Integrating patient metadata and pathogen genomic data: advancing pandemic preparedness with a multi-parametric simulator

被引：0

作者：

Bonjean Maxime ^{[1
]}

Ambroise Jérôme ^{[1
]}

Orchard Francisco ^{[2
]}

Sentis Alexis ^{[2
]}

Hurel Julie ^{[1
]}

Hayes Jessica S. ^{[3
]}

Connolly Máire A. ^{[3
]}

Jean-Luc Gala ^{[1
]}

机构：

[1] Centre for Applied Molecular Technologies (CTMA), Experimental and Clinical Research Institute (IREC), UCLouvain, Avenue Hippocrate 54/B1.54.01, Brussels

[2] Epiconcept, Paris

[3] School of Health Sciences, College of Medicine, Nursing and Health Sciences, University of Galway, Galway

来源：

BMC Research Notes | / 18卷 / 1期

关键词：

Accidental or intentional biological incident; Functional exercise; Multi-parametric simulator; Natural; Pandemics; Preparedness; Public health crisis; Response; Training;

D O I：

10.1186/s13104-025-07207-1

中图分类号：

学科分类号：

摘要：

Stakeholder training is essential for handling unexpected crises swiftly, safely, and effectively. Functional and tabletop exercises simulate potential public health crises using complex scenarios with realistic data. These scenarios are designed by integrating datasets that represent populations exposed to a pandemic pathogen, combining pathogen genomic data generated through high-throughput sequencing (HTS) together with patient epidemiological, clinical, and demographic information. However, data sharing between EU member states faces challenges due to disparities in data collection practices, standardisation, legal frameworks, privacy, security regulations, and resource allocation. In the Horizon 2020 PANDEM-2 project, we developed a multi-parametric training tool that links pathogen genomic data and metadata, enabling training managers to enhance datasets and customise scenarios for more accurate simulations. The tool is available as an R package: https://github.com/maous1/Pandem2simulator and as a Shiny application: https://uclouvain-ctma.Shinyapps.io/Multi-parametricSimulator/ , facilitating rapid scenario simulations. A structured training procedure, complete with video tutorials and exercises, was shown to be effective and user-friendly during a training session with twenty PANDEM-2 participants. In conclusion, this tool enhances training for pandemics and public health crises preparedness by integrating complex pathogen genomic data and patient contextual metadata into training simulations. The increased realism of these scenarios significantly improves emergency responder readiness, regardless of the biological incident's nature, whether natural, accidental, or intentional. © 2025. The Author(s).

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