability, with full technical details described elsewhere. RESULTS: The estimated R(t) captured phase-specific swings in transmissibility and responded to shifts in mobility and detection timing. Household transmission provided a relatively stable baseline, whereas non-household activity drove episodic surges. Surveillance adjustment shortened effective generation times during periods of faster detection and improved calibration of R(t) relative to naïve incidence-based estimates. Forecast evaluation demonstrated consistent short-term skill with appropriate empirical coverage. CONCLUSIONS: Combining routinely available mobility and surveillance summaries improves the interpretability and responsiveness of R(t) estimation in densely connected settings. The workflow is transparent and reproducible, supporting near-term assessment and communication of transmission risk, and can be adapted to other surveillance systems where behavioral and diagnostic conditions evolve over time.

Source: Archives of Public Health

DOI: 10.1186/s13690-026-01858-7

Authors: Byul Nim Kim, Suhyeon Kim, Haram Seo, Gerardo Chowell, Sunmi Lee

Institutions: Kyung Hee University, Georgia State University

Language: English

Topic: COVID-19 epidemiological studies

Subfield: Modeling and Simulation

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,990