Statistical outbreak detection by joining medical records and pathogen similarity.

James K. Miller, Jieshi Chen, Alexander Sundermann, Jane W. Marsh, Melissa I. Saul, Kathleen A. Shutt, Marissa Pacey, Mustapha M. Mustapha, Lee H. Harrison, Artur Dubrawski

Abstract: Highlights • Joining pathogen similarity with epidemiological data increases outbreak detection. • Joint root cause and patient inference improves detection of small outbreaks. • Machine learning methods can effectively tune outbreak detection models. Abstract We present a statistical inference model for the detection and characterization of outbreaks of hospital associated infection. The approach combines patient exposures, determined from electronic medical records, and pathogen similarity, determined by whole-genome sequencing, to simultaneously identify probable outbreaks and their root-causes. We show how our model can be used to target isolates for whole-genome sequencing, improving outbreak detection and characterization even without comprehensive sequencing. Additionally, we demonstrate how to learn model parameters from reference data of known outbreaks. We demonstrate model performance using semi-synthetic experiments. Graphical abstract Download : Download high-res image (95KB) Download : Download full-size image Previous article in issue Next article in issue