Integrating Patient Metadata and Pathogen Genomic Data: Advancing Pandemic Preparedness with a Multi-Parametric Simulator

doi:10.21203/rs.3.rs-5098537/v1

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Integrating Patient Metadata and Pathogen Genomic Data: Advancing Pandemic Preparedness with a Multi-Parametric Simulator

https://doi.org/10.21203/rs.3.rs-5098537/v1

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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 H2020 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.

Health Policy

Bioinformatics

Medical Informatics

multi-parametric simulator

pandemics

natural

accidental or intentional biological incident

training

preparedness

response

public health crisis

functional exercise

The rapid, safe, and effective management of unusual crises, such as pandemics and accidental or intentional biological incidents, requires extensive training to develop the necessary expertise. Functional and tabletop exercises are regularly conducted to better prepare emergency responders and crisis managers for such scenarios (1, 2). These simulations use a combination of realistic epidemiological, clinical, and biological data from affected populations to replicate complex public health crises (3). To create meaningful functional and tabletop exercises, databases containing multiple variables that describe population groups exposed to simulated events – described as contextual metadata - are required.

To adequately prepare public health professionals for future pandemics or biological incidents, pathogen genomic data must be combined with patients’ contextual metadata. High-throughput sequencing (HTS), also known as next-generation sequencing (NGS), has revolutionised the collection of pathogen genomic data by enabling the rapid sequencing of entire genomes during outbreaks (4, 5). Since the onset of the COVID-19 pandemic, the availability of open-access genome databases has increased significantly, providing essential genomic features such as taxonomy, new mutations, drug resistance genes, and virulence factors (6, 7). These genome insights are crucial for assessing the risk of pathogen spread, and must be integrated with patient metadata including as demographics, health histories, and disease outcomes, to gain a deeper understanding of the pathogen’s lethality and transmission potential. (8, 9)

If this integration were consistently possible in practice, it would greatly enhance the preparation of exercises for future pandemics. However, despite the increasing availability of genomic data, open access to this contextual metadata remains limited (10). Data sharing and interconnection among EU member states face obstacles due to differences in data collection methods, standardisation practices, legal frameworks, privacy, and security regulations, as well as disparities in resources and infrastructure (Fig. 1) (11–13).

To address these challenges, the Horizon 2020 PANDEM-2 project developed a dedicated training platform to improve pandemic preparedness and response. As part of this initiative, our team designed an innovative multi-parametric simulation tool that realistically links pathogen molecular data with patient contextual metadata (3, 14).

By combining pathogen genomic data with patient contextual metadata, this tool allows trainees to work on realistic training scenario that simulate disease transmission, evolution, and impact on various populations based on specific genomic features. When incorporated into training exercises, these integrated databases not only better prepare trainees for real-time monitoring and rapid response to emerging pathogen variants but also enhance their ability to track pathogen spread and inform targeted public health interventions.

This simulator enables training managers and trainees to enrich existing datasets by adding new variables through data-driven or random simulations. It also allows users to adjust the proportion of specific variables based on scenario requirements and objectives, helping to achieve desired outcomes. Built using an R package and accessible via a Shiny application, the tool enhances accessibility and usability.

The purpose of this paper is to present the simulator, providing an overview of its features and demonstrating the applications and training procedures developed during the PANDEM-2 project.

Our multiparameter simulation tool enables the user to adjust an existing initial dataset corresponding to the scenario-driven evolution of the number of cases during a specified period.

Accordingly, the initial dataset should include at least two variables: (i) the time expressed in a pre- defined format (e.g., YY-MM-DD) and (ii) the incidence expressed as an integer. It is worth noting that this incidence can correspond to the number of new cases or of new hospitalizations at any given time.

Aside from these two mandatory columns, the initial dataset can also include additional variables (e.g., localisation [country name or region], vaccination status).

This initial dataset can be modified by the user through two distinct operations: (i) adding a new variable and (ii) modifying a relative risk characterising the association between two selected variables.

These two operations can be used multiple times in order to add multiple distinct variables and/or modifying multiple distinct relative risks. The following steps are described in detail:

2.1 Methodological Steps

2.1.1 Step 1: Addition of a new variable

The multi-parametric tool can be used to enrich the initial dataset (which represents the evolution of the number of infected cases) in two ways: A data-driven simulation or a random simulation (Supplementary Fig. 1).

(a) Data-driven simulation (Fig. 2): This method uses a KNN (K-Nearest Neighbours)-based supervised classification method. The user must provide a second (named learning) dataset that has a variable (named X) that is also present in the initial dataset, as well as an additional variable (named Y) that will be added to the initial dataset. The KNN method learns the relationship between X and Y in the learning dataset and then predicts realistic values for this Y variable in the training dataset based on the values of the X variable.

This straightforward method can be viewed as a means of connecting databases that were previously unconnected but share a common variable (for instance, time).

(b) Random simulation: This is useful if the user wants to add another additional variable but does not have a learning database (as in data-driven simulation). The user can enter the name of the new categorical variable (for instance, vaccination status) as well as each of its levels (unvaccinated, first and second dose of vaccination). The user can then choose the desired appropriate proportion of each level.

2.1.2 Step 2: Modifying a relative risk

In addition to enriching an initial dataset by adding new variables, the multivariate simulation tool also enables users to adjust the proportions of levels of variables within specific groups of patients based on pre-specified levels of other variables. Such adjustments can be made to meet the requirements and objectives of various scenarios. The «enrichment» function T is used to achieve the desired result. Users can adjust the relative risk (RR) to a desired level, enhancing the tool's flexibility for creating diverse scenarios during epidemic training sessions. It is worth noting that, when the RR is modified, proportions of the levels of each variable are maintained.

We developed our multi-parameter simulation tool using the R package, which is available at https://github.com/maous1/Pandem2simulator. We practised functional programming with the purrr package (16). To enhance user-friendliness and ensure widespread accessibility, we also created a Shiny application, which can be accessed at: https://uclouvain- ctma.Shinyapps.io/MultiparametricSimulator/.

2.2 Training session to teach non-expert trainees how to use the multi-parametric tool

As part of H2020 project PANDEM-2, which aims to develop a pandemic preparedness training platform, we conducted a training session on February 1, 2023. This session involved 20 participants from the PANDEM-2 project, including first responders, epidemiologists, public health experts, analysts, software developers, supply chain managers, and disaster management professionals. During the 30-minute training session, the trainees learnt how to use the multi-parametric tool. This was followed by a hands-on training activity in which they were tasked to create datasets for the following pandemic scenarios:

New SARS-CoV-2 variant of concern (VOC) spreading rapidly in young people: trainees were asked to create a realistic dataset for a FX (Functional exercise) that tracked the evolution of the number of infected cases caused by this new Sars-CoV-2 VOC.
Highly virulent and resistant bacteria which spreads mainly in immunocompromised people and/or those above the age of 65: trainees were asked to generate a dataset for a FX based on the evolution of the number of infected cases.
Influenza H1N1 virus with a risk of hospitalisation depending on vaccination status and age group: trainees were tasked with generating a dataset for a FX that reflected the evolution of the number of infected cases.

A questionnaire-based evaluation of the multiparametric simulator was conducted with trainees after their training session to assess the tool’s effectiveness in generating data and facilitating functional exercises (supplementary file 2: Questionnaire S1). The questionnaire focused on the clarity of training explanations concerning variable generation methods, data enrichment concepts, and dataset visualisation. It also examined the tool's ability to modify and visualise datasets and gathered feedback on usability improvements. Finally, suggestions

for tool development, layout refinement, and content enhancement were solicited to fully capture the assessment's scope.

2.3 Use of the multi-parametric tool in a functional exercise (FX)

In the context of crisis management and preparedness, a FX is a type of simulation or drill that tests and improves the capabilities and coordination of stakeholders involved in responding to a major crisis or emergency. The goal of FX is to simulate a realistic crisis scenario, allowing participants to practise their roles, responsibilities, and decision-making processes in a safe environment (17), and to evaluate their responses.

On March 15–16, 2023, the multi-parametric tool was used to simulate an influenza pandemic caused by the transmission of a novel viral strain from birds to humans. The FX evaluated the response strategies implemented by two interacting Public Health Emergency Operation Centres from the national public health agencies of neighbouring countries (Germany and the Netherlands) and assessed their preparedness levels.

3.1 Shiny Application overview:

The Shiny application comprises 5 tabs, each dedicated to a different stage in the process. The first tab focuses on importing the initial dataset import (Fig. 3). In the second tab the user can easily add new variables to the dataset. The third tab allows the user to visualise the dataset according to three specific variables. The time, the first variable, is consistently displayed on the x-axis. Meanwhile, the levels of the second variable are shown in separate panels, while the levels of the third variable are differentiated by different colours. In the fourth tab, the user can modify the RR, which is then displayed in the fifth tab.

3.2 Training session:

The training session, which included 20 participants, demonstrated the user-friendliness and accessibility of the multi-parametric Shiny application. The evaluation provided by the trainees confirms that the concise video presentation of the tool and the subsequent creation of three distinct scenarios resulted in a thorough understanding of its functionalities (Table 1).

Table 1

Overview of the training session participants' evaluations
Aspect	Appreciation (1–10 scale) - Averaged from 10 feedback
Clarity of presentation*	7.6
User-friendliness (easy to operate, straightforward to handle)	7.1
Usefulness of the tool for creating a functional exercise-targeted dataset	7.9
Understanding by the user of the range of functionalities	7.6

*well organised menus, intelligible display design, universal symbols, clean visualisation, understandable terminology

This successful training session demonstrates that the simulator can be effectively used by a variety of stakeholders, including public health practitioners, researchers, and policymakers.

The initial datasets for these scenarios, along with solutions on how to modify them using the tool (supplementary file 3: Dataset), and a video explaining the tool's main functionalities are available as supplementary materials (supplementary file 4: Video).

3.3 Use of the multi-parametric tool in a H2020 PANDEM-2 functional exercise (FX):

The FX organisers provided an initial dataset that tracked the evolution of case numbers over time. The multi-parametric tool was used to produce a numeric and graphic representations of the number cases over time. Additionally, a new variable was introduced to the dataset, containing the name of the variant. This variable contained two levels, which corresponded to the names of the previous (referred to in the FX as Alpha) and new (referred to in the FX as Gamma) strains that emerged in December according to the timeline of the pandemic scenario (Fig. 4).

Considering that integration of HTS-driven pathogen genomic database and patient contextual metadata is not yet a reality, having a tool that can simulate this combination in a realistic manner is essential for enhancing training exercises and tabletop simulations. In that respect, the development and implementation of the multi-parametric simulation tool presented in this study represents a significant step forward in improving training procedures for pandemic preparedness and response. The tool has a wide range of potential applications for training emergency responders and crisis managers to handle various public health crises caused by infectious agents, whether spread naturally, accidentally or intentionally.

The tool's ability to enrich existing datasets and generate new variables through data-driven or random simulations provides useful insights and practical applications across different crisis scenarios, including tabletop and functional exercises (11). Its capacity to connect previously unconnected databases greatly improves the realism of training simulations, which is essential for effective learning.

The FX conducted as part of the H2020 PANDEM-2 project demonstrated the effectiveness and adaptability of the simulation tool. The tool successfully simulated the evolution of the number of cases during an influenza pandemic, as well as the emergence of a new strain on a specific date defined by the scenario. This underscores its applicability to a wide range of infectious agents and scenario-driven genetic evolution, such as strains or variants.

The flexibility of the simulation tool proved to be an asset for evaluating response strategies of two interacting Public Health Emergency Operation Centres and assessing their preparedness levels. The multi-parametric tool showcased its value as a resource for FX and public health professionals seeking to improve their crisis management skills.

Furthermore, the seamless flow of the training session, which was evaluated by the 20 participants, highlighted the user-friendliness and accessibility of the multi-parametric Shiny application. The combination of a brief video presentation and a hands-on training session allowed trainees to explore provided databases and generate data as needed for the three proposed scenarios, leading to a thorough understanding of the tool’s functionalities and capabilities. The success of this training session emphasises the simulator’s effectiveness in educating a wide range of decision-makers including public health officials, health care professionals, researchers, and policymakers.

The integration of the multi-parametric simulation tool with the R programming language, encapsulated in the Pandem2simulator package, highlights its potential for broader application and adoption within the scientific community.

However, alongside significant achievements, certain limitations must be acknowledged. Notably, while the tool generates rich datasets, it is important to emphasise that these datasets are fictional and do not carry the same value as authentic, real-world data. A key challenge remains in collecting real patient metadata related to pathogen genomic data and integrating them into properly structured databases. To facilitate data sharing between countries and institutions, future efforts should address privacy, regulatory and standardisation concerns, as well as improve data management policies (18).

Additionally, regular updates and improvements to the simulator's functionalities and user interface would further increase its usability and relevance. Despite these limitations, the learning experience provided by the simulator significantly contributes to improving training in the field of health emergency preparedness and response, enabling key personnel to effectively manage pandemic responses.

As a future perspective, another application of the simulation tool could involve the incorporation of patient genomic data, specifically focusing on genetic factors that predispose individuals to severe outcomes following an infectious disease. By integrating this third layer of data, the simulator could more comprehensively model how both pathogen genetics and host genetics interact, expanding its capacity to address individual and population-level risks during a pandemic. This addition would further enhance the tool's capacity to simulate more personalised crisis scenarios, complementing the pathogen

genomic data and patient contextual metadata already used, and providing a more robust framework for pandemic preparedness and targeted public health interventions.

In conclusion, the multi-parametric simulation tool developed as part of the H2020 PANDEM-2 project offers a powerful and versatile resource for policymakers, public health officials, and practitioners to better prepare for various crisis situations. By combining pathogen genomic data with patient epidemiological, clinical, and biological metadata, the simulator is a first step toward bridging the gap between two types of currently unconnected, but inherently complementary, data, namely the causative pathogens and the infected patients. While other pandemic simulation tools have been reported (19–22), they address different aspects of pandemic requirements. Some focus on viral genealogy (19), while others examine healthcare resource availability and the impact of shortages on public health (20), and COVID-19 symptomatic case projections (22). The multi-parametric tool stands out for its ability to simulate a wide range of crisis scenarios and its adaptability for content and layout enhancements. This flexibility makes it an asset for generating inputs

needed for pandemic training and, more broadly, in preparing for public health crises related to the spread of infectious agents regardless of their origin.

ECDC

European Centre for Disease Prevention and Control EU:European Union

Functional exercise

HTS

High-throughput sequencing KNN:K-Nearest Neighbours

NGS

next-generation sequencing RR:Relative risk

VOC

Variant of concern

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

Author Contributions

Conceptualisation, J.A., A.S., F.O. and J-L.G.; Methodology, J.A., MB., J.H, F.O., A.S. and J-L.G.;

Validation, J.A., MB. and J-L.G.; Formal Analysis, J.A., M.B., F.O.; Investigation, J.A., MB. and J-L.G.; Resources, J-L.G.; Data Curation, J.A., M.B., J.H. and F.O.; Writing – Original Draft Preparation, M.B., J.A., and J-L.G.; Writing – Review & Editing, J.A., F.O., M.C. and J-L.G.; Visualisation, J.A. and J-L.G.; Supervision, J-L.G.C.; Project Administration, J-L.G.; Funding Acquisition, J-L.G. and M.C.”

Acknowledgments

We express our sincere gratitude to the entire H2020 PANDEM-2 technological team for their invaluable contributions to the development and implementation of the integrated dashboard featuring the multi-parametric simulator. Specifically, we would like to extend our appreciation to the following individuals for their dedicated efforts: C. Tighe, O; Zayed, C. Green, A. Ortiz, J. Albert, H. Chen (University of Galway, Ireland); G. Pacurar, B. Bucura, M. Enache (Clarisoft, Bucarest, Romania);

C. Houareau (Robert Koch-Institut, Berlin, Germany); B. Kaluza, S. Römer (Fraunhofer, Munchen, Germany); L. Ngongalah (Trilateral Research LTD, London, United Kingdom).

Funding

The project received funding from the European Union’s Horizon 2020 Research and Innovation programme under the Grant Agreement No. 883285. The material presented and views expressed here are the responsibility of the author(s) only. The EU Commission takes no responsibility for any use made of the information set out.

Data Availability Statement

The applications presented in this study are open access. The link is provided in the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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supplementaryfile2QuestionnaireS1.pdf
supplementaryfile3Dataset.zip
supplementaryfile4Video.mp4
Fig.1Supplementary.png
Data-driven and/or random simulations are employed to introduce an additional variable into the initial dataset using the multi-parametric simulation tool. Legend: These two types of simulation serve to enrich the initial dataset presented in the left panels, depicting distinct pandemic scenarios—SARS-CoV-2 in the upper panel and other pathogens (such as bacteria) or antimicrobial resistance (AMR) in the lower panel. The upper left panel illustrates the SARS-CoV-2 time-series variant dataset provided by primary databases. The NGS dataset from ECDC and NCBI provides variables such as ‘variant’ in relation to ‘time’ and ‘country’. Information about other mutations and genome wide association studies (GWAS) can be found only in NCBI databases. By using common variables like ‘country’ and ‘time’, a data-driven simulation establishes a connection between the ‘variant’ variable and the contextual data as ‘age’ of the population group in which this variant is identified. if the contextual data ‘diseases outcome’ or ‘comorbidities’ are not accessible, linking them to the ‘variant’ variable would necessitate a random simulation. The Lower left panel illustrates a time-series dataset representing the evolution of other pathogens, such as bacteria and antimicrobial resistance (AMR) genes. This data is sourced from the NCBI database. Using a similar approach as described above, both data-driven and random simulations are employed to establish connections between variables such as ‘virulence’, ‘resistance’, ‘presence of resistance plasmids’, ‘single nucleotide variant (SNV)’ or of ‘insertion-deletion (Indels)’.

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Integrating Patient Metadata and Pathogen Genomic Data: Advancing Pandemic Preparedness with a Multi-Parametric Simulator

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Abstract

Figures

1. Introduction

2. Materials and methods

2.1 Methodological Steps

2.1.1 Step 1: Addition of a new variable

2.1.2 Step 2: Modifying a relative risk

2.2 Training session to teach non-expert trainees how to use the multi-parametric tool

2.3 Use of the multi-parametric tool in a functional exercise (FX)

3. Results

3.1 Shiny Application overview:

3.2 Training session:

3.3 Use of the multi-parametric tool in a H2020 PANDEM-2 functional exercise (FX):

4. Discussion

Abbreviations

Declarations

Ethics approval and consent to participate

Consent for publication

Author Contributions

Acknowledgments

Funding

Data Availability Statement

Conflicts of Interest

References

Additional Declarations

Supplementary Files

Status:

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