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

Fernanda C Dórea; Flavie Vial; Crawford W Revie

doi:10.3389/fvets.2023.1114800

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

Front Vet Sci. 2023 Jan 27:10:1114800. doi: 10.3389/fvets.2023.1114800. eCollection 2023.

Authors

Fernanda C Dórea¹, Flavie Vial², Crawford W Revie³

Affiliations

¹ Department of Disease Control and Epidemiology, National Veterinary Institute, Uppsala, Sweden.
² Animal and Plant Health Agency, Sand Hutton, United Kingdom.
³ Department of Computer and Information Sciences, University of Strathclyde, Glasgow, United Kingdom.

Abstract

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.

Keywords: big data; data-driven surveillance; decision support system; epidemiology; syndromic surveillance.

Grants and funding

FD received funding from the Swedish research council Formas (Contract nr. 221-2011-2214).