Real-time estimation of the epidemic reproduction number: Scoping review of the applications and challenges

Abstract

The time-varying reproduction number (Rt) is an important measure of transmissibility during outbreaks. Estimating whether and how rapidly an outbreak is growing (Rt > 1) or declining (Rt < 1) can inform the design, monitoring and adjustment of control measures in real-time. We use a popular R package for Rt estimation, EpiEstim, as a case study to evaluate the contexts in which Rt estimation methods have been used and identify unmet needs which would enable broader applicability of these methods in real-time. A scoping review, complemented by a small EpiEstim user survey, highlight issues with the current approaches, including the quality of input incidence data, the inability to account for geographical factors, and other methodological issues. We summarise the methods and software developed to tackle the problems identified, but conclude that significant gaps remain which should be addressed to enable easier, more robust and applicable estimation of Rt during epidemics.

EpiLPS: A fast and flexible Bayesian tool for estimation of the time-varying reproduction number

“…EpiEstim is a powerful tool to estimate in real-time and is probably the best tool currently available to deliver timely estimates of the reproduction number [ 41 ]. EpiLPS can be considered a real-time approach only to a certain degree, where the real-time concept is partially present but fundamentally different from the one proposed in EpiEstim.…”

“…EpiEstim is a powerful tool to estimate R t in real-time and is probably the best tool currently available to deliver timely estimates of the reproduction number [41]. EpiLPS can be Table 4.…”

Estimating the epidemic reproduction number from temporally aggregated incidence data: a statistical modelling approach and software tool

“…delay distributions and ascertainment rates), and are much more computationally intensive, which can be a barrier to the adoption of such methods by users. 14 Here, Rt estimates are based on a single daily incidence reconstruction, meaning Rt can be estimated very rapidly from aggregated data, which is particularly desirable during real-time outbreak analysis. 14 A potential downside is that uncertainty in Rt estimates could be underestimated.…”

“…14 Here, Rt estimates are based on a single daily incidence reconstruction, meaning Rt can be estimated very rapidly from aggregated data, which is particularly desirable during real-time outbreak analysis. 14 A potential downside is that uncertainty in Rt estimates could be underestimated. However, the simulation study showed that the 95% credible interval of estimates encompassed the correct value of Rt the majority of the time, and we found no substantial indication that this approach detrimentally affected our characterisation of the uncertainty.…”

“…12,13 Therefore, enabling estimation of Rt from temporally aggregated data is critical to ensure methods such as EpiEstim are widely applicable. 14 In this study, we combine an expectation-maximisation (EM) algorithm with the renewal equation approach implemented in EpiEstim to reconstruct daily incidence from aggregated data and estimate Rt. We assess the performance of the method using influenza and COVID-19 data, in addition to an extensive simulation study.…”