Automated collection of pathogen-specific diagnostic data for real-time syndromic epidemiological studies

Lindsay Meyers; Christine C. Ginocchio; Aimie N. Faucett; Frederick S. Nolte; Per H. Gesteland; Amy Leber; Diane Janowiak; Virginia Donovan; Jennifer Dien Bard; Silvia Spitzer; Kathleen A. Stellrecht; Hossein Salimnia; Rangaraj Selvarangan; Stefan Juretschko; Judy A. Daly; Jeremy C. Wallentine; Kristy Lindsey; Franklin Moore; Sharon L. Reed; Maria Aguero-Rosenfeld; Paul D. Fey; Gregory A. Storch; Steve J. Melnick; Christine C. Robinson; Jennifer F. Meredith; Camille V. Cook; Robert K. Nelson; Jay D. Jones; Samuel V. Scarpino; Benjamin M. Althouse; Kirk M. Ririe; Bradley A. Malin; Mark A. Poritz

Similarly, the circulating pathogen number of Figure 5A is identical to the Species Richness measure of ecology. We calculate MIE using frequencies (P i ) of pathogen positivity per FilmArray test and note that the sum of all pathogen frequencies can add up to more than 100% because of co-detections or be less than 100% because of the presence of negative tests. In this 440 regard, MIE differs from PIE because it is not a probability measure. infections at a rate that can be predicted by their observed abundance. Data however may be biased by the patient population tested and the type of respiratory disease. The data also does not rule out that there are particular respiratory pathogens that occur more or less often in mixed infections than predicted by their individual percent positivity rates. [74, 83] . As we improve our ability to remove non-patient test results from the Trend dataset we will be able to characterize 450 specific virus co-detection rates and their significance [65, 66, 78, 79, [84] [85] [86] .