Real-time tracking of the effective reproduction number during the COVID-19 pandemic in Ohio: A discrete-time modelling framework for public health response

Background: The effectiveness of public health interventions during an epidemic relies on the timely assessment of transmission dynamics. The effective reproduction number, R _e ( t ), is a critical metric for this purpose, but its real-time estimation from surveillance data remains computationally challenging. Methods: We developed a discrete-time data assimilation framework based on the SIR model. This framework enables efficient, daily recalibration of the transmission rate ( β _i ) using reported case data, facilitating the real-time tracking of R _e ( t ). We applied this framework to the COVID-19 pandemic in Ohio, USA, from March 2020 to January 2022. Findings: Our model successfully reconstructed the complex multi-wave dynamics of the outbreak. The estimated R _{e ( t ) time series provides a quantitative assessment of the impact of key interventions and events, including mask mandates, lockdowns, relaxations, holiday gatherings, vaccine rollout, and the emergence of the Delta and Omicron variants. The analysis reveals how shifts in policy and pathogen transmissibility were directly reflected in transmission dynamics. Interpretation: This work demonstrates that a parsimonious, discrete-time model can serve as a powerful tool for real-time epidemic surveillance and policy evaluation, converting raw surveillance data into actionable insights for public health decision-makers.}