Data-fed, needs-driven: designing analytical workflows fit for disease surveillance

Syndromic surveillance has been an important driver for the incorporation of “big
data analytics” into animal disease surveillance systems over the past decade. As
the range of data sources to which automated data digitalization can be applied
continues to grow, we discuss how to move beyond questions around the means
to handle volume, variety and velocity, so as to ensure that the information generated is fit for disease surveillance purposes. We make the case that the value of data-driven surveillance depends on a "needs-driven" design approach to data digitalization and information delivery and highlight some of the current challenges and research frontiers in syndromic surveillance.