Lindsay Meyers, BioFire Diagnostics
Christine C. Ginocchio, BioFire Diagnostics
Aimie N. Faucett, BioFire Diagnostics
Frederick S. Nolte, Medical University of South Carolina
Per H. Gesteland, The University of Utah
Amy Leber, Children's Hospital Columbus
Diane Janowiak, South Bend Medical Foundation
Virginia Donovan, NYU Winthrop Hospital
Jennifer Dien Bard, Children's Hospital Los Angeles
Silvia Spitzer, Stony Brook University Hospital
Kathleen A. Stellrecht, Albany Medical Center
Hossein Salimnia, Wayne State University School of Medicine
Rangaraj Selvarangan, Children's Mercy HospitalFollow
Stefan Juretschko, Division of Infectious Disease Diagnostics
Judy A. Daly, The University of Utah
Jeremy C. Wallentine, Intermountain Medical Center
Kristy Lindsey, University of Massachusetts Medical School
Franklin Moore, University of Massachusetts Medical School
Sharon L. Reed, University of California, San Diego
Maria Aguero-Rosenfeld, NYU Langone Health
Paul D. Fey, University of Nebraska Medical Center
Gregory A. Storch, Washington University in St. Louis
Steve J. Melnick, Nicklaus Children's Hospital
Christine C. Robinson, The Children's Hospital, Aurora
Jennifer F. Meredith, Greenville Hospital System
Camille V. Cook, BioFire Diagnostics
Robert K. Nelson, BioFire Diagnostics
Jay D. Jones, BioFire Diagnostics
Samuel V. Scarpino, Northeastern University
Benjamin M. Althouse, University of Washington, Seattle
Kirk M. Ririe, BioMérieux SA
Bradley A. Malin, Vanderbilt University
Mark A. Poritz, BioMérieux SA

Document Type


Publication Date



DOI: 10.2196/publichealth.9876; PMCID: PMC6054708


© Lindsay Meyers, Christine C Ginocchio, Aimie N Faucett, Frederick S Nolte, Per H Gesteland, Amy Leber, Diane Janowiak,.

Background: Health care and public health professionals rely on accurate, real-time monitoring of infectious diseases for outbreak preparedness and response. Early detection of outbreaks is improved by systems that are comprehensive and specific with respect to the pathogen but are rapid in reporting the data. It has proven difficult to implement these requirements on a large scale while maintaining patient privacy.

Objective: The aim of this study was to demonstrate the automated export, aggregation, and analysis of infectious disease diagnostic test results from clinical laboratories across the United States in a manner that protects patient confidentiality. We hypothesized that such a system could aid in monitoring the seasonal occurrence of respiratory pathogens and may have advantages with regard to scope and ease of reporting compared with existing surveillance systems.

Methods: We describe a system, BioFire Syndromic Trends, for rapid disease reporting that is syndrome-based but pathogen-specific. Deidentified patient test results from the BioFire FilmArray multiplex molecular diagnostic system are sent directly to a cloud database. Summaries of these data are displayed in near real time on the Syndromic Trends public website. We studied this dataset for the prevalence, seasonality, and coinfections of the 20 respiratory pathogens detected in over 362,000 patient samples acquired as a standard-of-care testing over the last 4 years from 20 clinical laboratories in the United States.

Results: The majority of pathogens show influenza-like seasonality, rhinovirus has fall and spring peaks, and adenovirus and the bacterial pathogens show constant detection over the year. The dataset can also be considered in an ecological framework; the viruses and bacteria detected by this test are parasites of a host (the human patient). Interestingly, the rate of pathogen codetections, on average 7.94% (28,741/362,101), matches predictions based on the relative abundance of organisms present.

Conclusions: Syndromic Trends preserves patient privacy by removing or obfuscating patient identifiers while still collecting much useful information about the bacterial and viral pathogens that they harbor. Test results are uploaded to the database within a few hours of completion compared with delays of up to 10 days for other diagnostic-based reporting systems. This work shows that the barriers to establishing epidemiology systems are no longer scientific and technical but rather administrative, involving questions of patient privacy and data ownership. We have demonstrated here that these barriers can be overcome. This first look at the resulting data stream suggests that Syndromic Trends will be able to provide high-resolution analysis of circulating respiratory pathogens and may aid in the detection of new outbreaks.

Journal Title

Journal of Medical Internet Research






Communicable disease, Epidemiology, Internet, Pathology, molecular, Patients, Privacy


Grant support

This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.

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