Journal article
Fast and near-optimal monitoring for healthcare acquired infection outbreaks
PLoS computational biology, Vol.15(9), pp.e1007284-e1007284
09/16/2019
DOI: 10.1371/journal.pcbi.1007284
PMCID: PMC6762212
PMID: 31525183
Abstract
According to the Centers for Disease Control and Prevention (CDC), one in twenty five hospital patients are infected with at least one healthcare acquired infection (HAI) on any given day. Early detection of possible HAI outbreaks help practitioners implement countermeasures before the infection spreads extensively. Here, we develop an efficient data and model driven method to detect outbreaks with high accuracy. We leverage mechanistic modeling of C. difficile infection, a major HAI disease, to simulate its spread in a hospital wing and design efficient near-optimal algorithms to select people and locations to monitor using an optimization formulation. Results show that our strategy detects up to 95% of “future” C. difficile outbreaks. We design our method by incorporating specific hospital practices (like swabbing for infections) as well. As a result, our method outperforms state-of-the-art algorithms for outbreak detection. Finally, a qualitative study of our result shows that the people and locations we select to monitor as sensors are intuitive and meaningful.
Details
- Title: Subtitle
- Fast and near-optimal monitoring for healthcare acquired infection outbreaks
- Creators
- Bijaya Adhikari - University of Iowa, Computer ScienceBryan LewisAnil VullikantiJosé Mauricio JiménezB. Aditya Prakash
- Resource Type
- Journal article
- Publication Details
- PLoS computational biology, Vol.15(9), pp.e1007284-e1007284
- DOI
- 10.1371/journal.pcbi.1007284
- PMID
- 31525183
- PMCID
- PMC6762212
- NLM abbreviation
- PLoS Comput Biol
- ISSN
- 1553-7358
- eISSN
- 1553-7358
- Language
- English
- Date published
- 09/16/2019
- Academic Unit
- Computer Science
- Record Identifier
- 9984130695602771
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