Dataset Information

Modelling pathogen spread in a healthcare network: Indirect patient movements.

ABSTRACT: Inter-hospital patient transfers (direct transfers) between healthcare facilities have been shown to contribute to the spread of pathogens in a healthcare network. However, the impact of indirect transfers (patients re-admitted from the community to the same or different hospital) is not well studied. This work aims to study the contribution of indirect transfers to the spread of pathogens in a healthcare network. To address this aim, a hybrid network-deterministic model to simulate the spread of multiresistant pathogens in a healthcare system was developed for the region of Lower Saxony (Germany). The model accounts for both, direct and indirect transfers of patients. Intra-hospital pathogen transmission is governed by a SIS model expressed by a system of ordinary differential equations. Our results show that the proposed model reproduces the basic properties of healthcare-associated pathogen spread. They also show the importance of indirect transfers: restricting the pathogen spread to direct transfers only leads to 4.2% system wide prevalence. However, adding indirect transfers leads to an increase in the overall prevalence by a factor of 4 (18%). In addition, we demonstrated that the final prevalence in the individual healthcare facilities depends on average length of stay in a way described by a non-linear concave function. Moreover, we demonstrate that the network parameters of the model may be derived from administrative admission/discharge records. In particular, they are sufficient to obtain inter-hospital transfer probabilities, and to express the patients' transfers as a Markov process. Using the proposed model, we show that indirect transfers of patients are equally or even more important as direct transfers for the spread of pathogens in a healthcare network.

SUBMITTER: Piotrowska MJ

PROVIDER: S-EPMC7728397 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Modelling pathogen spread in a healthcare network: Indirect patient movements.

Piotrowska Monika J MJ Sakowski Konrad K Karch André A Tahir Hannan H Horn Johannes J Kretzschmar Mirjam E ME Mikolajczyk Rafael T RT

PLoS computational biology 20201130 11

Inter-hospital patient transfers (direct transfers) between healthcare facilities have been shown to contribute to the spread of pathogens in a healthcare network. However, the impact of indirect transfers (patients re-admitted from the community to the same or different hospital) is not well studied. This work aims to study the contribution of indirect transfers to the spread of pathogens in a healthcare network. To address this aim, a hybrid network-deterministic model to simulate the spread o ...[more]

PMID: 33253154

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Project description:BACKGROUND: Reverse engineering of gene regulatory networks can be used to predict regulatory interactions of an organism faced with environmental changes, but can prove problematic, especially when focusing on complicated multi-factorial processes. Candida albicans is a major human fungal pathogen. During the infection process, this fungus is able to adapt to conditions of very low iron availability. Such adaptation is an important virulence attribute of virtually all pathogenic microbes. Understanding the regulation of iron acquisition genes will extend our knowledge of the complex regulatory changes during the infection process and might identify new potential drug targets. Thus, there is a need for efficient modelling approaches predicting key regulatory events of iron acquisition genes during the infection process. RESULTS: This study deals with the regulation of C. albicans iron uptake genes during adhesion to and invasion into human oral epithelial cells. A reverse engineering strategy is presented, which is able to infer regulatory networks on the basis of gene expression data, making use of relevant selection criteria such as sparseness and robustness. An exhaustive use of available knowledge from different data sources improved the network prediction. The predicted regulatory network proposes a number of new target genes for the transcriptional regulators Rim101, Hap3, Sef1 and Tup1. Furthermore, the molecular mode of action for Tup1 is clarified. Finally, regulatory interactions between the transcription factors themselves are proposed. This study presents a model describing how C. albicans may regulate iron acquisition during contact with and invasion of human oral epithelial cells. There is evidence that some of the proposed regulatory interactions might also occur during oral infection. CONCLUSIONS: This study focuses on a typical problem in Systems Biology where an interesting biological phenomenon is studied using a small number of available experimental data points. To overcome this limitation, a special modelling strategy was used which identifies sparse and robust networks. The data is augmented by an exhaustive search for additional data sources, helping to make proposals on regulatory interactions and to guide the modelling approach. The proposed modelling strategy is capable of finding known regulatory interactions and predicts a number of yet unknown biologically relevant regulatory interactions.

Dataset Information

Modelling pathogen spread in a healthcare network: Indirect patient movements.

Publications

Modelling pathogen spread in a healthcare network: Indirect patient movements.

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