Abstract The effective reproduction number, R , is a predominant statistic for tracking infectious disease spread and informing health policies. An estimated R = 1 is universally interpreted as a stability threshold distinguishing epidemic growth ( R > 1 ) from control ( R < 1 ). We demonstrate that this interpretation frequently fails because R typically averages over groups with heterogeneous characteristics. We find that R = 1 conceals valuable early-warning signals of resurgence and misclassifies complex dynamics as noise, generating false positive stability thresholds that diminish predictive and policymaking value. We further illustrate that a popular alternative transmissibility definition (using next-generation matrices) overcorrects this issue, producing false negative stability signals by amplifying stochastic variation. We address these limitations by adapting a recently developed statistic, E , derived from R using experimental design theory. We show that E tightly constrains the set of scenarios consistent with stability, while remaining robust to noise and establish E = 1 as a more practical and meaningful real-time threshold.
Parag et al. (2026) studied this question.