The research team set out to answer a deceptively simple question: how are quantitative modelling approaches being used to support One Health in the context of agri-food systems, and how could they be better organized within governmental institutions? The question matters because the food system sits at the center of some of the most pressing health challenges of the century, from zoonotic disease emergence and antimicrobial resistance to foodborne illness and the nutritional consequences of environmental change. Public health authorities, the authors argue, cannot prioritize interventions or evaluate trade-offs across these domains without quantitative models that capture the dynamics of the animal-human-environment interface. Intuition and siloed expertise, however sophisticated, are not enough when a single policy decision ripples simultaneously through livestock production, ecosystems, and human diets.

To establish what governments are actually doing, the researchers first defined an operational scope of One Health by examining the One Health Joint Plan of Action, the strategic framework developed by the quadripartite alliance of the World Health Organization, the Food and Agriculture Organization, the World Organization on Animal Health, and the United Nations Environmental Programme. They then cross-referenced the plan’s proposed food-system activities against the publicly available One Health resources of the Group of 20 countries, the economies with the greatest institutional capacity to act. The result was sobering. The activities that governments have actually implemented cluster tightly around coordination, interoperability, and data sharing, and they concentrate on a short list of established threats: antimicrobial resistance, zoonoses, and vector-borne diseases. Broader food-system concerns, including nutrition, environmental sustainability, and the structural dynamics of agricultural production, remain largely outside the institutional One Health agenda.

That institutional narrowness, however, contrasts sharply with what is happening in the research literature. The team conducted a PRISMA-compliant scoping review of One Health quantitative modelling with relevance to the food system, covering publications from 2021 to 2026. The search yielded 129 relevant articles, a body of work that reveals how far methodological ambition has outrun governmental practice. Beyond the epidemiological data that one might expect, these studies drew on agricultural, geospatial, and ecological data streams to address food-system questions. Researchers were combining crop and livestock statistics with satellite-derived land cover information, wildlife distribution data, and environmental indicators, weaving together evidence from domains that traditionally have never shared a analytical framework, let alone a database.

The methodological landscape that emerged from the review is strikingly heterogeneous, and the authors identify three broad strategies that dominate the field. The first involves models that ingest heterogeneous data, fusing inputs of different types and resolutions into a single analytical structure. The second relies on mixed-effects approaches, statistical designs that explicitly account for variation at multiple levels, whether that means differences between farms, regions, or ecological zones, and can therefore represent the nested structure of real food systems rather than flattening it. The third and perhaps most consequential strategy is model integration: coupling separate models, each specialized for one domain, into combined frameworks capable of scenario analysis. A disease transmission model might be linked to an agricultural production model and an environmental impact model, allowing analysts to ask what happens to human health, animal welfare, and ecosystems under different policy futures simultaneously.

This is where the technical heart of the paper lies, and it is worth pausing on why integrated modelling is so difficult and so valuable. Each component model carries its own assumptions, spatial and temporal scales, and data requirements. An epidemiological model of pathogen spread may operate on daily time steps across networks of farms and markets, while an ecological model of land-use change may work in yearly increments across entire watersheds. Reconciling these scales, propagating uncertainty across model boundaries, and ensuring that outputs from one model are meaningful inputs to another are nontrivial engineering and statistical challenges. The fact that 129 recent studies attempt this integration at all signals a maturing field, but the review also makes clear that the approaches remain fragmented, with little standardization in how interdisciplinary collaboration is structured or how data infrastructures support it.

The conceptual contribution of the paper is a modular framework designed to operationalize modelling within One Health initiatives that involve the food system. Rather than prescribing a single monolithic model, the framework treats modelling as a set of interoperable modules that institutions can assemble according to their needs, connecting data acquisition, domain-specific models, and scenario evaluation in a flexible architecture. The design is explicitly aimed at supporting interdisciplinary collaboration between food-systems research and One Health practice, providing a common structure through which epidemiologists, agricultural scientists, ecologists, and data specialists can contribute components without needing to master one another’s entire methodological repertoire. For governmental institutions, the appeal is practical: a modular approach lowers the barrier to entry and allows capacity to be built incrementally rather than requiring an impossible upfront investment in a fully integrated system.

Underpinning all of this is a data infrastructure problem that the authors place at the center of their conclusions. The review found that genomic, geospatial, and agricultural data are the raw material of interdisciplinary One Health modelling, and that the availability and interoperability of these data streams largely determines what models can achieve. Genomic surveillance data allow pathogens to be traced across species boundaries and supply chains, revealing transmission pathways that would otherwise remain invisible. Geospatial data connect disease events to environmental drivers such as land conversion, water systems, and climate variables. Agricultural data anchor the models in the economic and biological realities of food production. Yet these data types are held by different ministries, agencies, and research communities, often in incompatible formats and under different governance regimes. The coordination and data-sharing activities that dominate current G20 One Health initiatives are, in this light, a necessary foundation, but the review suggests they must extend beyond the narrow disease-focused remit to support genuinely food-system-wide modelling.

The implications reach well beyond academic methodology. As climate change alters agricultural zones, as demand for animal protein reshapes livestock systems, and as antimicrobial resistance erodes the foundations of modern medicine, the trade-offs facing policymakers will become increasingly difficult to evaluate with single-domain analysis. A policy that reduces antimicrobial use in livestock may have consequences for animal welfare, farm economics, food prices, and ultimately human nutrition. A land-use change that boosts crop yields may expand habitat overlap between wildlife and humans, raising zoonotic spillover risk. Only quantitative models that span these domains can make such trade-offs explicit, and only institutions equipped with appropriate data infrastructure and collaborative frameworks can deploy those models where decisions are actually made. The gap between the 129 studies of the research frontier and the narrow institutional initiatives documented in the G20 review is therefore not merely an academic observation; it is a measure of how much analytical capacity remains untapped in exactly the places where it is most needed.

The study, funded by the German Federal Ministry of Research, Technology and Space under the WeAreOne project on public health synergies in the Anthropocene, arrives at a moment when One Health is being institutionalized worldwide, with national plans, dedicated agencies, and international frameworks proliferating. Its central message is a constructive tension: One Health is holistic in principle but narrow in practice, and quantitative modelling is both a symptom of that narrowness and a potential cure. By mapping the methodological state of the art, documenting the institutional shortfall, and proposing a modular framework grounded in genomic, geospatial, and agricultural data infrastructure, Burke and Risius have provided a concrete agenda for turning a unifying philosophy into an operational science. Whether governments seize that agenda will determine whether One Health remains a compelling slogan or becomes the analytical engine that the food systems of the twenty-first century urgently require.

Subject of Research: Interdisciplinary quantitative modelling for One Health applications in agri-food systems

Article Title: Interdisciplinary modelling approaches for One Health in agri-food systems: a scoping review and conceptual analysis

Article References: Burke, T. A., & Risius, A. (2026). Interdisciplinary modelling approaches for One Health in agri-food systems: a scoping review and conceptual analysis. BMC Public Health, 26(1), Article 2760. https://doi.org/10.1186/s12889-026-29666-2

Image Credits: AI Generated

DOI: 10.1186/s12889-026-29666-2

Keywords: One Health, agri-food systems, quantitative modelling, scoping review, antimicrobial resistance, zoonoses, food system, public health, geospatial data, genomic surveillance, interdisciplinary collaboration, G20