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Updated: Dec 27, 2025

In Vivo Modeling of the Morbid Human Genome using Danio rerio
Published on: August 24, 2013
A Bayesian approach for detecting a disease that is not being modeled
John M Aronis1, Jeffrey P Ferraro2, Per H Gesteland2
1Real-time Outbreak and Disease Surveillance (RODS) Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
Abstract:
Over the past decade, outbreaks of new or reemergent viruses such as severe acute respiratory syndrome (SARS) virus, Middle East respiratory syndrome (MERS) virus, and Zika have claimed thousands of lives and cost governments and healthcare systems billions of dollars. Because the appearance of new or transformed diseases is likely to continue, the detection and characterization of emergent diseases is an important problem. We describe a Bayesian statistical model that can detect and characterize previously unknown and unmodeled diseases from patient-care reports and evaluate its performance on historical data.
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