Robust uncertainty quantification in popular estimators of the instantaneous reproduction number.

ABSTRACT: The instantaneous reproduction number (${R}_t$) is a key measure of the rate of spread of an infectious disease. Correctly quantifying uncertainty in ${R}_t$ estimates is crucial for making well-informed decisions. Popular ${R}_t$ estimators leverage smoothing techniques to distinguish signal from noise. Examples include EpiEstim and EpiFilter, which are both controlled by a "smoothing parameter" that is traditionally selected by users. We demonstrate that the values of these smoothing parameters are unknown, vary markedly with epidemic dynamics, and show that data-driven smoothing is crucial for accurate uncertainty quantification of real-time ${R}_t$ estimates. We derive novel model likelihoods for the smoothing parameters in both EpiEstim and EpiFilter and develop a Bayesian framework t

SUBMITTER: Steyn N 

PROVIDER: S-EPMC12634121 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature