‘Big data’ has promised significant improvements for the global surveillance of infectious disease. This SSHAP Case Study highlights how, over the past two decades, new disease surveillance practices built on amassing and processing large data sets – analysed computationally to reveal patterns, trends, and associations, relating to human behaviour and interactions – have been successful in the advanced forecasting of deadly disease outbreaks including severe acute respiratory syndrome (SARS), Middle East respiratory syndrome coronavirus (MERS-CoV), human influenza, the Ebola virus and novel coronavirus (COVID-19).
The increasing incorporation of non-expert evidence – that is, data that is collected and analysed from sources outside of traditional clinical/healthcare sectors into infectious disease and public health surveillance practices – must be continually monitored and verified as technological capacities and innovation towards the rapid identification of public health threats advance.
