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Spatiotemporal Data Modelling for Epidemiological Research in Hospitals.
IEEE Journal of Biomedical and Health Informatics
|June 21, 2024
Summary
This study introduces a flexible data model to track hospital-acquired infections by mapping patient movements and events. This approach aids in understanding disease spread and identifying transmission routes within healthcare settings.
Area of Science:
- Health Informatics
- Epidemiology
- Data Modeling
Background:
- Nosocomial infections pose significant risks in healthcare.
- Hospital spatial layout and patient movement are key factors in outbreak spread.
- Existing infection spread models lack generalizability.
Purpose of the Study:
- To design a generalizable data model for studying hospital-acquired infection spread.
- To incorporate spatial (hospital layout) and temporal (patient events) dimensions.
- To facilitate diverse epidemiological research topics.
Main Methods:
- Developed a data model with spatial and temporal dimensions.
- Implemented the model as an RDF* knowledge graph.
- Utilized SPARQL* for data querying.
- Designed and conducted experiments with synthetic data.
Main Results:
- Demonstrated model suitability with six epidemiological queries.
- Queries focused on patient movement and contacts to identify transmission routes.
- Successfully analyzed two Clostridium difficile outbreaks using the knowledge graph.
Conclusions:
- The proposed data model is adaptable to various hospital layouts and epidemiological research questions.
- The RDF* knowledge graph implementation enables effective analysis of infection spread.
- This approach can enhance the understanding and control of nosocomial infections.
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