Interpretative Labor and the Bane of Nonstandardized Metadata in Public Health Surveillance and Food Safety

Abstract

Open-source DNA sequence databases have long been touted as beneficial to public health, including the facilitation of earlier detection and response to infectious disease outbreaks. Of critical importance to harnessing these benefits is the metadata that describe general and other domain-specific attributes (eg, collection location, isolate type) of a sample. Unlike the sequence data, metadata are often incomplete and lack adherence to an international standard. Here, we describe the problem posed by such variable and incomplete metadata in terms of interpretative labor costs (the time and energy necessary to make sense of the signal in the genetic data) and the impact such metadata have on foodborne outbreak detection and response. Improving the quality of sequence-associated metadata would allow for earlier detection of emerging food safety hazards and allow faster response to foodborne outbreaks.

Keywords: foodborne pathogen; interpretive labor; metadata; whole-genome sequencing.