TY - JOUR T1 - Automated collection of pathogen-specific diagnostic data for real-time syndromic epidemiological studies JF - bioRxiv DO - 10.1101/157156 SP - 157156 AU - Lindsay Meyers AU - Christine C. Ginocchio AU - Aimie N. Faucett AU - Frederick S. Nolte AU - Per H. Gesteland AU - Amy Leber AU - Diane Janowiak AU - Virginia Donovan AU - Jennifer Dien Bard AU - Silvia Spitzer AU - Kathleen A. Stellrecht AU - Hossein Salimnia AU - Rangaraj Selvarangan AU - Stefan Juretschko AU - Judy A. Daly AU - Jeremy C. Wallentine AU - Kristy Lindsey AU - Franklin Moore AU - Sharon L. Reed AU - Maria Aguero-Rosenfeld AU - Paul D. Fey AU - Gregory A. Storch AU - Steve J. Melnick AU - Camille V. Cook AU - Robert K. Nelson AU - Jay D. Jones AU - Samuel V. Scarpino AU - Benjamin M. Althouse AU - Kirk M. Ririe AU - Bradley A. Malin AU - Mark A. Poritz Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/07/31/157156.abstract N2 - Public health decision makers rely on accurate, real-time monitoring of infectious diseases for outbreak preparedness and response. Pathogen early detection is improved by systems that are disease-specific. Here we describe a system, FilmArray® Trend, for rapid disease reporting that is syndrome-based but pathogen-specific. Results from a multiplex molecular diagnostic test are sent directly to a cloud database. www.syndromictrends.com presents these data in near real-time. Trend preserves patient privacy by removing or obfuscating patient identifiers. We summarize the respiratory pathogen results, for 20 organisms from >320,000 patient samples acquired as standard of care testing over the last four years from 18 clinical laboratories in the United States. The majority of pathogens show an influenza-like seasonality, rhinovirus has fall and spring peaks and adenovirus and bacterial pathogens show constant detection over the year. Interestingly, the rate of pathogen co-detections, on average 7.6%, matches expectations based on the relative abundance of organisms present. ER -