Sample-initiated retrospective outbreak investigations (SIROIs) have been increasingly used by U.S. public health and regulatory partners over the past decade. Conventional outbreak investigations begin with a cluster of illness, or when several clinical cases test positive for a pathogen. After a cluster has been detected, bacterial isolates from patients are genetically analyzed, where a shared DNA pattern suggests a linkage to a common source, thereby triggering an epidemiologic investigation. Once a likely source is identified, agencies trace back the contaminated food to its production site to issue recalls. SIROIs flip this approach by starting with samples of food or environmental isolates and then retrospectively searching for matching human cases in databases. A compelling advantage of sample-initiated outbreak investigations is their ability to rapidly identify and investigate outbreaks by matching isolates in food or environmental samples to human clinical cases. This approach leads to more timely identification of nation-wide outbreaks that might be geographically dispersed. This reduces the number of illnesses through rapid public health intervention. Because systematic collection of isolates allows for genomics analyses, they can also more reliably detect novel associations between pathogens and the food items that transmitted them, bridging gaps in conventional surveillance methods.
One notable example of a successful SIROI involved Listeria monocytogenes contamination of ice cream. Listeriosis is a rare but sometimes severe invasive infection resulting from this pathogen. In August 2017, Listeria isolates collected from ice cream samples during routine testing were uploaded to national genomic databases. These samples were later found to match clinical isolates from two human listeriosis cases from 2013, prompting a multi-agency investigation that linked the illnesses to a single ice cream producer, leading to a large-scale recall. This outbreak highlights how this multi-agency collaboration can uncover outbreaks that would be undiscovered by conventional surveillance.