Detecting Virus Exposure During the Pre Symptomatic Incubation Period Using Physiological Data (with Supplementary Materials)
Abstract
Early pathogen exposure detection allows better patient care and faster implementation of public health measures (patient isolation, contact tracing). Existing exposure detection most frequently relies on overt clinical symptoms, namely fever, during the infectious prodromal period. We have developed a robust machine learning method to better detect asymptomatic states during the incubation period using subtle, sub-clinical physiological markers. Using high-resolution physiological data from non-human primate studies of Ebola and Marburg viruses, we pre-processed the data to reduce short-term variability and normalize diurnal variations, then provided these to a supervised random forest classification algorithm. In most subjects detection is achieved well before the onset of fever; subject cross-validation lead to 5214h mean early detection (at >0.90 area under the receiver-operating characteristic curve). Cross-cohort tests across pathogens and exposure routes also lead to successful early detection(2816h and 4322h, respectively). We discuss which physiological indicators are most informative for early detection and options for extending this capability to lower data resolution and wearable, non-invasive sensors.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 30, 2016
- Accession Number
- AD1033685
Entities
People
- Albert Jr J. Swiston
- Albert Reuther
- Anna N. Honko
- Arthur J Goff
- Catherine Cabrera
- Franco Rossi
- Greg Ciccarelli
- Jack Fleischman
- John Trefry
- Lauren E Milechin
- Lisa Hensley
- Mark Hernandez
- Shakti K Davis
- Steven Schwartz
- Tejash Patel
- W. P. Pratt