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. 2025 Apr 15;18(1):174.
doi: 10.1186/s13104-025-07207-1.

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

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  4
Affiliations

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

Bonjean Maxime et al. BMC Res Notes. .

Abstract

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.

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

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: As part of this training, we used open-source data from the ECDC database, in accordance with the EU Open Data Directive and the ECDC open data policy. This approach avoids ethical issues relating to confidentiality and consent, while aligning our research with the principles of data transparency and re-use promoted by the ECDC. This approach also promotes the reproducibility of the research and maximises the public health and scientific impact of the data used. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Illustration of the missing link between contextual metadata and molecular data from pathogens. The figure shows weekly time series of pathogen data provided by the TESSy dataset. (source ECDC [15]) to monitor the evolution of SARS-CoV-2 variants in one EU Member State.
Fig. 2
Fig. 2
Data-Driven Integration of an Additional Variable into the Simulation Dataset. Note Data-driven are employed to introduce an additional variable into the initial dataset using the multi-parametric simulation tool
Fig. 3
Fig. 3
Application of Data-Driven and Random Simulations: Integrating Direct and Indirect Links for SARS-CoV-2 and other Pathogens Risk Modelling
Fig. 4
Fig. 4
Screenshots of the Third Tab in the Multi-Parametric Simulator Shiny Application
Fig. 5
Fig. 5
Application of the Multi-Parametric Tool in a Simulated Pandemic Scenario. Note Data-driven are employed to introduce an additional variable into the initial dataset using the multi-parametric simulation tool
See this image and copyright information in PMC

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