Enterovirus D68 outbreak detection through a syndromic disease epidemiology network

Lindsay Meyers*, Jennifer Dien Bard, Ben Galvin, Jeff Nawrocki, Hubert G M Niesters, Kathleen A Stellrecht, Kirsten St George, Judy A Daly, Anne J Blaschke, Christine Robinson, Huanyu Wang, Camille V Cook, Ferdaus Hassan, Sam R Dominguez, Kristin Pretty, Samia Naccache, Katherine E Olin, Benjamin M Althouse, Jay D Jones, Christine C GinocchioMark A Poritz, Amy Leber, Rangaraj Selvarangan

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
55 Downloads (Pure)

Abstract

BACKGROUND: In 2014, enterovirus D68 (EV-D68) was responsible for an outbreak of severe respiratory illness in children, with 1,153 EV-D68 cases reported across 49 states. Despite this, there is no commercial assay for its detection in routine clinical care. BioFire® Syndromic Trends (Trend) is an epidemiological network that collects, in near real-time, deidentified. BioFire test results worldwide, including data from the BioFire® Respiratory Panel (RP).

OBJECTIVES: Using the RP version 1.7 (which was not explicitly designed to differentiate EV-D68 from other picornaviruses), we formulate a model, Pathogen Extended Resolution (PER), to distinguish EV-D68 from other human rhinoviruses/enteroviruses (RV/EV) tested for in the panel. Using PER in conjunction with Trend, we survey for historical evidence of EVD68 positivity and demonstrate a method for prospective real-time outbreak monitoring within the network.

STUDY DESIGN: PER incorporates real-time polymerase chain reaction metrics from the RPRV/EV assays. Six institutions in the United States and Europe contributed to the model creation, providing data from 1,619 samples spanning two years, confirmed by EV-D68 gold-standard molecular methods. We estimate outbreak periods by applying PER to over 600,000 historical Trend RP tests since 2014. Additionally, we used PER as a prospective monitoring tool during the 2018 outbreak.

RESULTS: The final PER algorithm demonstrated an overall sensitivity and specificity of 87.1% and 86.1%, respectively, among the gold-standard dataset. During the 2018 outbreak monitoring period, PER alerted the research network of EV-D68 emergence in July. One of the first sites to experience a significant increase, Nationwide Children's Hospital, confirmed the outbreak and implemented EV-D68 testing at the institution in response. Applying PER to the historical Trend dataset to determine rates among RP tests, we find three potential outbreaks with predicted regional EV-D68 rates as high as 37% in 2014, 16% in 2016, and 29% in 2018.

CONCLUSIONS: Using PER within the Trend network was shown to both accurately predict outbreaks of EV-D68 and to provide timely notifications of its circulation to participating clinical laboratories.

Original languageEnglish
Article number104262
Number of pages6
JournalJournal of Clinical Virology
Volume124
Early online date16-Jan-2020
DOIs
Publication statusPublished - Mar-2020

Cite this