PRESymptomatic AGent Exposure Detection (PRESAGED)

Organization: MIT Lincoln Laboratory
Co-Developer(s): U.S. Army Medical Research Institute of Infectious Diseases
Year: 2017

PRESymptomatic AGent Exposure Detection (PRESAGED) is an algorithm that uses real-time physiological data—such as heart electrical activity (electrocardiography, ECG), breathing rate and temperature—to predict the probability that a person has been exposed to a pathogen, such as a virus or bacteria. In non-human primates, PRESAGED has shown to provide two to three days of early warning of incipient symptoms, without regard to the particular pathogen (virus or bacteria), exposure route, pathogen dose, or animal species. PRESAGED has also shown to provide early warning on entirely novel pathogens (i.e., pathogens unavailable for algorithm training), suggesting that this approach will be robust in detecting emergent diseases, such as SARS/MERS or novel flu strains, well before new training data are available. PRESAGED can increase early interventions and provide faster, more confident implementation of public health measures, such as isolation, that will improve overall population health by blunting epidemics. Triggering the use of diagnostics early would allow clinicians to target drug-based interventions, such as using the exact right antibiotic rather than relying on broad-spectrum versions that contribute to the evolution of drug-resistant bacteria. Simple supportive care for mild infections could ease the burden on healthcare workers in hospitals, nursing homes or college campuses.

 

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