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

The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. . https://doi.org/10.1101/157156 doi: bioRxiv preprint some non-patient tests, we have chosen not to analyze every possible dual or triple infection individually. Rather we have taken a global approach and compared the overall rate of observed co-detections with MIE, which is a measure of the diversity of viruses circulating in a specific region and time period. MIE is similar, but not identical, to PIE (Probability of Interspecific Encounter [80] ), also referred to as the Gini-Simpson index (1-D, where D is the Simpson's 435 index [81, 82] ), which is used in ecology as a measure of the species diversity of a region.