Are epidemic growth rates more informative than reproduction numbers?

Abstract

Summary statistics, often derived from simplified models of epidemic spread, inform public health policy in real time. The instantaneous reproduction number, Rt , is predominant among these statistics, measuring the average ability of an infection to multiply. However, Rt encodes no temporal information and is sensitive to modelling assumptions. Consequently, some have proposed the epidemic growth rate, rt , i.e., the rate of change of the log-transformed case incidence, as a more temporally meaningful and model-agnostic policy guide. We examine this assertion, identifying if and when estimates of rt are more informative than those of Rt . We assess their relative strengths both for learning about pathogen transmission mechanisms and for guiding epidemic interventions in real time.

Fundamental limits on inferring epidemic resurgence in real time using effective reproduction numbers

“…Approaches aimed at improving case-based inference generally correct for reporting biases or propose more robust measures of transmissibility, such as time-varying growth rates [ 14 , 41 , 56 ]. However, as our study highlights limits that persist at the gold standard of perfect case reporting and, further it is known that under such conditions growth rates and R are equally informative [ 57 ], these lines of investigation are unlikely to minimise the detection limits that we have exposed.…”

Generation time of the alpha and delta SARS-CoV-2 variants: an epidemiological analysis

“… 8 Strength reflects the variant's transmissibility 8 and is typically measured by the time-dependent reproduction number (ie, the number of people that each infected person is expected to infect). The generation time (ie, the time between infection events in infector–infectee pairs) determines the relationship between a variant's speed and its strength 8 , 9 , 10 , 11 and is an input used in models to estimate the reproduction number from case notification data. 12 , 13 In principle, an increased growth rate of COVID-19 cases, as observed for the delta variant, is attributable to increased transmissibility, a shorter generation time, or both of these factors.…”

Inference of the SARS-CoV-2 generation time using UK household data

“…Inferring the generation time distribution of SARS-CoV-2 is important in order to predict the effects of non-pharmaceutical interventions such as contact tracing and quarantine ( Ashcroft et al, 2021 ; Ferretti et al, 2020b ; Hart et al, 2021 ). In addition, the generation time distribution is widely used in epidemiological models for estimating the time-dependent reproduction number from case notification data ( Abbott et al, 2020 ; Fraser, 2007 ; Gostic et al, 2020 ; Thompson et al, 2020 ) and is crucial for understanding the relationship between the reproduction number and the epidemic growth rate ( Fraser, 2007 ; Parag et al, 2021 ; Park et al, 2020a ; Wallinga and Lipsitch, 2007 ).…”