Spatiotemporal Data Modelling for Epidemiological Research in Hospitals.

Nosocomial infections are a great source of concern for healthcare organizations. The spatial layout of hospitals and the movements of patients play significant roles in the spread of outbreaks. However, the existing models are ad-hoc for a specific hospital and research topic. This work shows the design of a data model to study the spread of infections among hospital patients. Its spatial dimension describes the hospital layout with several levels of detail, and the temporal dimension describes everything that happens to the patients in the form of events, which can relate to the spatial dimension. The model is meant to be sufficiently general to fit any hospital layout and to be used for different epidemiological research topics. We proved the model's suitability by defining six queries based on patients' movements and contacts that could assist in several epidemiological research tasks, such as discovering potential transmission routes. The model was implemented as an RDF* knowledge graph, and the queries were in SPARQL*. Finally, we designed two experiments in which two outbreaks of Clostridium difficile were analyzed using several queries (four in the first experiment and two in the second) on a knowledge graph (105,000 nodes, 185,000 edges) with synthetic data.

The use of networks in spatial and temporal computational models for outbreak spread in epidemiology: A systematic review.

OBJECTIVES: To examine recent literature in order to present a comprehensive overview of the current trends as regards the computational models used to represent the propagation of an infectious outbreak in a population, paying particular attention to those that represent network-based transmission. METHODS: a systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Papers published in English between 2010 and September 2021 were sought in the ACM Digital Library, IEEE Xplore, PubMed and Scopus databases. RESULTS: Upon considering their titles and abstracts, 832 papers were obtained, of which 192 were selected for a full content-body check. Of these, 112 studies were eventually deemed suitable for quantitative and qualitative analysis. Emphasis was placed on the spatial and temporal scales studied, the use of networks or graphs, and the granularity of the data used to evaluate the models. The models principally used to represent the spreading of outbreaks have been stochastic (55.36%), while the type of networks most frequently used are relationship networks (32.14%). The most common spatial dimension used is a region (19.64%) and the most used unit of time is a day (28.57%). Synthetic data as opposed to an external source were used in 51.79% of the papers. With regard to the granularity of the data sources, aggregated data such as censuses or transportation surveys are the most common. CONCLUSION: We identified a growing interest in the use of networks to represent disease transmission. We detected that research is focused on only certain combinations of the computational model, type of network (in both the expressive and the structural sense) and spatial scale, while the search for other interesting combinations has been left for the future.