A key feature of disease surveillance today is the increasing volume and diversity of potentially relevant data, including from underserved groups. Examples include information from laboratory surveillance, ambulance dispatch data, internet search activity as well as WHO disease outbreak news. Benefits from such “big data” require parallel development of new analytical techniques but also a key focus on converting these data into information that is ultimately useful to those responsible for public health. The research work within this theme aims to address a number of key questions. First, how can new technologies and methods for data linkage, analysis and interpretation enhance the timely detection and assessment of events. Second how can the information outputs be made more user-centric? And third how can we make better judgements and assessments in relation to impact on public health for some low probability high-impact adverse events?

Lead researchers