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.

Automated summary

Syndromic surveillance has driven the integration of big data analytics into animal disease surveillance systems in the past decade. As more data sources can be digitally processed, we discuss the need to ensure that the generated information is suitable for disease surveillance by focusing on data digitalization and information delivery design. We argue that data-driven surveillance's value depends on a needs-driven approach and highlight current challenges and research frontiers in syndromic surveillance.