Enterovirus D68 outbreak detection through a syndromic disease epidemiology network
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.
Journal of Clinical Virology
Enterovirus D-68, Epidemiology, Machine learning
Meyers, L., Dien Bard, J., Galvin, B., Nawrocki, J., Niesters, H. G., Stellrecht, K. A., St. George, K., Daly, J. A., Blaschke, A. J., Robinson, C., Wang, H., Cook, C. V., Hassan, F., Dominguez, S. R., Pretty, K., Naccache, S., Olin, K. E., Althouse, B. M., Jones, J. D., Ginocchio, C. C., Poritz, M. A., Leber, A., Selvarangan, R. Enterovirus D68 outbreak detection through a syndromic disease epidemiology network Journal of Clinical Virology 124, (2020).