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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. Author summary Many software tools have been developed to support real-time outbreak response, allowing epidemiologists and other health professionals to characterise new and persistent pathogens and their threat to populations. Some of these tools allow the user to estimate how rapidly a pathogen is spreading through a population in real-time. This knowledge is crucial for a timely and effective response, helping to inform the optimal choice of control strategies. However, these tools are not perfect, and may need to be adapted to make them applicable to more pathogens and a variety of contexts. Our study summarises the different tools available in the literature and identifies the remaining issues that are yet to be addressed. We combine these findings with feedback from users of a popular tool, EpiEstim, to better understand the key priorities for development going forward.