When it came to COVID-19 in Europe, early 2021 echoed early 2020. Each year started in the shadow of a new infectious disease threat, with SARS-CoV-2 spreading widely in 2020, followed by the Alpha and Beta variants in 2021. These viruses initially moved undetected between countries, before causing large local outbreaks and triggering the implementation of strict control measures. However, there were also some crucial differences in 2021: researchers had a better understanding of the infection, effective vaccines were starting to roll-out, and health agencies had more real-time data streams available. In terms of tracking and responding to epidemics, the COVID-19 pandemic has highlighted some promising developments in data integration and analysis, but it has also illustrated some key challenges. Novel data streams have the potential to reshape epidemic preparedness and response, but there remains a pressing need to address issues around sharing, sustainability and scalability that could hinder future epidemic preparedness.

Challenges around data analysis span the whole life cycle of an epidemic. Early on, there is a need for fundamental insights into the epidemiological characteristics of a novel infection, from transmission potential to natural history. This requires a rapid scale-up of testing and sequencing, a fast assessment of clinical impact, and open sharing of early findings. As outbreaks grow, there is a need for forecasting of disease dynamics, estimation of potential burden and evaluation of interventions. Coordination and equitable data generation is critical here, to ensure that recommendations and responses are relevant to all affected demographics and settings. In the later stages, attention turns to estimation of vaccine effectiveness and tracking flare-ups and evolutionary dynamics. This can include analysis of longer-term datasets including cohort studies, which can provide insights into epidemic processes over months or even years.