Author summaryHow can we meaningfully summarise the transmission dynamics of an infectious disease? This question, although fundamental to epidemiology and crucial for informing the design and implementation of interventions (e.g., quarantines), is still not resolved. Current practice is to estimate the effective reproduction number R, which counts the average number of new infections generated per past infection, at large scales (e.g., nationally). An estimated R>1 signals epidemic growth. While R is easily interpreted and computed in real time, it averages infections across diverse locations or socio-demographic groups that likely possess different transmission dynamics. We prove that this averaging in R reduces sensitivity to resurgence, making R>1 slow to reflect realistic epidemic growth. This delay can substantially misinform policymakers and impede interventions. We apply optimal design theory to derive the risk averse reproduction number E as an alternative summary of diverse transmission dynamics. Using mathematical arguments, simulations and empirical COVID-19 datasets, we show that E>1 is an improved threshold for resurgence, providing timelier signals for informing policy or interventions and better uncertainty quantification. Further, E maintains the computability and interpretability of R. We propose E as meaningful statistic at large scales, where the averaging within R likely misrepresents the diversity of transmission dynamics. The effective reproduction number R is a prominent statistic for inferring the transmissibility of infectious diseases and effectiveness of interventions. R purportedly provides an easy-to-interpret threshold for deducing whether an epidemic will grow (R>1) or decline (R1generates timely resurgence signals (upweighting risky groups), while an EE as an alternative to R for informing policy and assessing transmissibility at large scales (e.g., state-wide or nationally), where R is commonly computed but well-mixed or homogeneity assumptions break down.
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