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. 2019 Mar:91:103126.
doi: 10.1016/j.jbi.2019.103126. Epub 2019 Feb 13.

Statistical outbreak detection by joining medical records and pathogen similarity

James K Miller  1 , Jieshi Chen  2 , Alexander Sundermann  3 , Jane W Marsh  4 , Melissa I Saul  5 , Kathleen A Shutt  4 , Marissa Pacey  4 , Mustapha M Mustapha  4 , Lee H Harrison  4 , Artur Dubrawski  2
Affiliations

Statistical outbreak detection by joining medical records and pathogen similarity

James K Miller et al. J Biomed Inform. 2019 Mar.

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.

Keywords: Electronic medical records; Epidemiology; Outbreak detection; Statistical inference; Transmission of pathogens; Whole genome sequencing.

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Figures

Figure 1:
Figure 1:
Receiver operating characteristic (ROC) curves for proposed methods and the DITOw0 baseline. Shaded region represents 95% confidence envelope.
Figure 2:
Figure 2:
Receiver operating characteristic (ROC) curves for DITOc and select baselines. Shaded region represents 95% confidence envelope.
Figure 3:
Figure 3:
Activity monitoring characteristic (AMOC) curves for proposed methods and the DITOw0 baseline. Shaded region represents 95% confidence envelope.
Figure 4:
Figure 4:
Activity monitoring characteristic (AMOC) curves for DITOc and select baselines. Shaded region represents 95% confidence envelope.
See this image and copyright information in PMC

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