<figure><img src="image_1.png" /></figure>

# Global Burden of Animal Diseases informatics strategy, data quality and model interoperability

K. Raymond* (1, 2), K.E. Sobkowich (1, 3), J.D. Phillips (1, 2, 3), L. Nguyen (1, 2), I. McKechnie (1, 2), R.N. Mohideen (1, 2), W. Fitzjohn (1, 2), M. Szurkowski (1, 2), J. Davidson (1, 2), J. Rushton (1, 4), D.A. Stacey (1, 2) & T.M. Bernardo (1, 3)

## Summary

The estimation of the global burden of animal diseases requires the integration of multidisciplinary models: economic, statistical, mathematical and conceptual. The output of one model often serves as input for another; therefore, consistency of the model components is critical. The Global Burden of Animal Diseases (GBADs) Informatics team aims to strengthen the scientific foundations of modelling by creating tools that address challenges related to reproducibility, as well as model, data and metadata interoperability. Aligning with these aims, several tools are under development.

- GBADs' Trusted Animal Information Portal (TAIL) is a data acquisition platform that enhances the discoverability of data and literature and improves the user experience of acquiring data. TAIL leverages advanced semantic enrichment techniques (natural language processing and ontologies) and graph databases to provide users with a comprehensive repository of livestock data and literature resources.

- The interoperability of GBADs' models is being improved through the development of an R-based modelling package and standardisation of parameter formats. This initiative aims to foster reproducibility, facilitate data sharing and enable seamless collaboration among stakeholders.

- The GBADs Knowledge Engine is being built to foster an inclusive and dynamic user community by offering data in multiple formats and providing user-friendly mechanisms to garner feedback from the community.

These initiatives are critical in addressing complex challenges in animal health and underscore the importance of combining scientific rigour with user-friendly interfaces to empower global efforts in safeguarding animal populations and public health.

## Keywords

Data management – Data quality – Data science – GBADs – Global Burden of Animal Diseases – Interoperability – Reproducibility.

## Introduction

Advances in scientific modelling have bolstered researchers' understanding of the past and ability to predict future outcomes. Models, simplified images or representations of reality, while not perfect, can improve understanding of complex

problems, such as climate change, population dynamics or the spread of a novel virus. In his book *How to Prevent the Next Pandemic*, Bill Gates says, 'For me, one of COVID's chief lessons about modelling is the extent to which every model relies on good data, and just how hard it can be to get that data' [1].

<footnote>(1) Global Burden of Animal Diseases (GBADs) Programme, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, 146 Brownlow Hill, Liverpool, L3 5RF, United Kingdom (https://animalhealthmetrics.org)</footnote>

<footnote>(2) School of Computer Science, University of Guelph, Reynolds Building, 474 Gordon Street, Guelph, ON N1G 2W1, Canada</footnote>

<footnote>(3) Department of Population Medicine, Ontario Veterinary College, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, Canada</footnote>

<footnote>(4) Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, 146 Brownlow Hill, Liverpool, L3 5RF, United Kingdom</footnote>

<footnote>*Corresponding author: kraymond@uoguelph.ca</footnote>
The estimation of the global burden of animal diseases incorporates several models (economic, statistical, mathematical and conceptual). These models use data and parameters from diverse sources to create outputs, where the output of one model may serve as input for another. Hence, there is a need for interoperability in parameters and data used across the various models. For example, the estimation of the number of animals at a given point or over a period of time is required for calculating both biomass and total economic value.

With the overarching goal of enhancing the discovery and interoperability of data, parameters and models used and created by Global Burden of Animal Diseases (GBADs) modellers and stakeholders, the GBADs information strategy consists of digital informatics tools designed to support the following core objectives:

- make it easier to access data;

- ensure downloaded data are available in a usable format that works with modelling tools that GBADs designed;

- improve the robustness, usability and modularity of modelling tools;

- allow members of the community to provide feedback on the functionality, usability and impact of the system.

These objectives are aligned with key findings highlighted in a review by Antle et al. [2], which underscores the importance of improving agricultural data and models through fostering interoperability among models, linking them to data and knowledge products, and implementing cloud-based data management and retrieval systems. Other initiatives such as 50×2030¹ and CGAIR's GARDIAN platform<fnref n="2" /> provide or index agricultural data that may be of use as inputs to GBADs models. GBADs builds upon the broader landscape of these initiatives by aiming to improve the quality and interoperability of models, data and parameters and facilitating the retrieval and management of these resources.

The GBADs Informatics team, with expertise in veterinary medicine, epidemiology, and computer and data science, conducted a survey to gather insights from successful organisations that operate and sustain global health data-gathering systems [3]. Based on these findings, a key recommendation was to spend more time and resources upfront in creating robust and data systems for the future. In line with this advice, cutting-edge technologies are being used to ensure the GBADs system is scalable and future-proof. This includes incorporating ontologies for

more effective searching, mapping relationships using a graph database, and incorporating proven machine learning and natural language processing (NLP) tools into the data-gathering system.

This article aims to present the approaches and tools that support each of the four main objectives, with the ultimate goal of improving usability, interoperability, reusability and consistency across GBADs models, data and dashboards. First, it introduces the Trusted Animal Information Portal (TAIL), a system designed to enhance the interoperability, discoverability and access to data and literature within the GBADs Knowledge Engine. Next, it describes the GBADs approach for improving the interoperability, reusability and reproducibility of models and their outputs through the modularisation of code and standardisation of parameters. Finally, it outlines the various tools being developed to gather feedback, engage stakeholders and quickly incorporate feedback into dashboards. Table I provides a comprehensive overview of all tasks and progress related to each tool discussed.

## Improved understanding and streamlined acquisition of livestock data

Livestock data, crucial for informed decision-making and policy formulation, exist in heterogeneous formats, originate from disparate sources and use inconsistent terminology for characterising species, often hindering the ability to attain the guiding principles of findability, accessibility, interoperability and reusability (FAIR) [4]. To address these issues, GBADs is developing TAIL (Fig. 1), a novel system designed to facilitate the acquisition and interoperability of livestock data and literature from diverse sources. This section describes the preliminary system architecture, placing emphasis on the system's ability to reconcile data and metadata inconsistencies and to enhance semantic search of data beyond traditional keyword queries.

## The Trusted Animal Information Portal: discovering metadata and literature

Consider a user who is interested in finding cattle population data. The user enters the GBADs TAIL and enters a free-text query such as 'cow population in Canada'. The query is sent to GloVe (Global Vectors for Word Representation), NLP software from Stanford University, which recognises terms in the query [5]. The term that is recognised as the species is sent to GBADs' ontology, which enriches the term with semantically related terms such as 'cattle', 'bovine' and 'dairy'. The user can then select which of the additional terms to include, resulting in a 'semantically enriched query'.

<footnote n="1">1. https://www.50x2030.org/</footnote>

<footnote n="2">2. https://gardian.bigdata.cgiar.org/#</footnote>
<caption>Table I</caption>

Progress towards tasks involved in the development of Global Burden of Animal Diseases data, modelling and visualisation tools

<table><thead><tr><th>Tool</th><th>Task</th><th>Description</th><th>Status</th></tr></thead><tbody><tr><td rowspan="10">GBADs TAIL</td><td>Develop graph model</td><td>Design the structure (nodes, properties and relationships) to store and represent metadata that will allow searching on the TAIL interface</td><td>Done</td></tr><tr><td>Populate graph database</td><td>Continuously add information to TAIL by preparing and loading metadata from various data sources. This ongoing process helps expand and increase the number of datasets that are findable through TAIL. Expand and increase the number of datasets that are findable through TAIL by continuously adding metadata from various data sources</td><td>Ongoing</td></tr><tr><td>Develop ontology</td><td>Create a formal representation of knowledge domain, defining entities and relationships. This involves unique definitions for species classifications and the connections between them based on the way terms are used in the data, rather than trying to impose a top-down approach</td><td>Ongoing</td></tr><tr><td>Develop user interface</td><td>Design and implement a user-friendly interface that allows users to search, access and download data and literature. Development includes integrating with ontology, the graph database and Semantic Scholar's API, as well as soliciting feedback for continual improvements</td><td>In development</td></tr><tr><td>Develop software development kits, middleware, code and modules</td><td>Create tools that enable different parts of TAIL to work together</td><td>In development</td></tr><tr><td>Develop API to interact with the graph database</td><td>An API has been developed to acquire information from the graph database</td><td>Ongoing</td></tr><tr><td>Develop API to interact with the ontology</td><td>Create an API to interact with the ontology and enhance the functionality of TAIL</td><td>In development</td></tr><tr><td>Develop tests</td><td>Plan and create tests to ensure the TAIL application works well. This includes checking search accuracy, user interface usability and overall performance</td><td>Not started</td></tr><tr><td rowspan="2">Data quality visualisation dashboard (Fig. 3)</td><td>Develop dashboard</td><td>Develop a data visualisation tool in Python Dash that allows users to investigate external and internal consistencies in livestock population datasets from the World Organisation for Animal Health, FAOSTAT, and national agricultural censuses and surveys</td><td>Done</td></tr><tr><td>Deploy dashboard</td><td>The dashboard is put into a software container, is deployed, and is then available via a URL</td><td>Done</td></tr><tr><td rowspan="3">GBADs R modelling package</td><td>Refactor and modularise the DPM R code</td><td>Improve the DPM R code by organising it into clear functions and structuring it for consistent use of parameters for robust calculation of the Animal Health Loss Envelope</td><td>In development</td></tr><tr><td>Prepare documentation for CRAN</td><td>Create detailed documentation for the R package, in preparation for submission to CRAN. This documentation will allow users to understand how to use the package effectively</td><td>In development</td></tr><tr><td>Create and run tests</td><td>Create a robust set of tests to ensure that the package is reliable and accurate and that code can be validated</td><td>Not started</td></tr><tr><td rowspan="2">Standards for GBADs parameters</td><td>Create a dictionary of parameters</td><td>A dictionary of parameters is being developed to collect the names, descriptions, codes and biological maximums and minimums of each parameter, for each model</td><td>Ongoing</td></tr><tr><td>Establish a template YAML file to ensure interoperability of parameters</td><td>Use the dictionary of parameters to create standard templates for configuration of parameters for the DPM. This standard format, coupled with the GBADs R package, will enable the reuse of model configurations, promoting reusability and transparency of GBADs modelling outputs</td><td>In development</td></tr></tbody></table>
<table><caption>Table I (cont.)</caption><thead><tr><th>Tool</th><th>Task</th><th>Description</th><th>Status</th></tr></thead><tbody><tr><td rowspan="3">Ethiopia<br>Sub-National<br>Dashboard<br>Development</td><td>Develop dashboard</td><td>Design the layout of the dashboard with input from the end users. Decide on types of data visualisations, prototype (creating wireframes to visualise the initial design) and build the dashboard in Python Dash</td><td>Done</td></tr><tr><td>Deploy dashboard</td><td>The dashboard is put into a software container and, once deployed, is available via a URL (https://gbadske.org/dashboards/ethiopia-population)</td><td>Done</td></tr><tr><td>Gather feedback from users<br>Implement the feedback</td><td>User feedback on the usability of dashboards was gathered during two stakeholder workshops at the International Livestock Research Institute. The feedback was used to improve the design and functionality of the dashboard (i.e. aesthetic changes, improved usability and functionality, improvements to speed) and will continue to be incorporated in future developments</td><td>Ongoing</td></tr><tr><td rowspan="2">Comments<br>feature in<br>dashboards</td><td>Develop a comment<br>feature in dashboards</td><td>Develop code that allows users to share thoughts, comments and suggestions directly within the dashboard and hence with the developers. This allows users such as modellers or government officials to easily provide feedback for incorporation into subsequent versions</td><td>Done</td></tr><tr><td>Develop a mechanism<br>to send comments to<br>Slack for approval</td><td>To protect against inappropriate use, the comments are sent to Slack (a software communications tool) for approval. This task involves developing and implementing a tool called a 'bot' to allow individuals to review and either approve or deny comments, ensuring a moderated and appropriate environment</td><td>Done</td></tr><tr><td rowspan="4">GBADs usage<br>statistics<br>dashboard<br>(Fig. 4)</td><td>Set up data tracker</td><td>Configure a system to keep track of the cities and countries from which users are accessing the dashboard. Decide how to measure this, where to save this information, in what format and where to save this information in AWS</td><td>Done</td></tr><tr><td>Enable dashboard<br>access</td><td>Develop code that allows the dashboard to securely access data from AWS. The data is anonymised and accessible only to authorised developers</td><td>Done</td></tr><tr><td>Create and design<br>dashboard</td><td>Plan how the dashboard will look, choose what information to show and the data visualisations that are used, and develop the dashboard in Python Dash</td><td>Done</td></tr><tr><td>Share dashboard</td><td>The dashboard is put into a software container and, once deployed, is available via a URL (https://gbadske.org/dashboards/users)</td><td>Done</td></tr><tr><td rowspan="2">Verification<br>and validation</td><td>Accessibility testing</td><td>All interfaces (websites, dashboards, etc.) must meet accessibility standards set by AODA. AODA is the standard in the Canadian province housing the GBADs Informatics team</td><td>Ongoing</td></tr><tr><td>Unit and integration<br>testing</td><td>All code (currently Python and R) will undergo a set of standardised tests using best practices for the language</td><td>Planning is ongoing</td></tr></tbody></table>

AODA: Accessibility for Ontarians with Disabilities Act

API: application programming interface

AWS: Amazon Web Services

CRAN: Comprehensive R Archive Network

DPM: Dynamic Population Model

FAOSTAT: Food and Agriculture Organization of the United Nations Corporate Statistical Database

GBADs: Global Burden of Animal Diseases

TAIL: Trusted Animal Information Portal

URL: uniform resource locator

YAML: Yet Another Markup Language

The semantically enriched query is sent to a graph database, which holds metadata for all data that have been catalogued by GBADs. By matching the terms to those connected to metadata in the graph database, a catalogue of relevant metadata is provided to the user, allowing the user to browse for datasets relevant to their search. In parallel, the augmented query is sent to Semantic Scholar, returning literature related to the user's search [6,7].

Finally, the information is displayed on the GBADs TAIL interface, providing the user with a catalogue of data and literature resources related to their search. The front-end interface is being built with React, a popular framework for dynamic interface development, and will eventually integrate knowledge about GBADs models into the portal, creating a truly contiguous network of accessible data resources [8].
GBADs
TAIL

<figure><img src="image_2.png" /></figure>

**GBADs:** Global Burden of Animal Diseases
**NLP:** natural language processing
**TAIL:** Trusted Animal Information Portal
**WOAH:** World Organisation for Animal Health

<caption>Figure 1</caption>

**Schematic representation of Global Burden of Animal Diseases Trusted Animal Information Portal operational workflow**

Terms from a user's free-text query are recognised by NLP, and species terms are enriched with semantically related terms using an ontology.

The user then refines their search by selecting related terms, resulting in an enriched search. The enriched search is sent to the graph database and Semantic Scholar, and relevant metadata is presented to the user
Table I outlines the progress made in the ongoing development of the GBADs TAIL interface. Livestock population data from the World Organisation for Animal Health (WOAH), Eurostat, the Food and Agriculture Organization of the United Nations Corporate Statistical Database (FAOSTAT) and the Ethiopia Central Statistics Agency are being made accessible in the primary build of the system.

## The technical underpinnings

A sophisticated back-end system aims to reconcile the inconsistencies in standardisation, terminology and content of livestock data and metadata. Using a graph database and ontologies, metadata and data are semantically enriched and made interoperable, allowing for efficient data and literature searches.

### Graph database metadata catalogue

The graph database that is employed by the system stores metadata for each of the datasets catalogued by GBADs. Metadata are defined broadly as data about data, providing information such as descriptions, temporal and spatial coverage, licensing and ownership information, date of last update and access instructions [9]. To streamline the efficient discovery and accessibility of data, all metadata are reconciled into a standard format (https://schema.org) and stored in a graph database (Fig. 2) [10]. For example, a user

interested in data related to a particular country and species can query the graph database to retrieve all related datasets and their related metadata.

### Ontologies

Recognising that datasets are characterised by heterogeneous terminology, searching for data based on explicit keywords fails to retrieve all relevant datasets. Explicit keyword search assumes that users know precisely what they are looking for and does not retrieve semantically similar terms. For instance, in many other data portals, a user searching for data using the keyword 'cattle' would not also retrieve data about 'dairy cattle', 'cows', 'calves' and 'bovine'. GBADs' TAIL employs a bottom-up approach and uses species terms found in data sources that may have varied meanings. Ontologies capture the semantics of data, allowing semantically similar, albeit heterogeneously described terms to be identified.

In the system, ontologies provide vocabularies to describe datasets and help to establish clear and shared definitions of concepts and their associated relationships. By identifying shared definitions and mapping similar concepts, ontologies can be operationalised to enable users to enrich their query with similar terms that may be defined differently. This relieves users from the time-consuming, and error-prone, task of manually seeking similar terms and their respective definitions.

<figure><img src="image_3.png" /><figcaption>**Figure 2**</figcaption></figure>

**Example of the graph database model that stores metadata for a dataset**

The graph database stores all GBADs metadata and connects data according to commonly reported species and countries, allowing users or machines to query for all datasets related to livestock species and countries of interest
Once similar terms are identified, these terms can be used to search for resources that are described using different, yet semantically similar, terms. This process yields relevant results that were previously in silos due to heterogeneity in characterisation, ultimately leading to more 'FAIR' data.

## Data quality: internal and external consistency

Beyond data acquisition, assessing data quality is a fundamental task. Data quality is a concept that encompasses a number of dimensions, including internal and external consistency [11,12]. External consistency refers to the synchronicity of two datasets reporting the same measurements [11]. For instance, comparing livestock population data reported by FAOSTAT and a national statistical organisation offers an indicator of consistency.

To facilitate data quality assessments and reproducibility, a data visualisation tool was developed (Fig. 3). This tool allows users to investigate and identify inconsistencies in livestock population datasets from WOAH, FAOSTAT and national agricultural censuses and surveys. By cross-referencing data between countries and species and over time, probable inconsistencies between and within datasets have been illustrated [15]. Consequently, datasets can be fully annotated, allowing inconsistencies to be communicated and providing users with an indicator of quality. This systematic approach

to data quality reduces the need for data quality to be assessed independently by each modeller, ultimately improving the reproducibility of modelling efforts in GBADs.

## Interoperability of models

At the core of GBADs are a set of interconnected animal health and economics models and calculations used for deriving metrics, such as the Dynamic Population Model (DPM). Briefly, the DPM is a novel, compartmentalised equation-based model that collates information from various data sources (e.g. milk, meat, waste) and at various animal life stages (e.g. neonate, juvenile, adult) to estimate monetary losses. Specifically, output from the DPM is employed to calculate the Animal Health Loss Envelope (AHLE), which is the difference between ideal and current scenarios.

Often, models are coded in programming languages such as R and use data and parameters. From these components, output data are produced, and these data – such as the economic cost of a particular disease on a production system – can inform policy decisions. The DPM includes several calculations such as biomass and economic value. By decomposing the model into pieces (modularising the code), components of the model can become interoperable (i.e. they can be exchanged seamlessly between different programmes). The implications of interoperable models are two-fold. Firstly, individual components

<figure><img src="image_4.png" /></figure>

FAOSTAT: Food and Agriculture Organization of the United Nations Corporate Statistical Database

GBADs: Global Burden of Animal Diseases

IQR: Interquartile Range

WOAH: World Organisation for Animal Health

### Figure 3

#### The Global Burden of Animal Diseases Data Quality Visualiser Tool showing the population of cattle in Ethiopia

The tool has five options: General Data Viewer, Polynomial Regression, Five-Year Population Average, Growth Rates and Interquartile Range. Each tool tab allows the user to visualise data in unique ways to spot inconsistencies. Data from WOAH, the FAOSTAT crops and livestock products (Live animals: QCL) dataset, and census data [13,14] are visualised. Note that there is only one year of census data and therefore it appears as a single green dot
of the model can be reused in analyses beyond those imagined for the original task. Secondly, models can be rerun using updated data or parameters without having to build the model from scratch, leading to reproducible and replicable methods [16].

The interoperability of models is therefore essential for the uptake of GBADs methods and to validate and verify those methods. To facilitate interoperability of models, a package is being developed using R, and the format and naming conventions of parameters used for modelling are being standardised. To this end, the GBADs Informatics team envisions the development of a user-friendly analysis tool that will permit the models (encompassing both parameters and data) to be chained together in an exchangeable and automatic fashion. The ultimate goal is to improve data-sharing capabilities among academics, scientists, health professionals, stakeholders and policy-makers within the larger purview of an international One Health datasphere [17-19]. In addition, the interoperable design of the modelling pipeline provides an opportunity to use the outputs for a variety of analytical treatments, including sensitivity analysis.

## R package for Global Burden of Animal Diseases models

For several reasons, R was chosen for the modularisation of code, which will result in a GBADs R package (i.e. an ensemble of interoperable modelling components). R is a free, open-source software programme encouraging the dissemination of collaborative knowledge and research findings through its extensive ecosystem of user-contributed packages [20]. R is supported by a large user base, including its own online journal and academic conference, and R packages can be created and deployed by the user community through submission to the Comprehensive R Archive Network (CRAN) [21]. CRAN enforces rigorous standards and policies, with packages checked daily to ensure operability across multiple major operating systems. Once packages are submitted to CRAN, any R user can download and use the package freely for their own work, encouraging collaboration. To this end, an R package invites other researchers to use GBADs models and their components, allowing GBADs methods to be adopted by those beyond the immediate GBADs community.

## Interoperability of parameters and modelling outputs

To ensure that parameters can be understood and reused among models (or components of models), standard formats and naming conventions must be used. In addition, standardised output formats ensure that results from one model can be used as inputs to others (thus allowing for chaining of models). Work is under way to collect input parameters from existing models, such as AHLE, and to solicit other

parameters that may be useful in future models. This work will provide a dictionary of commonly understood parameters, allowing for a common understanding and standardised naming of parameters used in GBADs models. Another goal is to develop a format for parameters in YAML (Yet Another Markup Language), a markup language that is human and machine-readable and is often used for writing configuration files [22]. To facilitate the largest number of users, parameters described in CSV or JSON files will be readily translatable by GBADs' shared input packages to the standardised YAML format for use in the model. This standardisation work will facilitate the collection and documentation of model parameters that can be widely shared. It will also generate a wealth of valuable metadata that can serve as an authoritative health informatics resource for GBADs personnel and other interested parties.

## Capacity and community building

Community building among all GBADs stakeholders is an important aspect of the Knowledge Engine. The first dimension of community engagement is the philosophy of offering data in multiple formats and from multiple access points (application programming interface, file downloads of CSV files from cloud storage, GBADs' TAIL, dashboards). The Knowledge Engine strives to facilitate, not dictate, how users work with data and models – through spreadsheets, R, Python and data analysis software. While this approach adds a level of complexity to the management of the system, automation techniques readily available in cloud repositories and systems allow GBADs to provide this level of accessibility and diversity.

Another dimension of community engagement is the concept of constant feedback and improvement. One example of this is the Ethiopia-related dashboards that have been incrementally improved based on feedback generated by local users at two in-person meetings in Ethiopia [23,24]. Response to feedback has been facilitated by the technological deployment of the dashboards via software containers and an elastic (i.e. dynamic) container deployment system in conjunction with storage of the software in cloud-based repositories (GitHub) that are engineered to immediately update the dashboards as soon as changes are made in the code [25]. This allows GBADs software developers to respond immediately to feedback so that users can see if their feedback has been properly interpreted. This ability greatly enhances the utility of in-person meetings with stakeholders, since they can see their insights being immediately integrated into the system.

An extension of this philosophy, which allows feedback to be collected continuously and from all users, is the new integration of dashboards with Slack (a software communications tool) channels used by the GBADs Informatics team to allow feedback from each dashboard to be directed to both the community at large and appropriate GBADs staff for action [26]. All dashboards will be provided with a 'Comments'
feature allowing users to submit feedback about the dashboard (and the specific data being viewed) that will be moderated, stored and displayed (Table I provides high-level tasks associated with this feature). Once the feedback has been reviewed (to protect the system from inappropriate use) and approved on Slack, the feature seamlessly updates the dashboard, making it visible to the public and creating a continuous community engagement system. All feedback will be stored in a database to allow for the development of analysis programmes to detect patterns that will help in the further improvement of the dashboards based on user interactions. This system is currently in beta testing and its full launch is anticipated for year-end 2024.

Community members need to feel that they are connecting to others, and one aspect of this is to know who those others are and what they are doing with the Knowledge Engine. All interactions with the system are being monitored so that GBADs can provide current and longitudinal data about where its community members are and what software tools they are using. These data are accessible by all community members through a dashboard (Fig. 4) that displays the location of users (resolved to only city and country to preserve privacy) and the popularity of tools such as the dashboards. Users can observe the reach of GBADs data, tools and analysis and can be inspired to try some of the tools they are not currently using.

In an analogous manner, developers in the area of animal health and economics can have access to all code driving the Knowledge Engine and the GBADs models and tools. All GBADs code is open source, and developers outside the GBADs Informatics team are encouraged to use any code available through the team's GitHub (https://github.com/GBADsInformatics) to develop their own tools, as well as to report bugs and feature requests. A community of developers can greatly add to the impact of the Knowledge Engine and welcome a diversity of voices into the GBADs tool development.

## Conclusions

GBADs' methods and data will reach a broader audience due to a) the development of GBADs' TAIL, b) advances in the standardisation of GBADs' modelling practices and c) the provision of tools to improve community and capacity building. The GBADs tools make data and models more in line with the FAIR guiding principles, facilitating reproducible outcomes and fostering trust in outputs. GBADs' TAIL will be refined and improved based on user feedback, will incorporate new languages and will evolve along with advancements in search capability, NLP and machine learning. The Informatics team strives to offer better data, for better models and for better decision-making in livestock production. This approach can be adapted to address other

<figure><img src="image_5.png" /></figure>

GBADs
informatics

Where our users are

The Global Burden for Animal Diseases (GBADs) dashboards have had 4152 visits from 69 unique countries over 7 months.

Our most popular dashboard is Animal Health Loss Envelope which has had 1479 total views.

GBADs currently offers 14 unique dashboards.

<figure><img src="image_6.png" /></figure>

API: application programming interface

GBADs: Global Burden of Animal Diseases

## Figure 4

### Global Burden of Animal Diseases Informatics Usage Statistics dashboard

The dashboard shows where users are and the number of views on each GBADs Informatics resource, such as dashboards and APIs
complex problems by making advanced technologies more
user-friendly and globally applicable.

## Acknowledgements

This article was written in the context of the second phase of
the Global Burden of Animal Diseases (GBADs) programme,
which was co-led by the University of Liverpool and the
World Organisation for Animal Health (WOAH). The research
is supported through Grant Agreement Investment ID INV-
005366 with the Bill and Melinda Gates Foundation and the
United Kingdom Foreign, Commonwealth and Development
Office. GBADs case studies receive additional funding

from the European Commission, the Australian Centre for
International Agricultural Research, the Brooke Foundation
and the Food and Agriculture Organization of the United
Nations. A full list of GBADs collaborators can be accessed at:
https://animalhealthmetrics.org/acknowledgements.

GBADs Informatics lost its co-leader, Dr Theresa Bernardo, on 13 March 2024 after a brief battle with ALS/Motor neurone disease. Theresa made numerous contributions in the fields of international veterinary medicine and health/veterinary informatics. She was an inspirational leader and mentor for everyone in GBADs Informatics.

# Stratégie informatique du programme « Impact mondial des maladies animales », qualité des données et interoperabilité des modèles

K. Raymond, K.E. Sobkowich, J.D. Phillips, L. Nguyen, I. McKechnie, R.N. Mohideen, W. Fitzjohn, M. Szurkowski, J. Davidson, J. Rushton, D.A. Stacey & T.M. Bernardo

## Résumé

L'estimation de l'impact mondial des maladies animales nécessite l'utilisation intégrée de modèles issus de diverses disciplines : économiques, statistiques, mathématiques et conceptuels. Les données de sortie d'un modèle constituent souvent celles d'entrée d'un autre modèle ; la cohérence des composantes des différents modèles est donc primordiale. L'équipe informatique du programme « Impact mondial des maladies animales » (GBADs) s'efforce de consolider les bases scientifiques de l'utilisation des modèles en mettant au point des outils permettant de résoudre les problèmes de reproductibilité et d'améliorer l'interopérabilité entre les différents modèles, données et métadonnées. En phase avec ces objectifs, plusieurs outils sont en cours de développement :

- le Portail du GBADs « Trusted Animal Information Portal » (TAIL) est une plateforme d'acquisition de données qui facilite l'accès aux données et à la littérature, tout en améliorant l'expérience utilisateur lors de l'acquisition des données. Le portail TAIL s'appuie sur des techniques avancées d'enrichissement sémantique (traitement du langage naturel et ontologies) et sur des bases de données graphiques pour apporter aux utilisateurs un référentiel complet des données et des ressources documentaires relatives aux animaux d'élevage ;

- l'interopérabilité des modèles du GBADs est en voie d'amélioration grâce à la mise au point d'un progiciel de modélisation fondé sur R et à la normalisation des formats de paramètres. Cette initiative vise à favoriser la reproductibilité, à faciliter le partage de données et à permettre une collaboration transparente entre les parties prenantes ;

- le moteur de connaissances du GBADs, en cours de construction, vise à encourager une communauté d'utilisateurs inclusive et dynamique en proposant des données dans une multiplicité de formats ainsi que des mécanismes conviviaux pour recueillir les commentaires de la communauté.

Ces initiatives se révéleront indispensables pour relever les défis complexes de la santé animale et soulignent l'importance d'associer une grande rigueur scientifique à la convivialité des interfaces, afin de donner encore plus d'élan aux efforts déployés dans le monde pour protéger les populations animales et la santé publique.

## Mots-clés

GBADs – Gestion des données – Impact mondial des maladies animales – Interopérabilité – Qualité des données – Reproductibilité – Science des données.
# Estrategia informática del programa sobre el Impacto Global de las Enfermedades Animales, calidad de los datos e interoperabilidad de los modelos

K. Raymond, K.E. Sobkowich, J.D. Phillips, L. Nguyen, I. McKechnie, R.N. Mohideen, W. Fitzjohn, M. Szurkowski, J. Davidson, J. Rushton, D.A. Stacey & T.M. Bernardo

## Resumen

La estimación del impacto global de las enfermedades animales requiere la integración de modelos multidisciplina-
rios: económicos, estadísticos, matemáticos y conceptuales. El resultado de un modelo a menudo sirve de entrada
para otro; por lo tanto, la coherencia entre los distintos componentes es fundamental. El equipo de informática del
programa sobre el Impacto Global de las Enfermedades Animales (GBADs) tiene como objetivo fortalecer los funda-
mentos científicos de la modelización mediante la creación de herramientas que aborden los retos relacionados con
la reproducibilidad, así como con la interoperabilidad de los modelos, datos y metadatos. En consonancia con estos
objetivos, se están desarrollando varias herramientas.

- El Portal del GBADs «Trusted Animal Information Portal» (TAIL) es una plataforma de adquisición de datos que mejora tanto la descubribilidad de datos y bibliografía como la experiencia del usuario a la hora de obtener datos. El portal TAIL utiliza técnicas avanzadas de enriquecimiento semántico (procesamiento del lenguaje natural y ontologías), así como bases de datos de grafos, para ofrecer a los usuarios un repositorio completo de datos sobre ganadería y recursos bibliográficos.

- Se está mejorando la interoperabilidad de los modelos del GBADs mediante el desarrollo de un paquete de modelización en R y la normalización de los formatos de los parámetros. Esta iniciativa pretende fomentar la reproducibilidad, facilitar el intercambio de datos y permitir una colaboración fluida entre las partes interesadas.

- El Motor de Conocimiento del GBADs se está construyendo con el objetivo de fomentar una comunidad de usuarios inclusiva y dinámica, ofreciendo datos en diferentes formatos y proporcionando mecanismos fáciles de usar para recopilar comentarios de la comunidad.

Estas iniciativas son fundamentales para hacer frente a los complejos retos en el ámbito de la sanidad animal y subra-
yan la importancia de combinar el rigor científico con interfaces fáciles de usar para potenciar los esfuerzos mundia-
les encaminados a proteger a las poblaciones animales y la salud pública.

## Palabras clave

Calidad de datos – Ciencia de datos – GBADs – Gestión de datos – Impacto Global de las Enfermedades Animales –
Interoperabilidad – Reproducibilidad.

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© 2024 Raymond K., Sobkowich K.E., Phillips J.D., Nguyen L., McKechnie I., Mohideen R.N., Fitzjohn W., Szurkowski M., Davidson J., Rushton J., Stacey D.A. & Bernardo T.M.; licensee the World Organisation for Animal Health. This is an open access article distributed under the terms of the Creative Commons Attribution IGO Licence (https://creativecommons.org/licenses/by/3.0/igo/legalcode), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. In any reproduction of this article there should not be any suggestion that WOAH or this article endorses any specific organisation, product or service. The use of the WOAH logo is not permitted. This notice should be preserved along with the article's original URL.