For much of the past decade, the case for One Health has been conceptual: that human, animal and environmental health cannot be treated as separate systems. That argument has largely been won. The harder question now, at the centre of the 9th World One Health Congress in Lisbon, is how to make it work in practice.
Human health cannot be separated neatly from animal health, animal health cannot be separated from ecosystems, food systems, land use, climate or biodiversity, and pathogens move between species and across borders as antimicrobial resistance emerges and circulates across human, animal and environmental systems and as changes in ecosystems alter the opportunities for pathogens, vectors, hosts and people to interact. That intellectual argument has largely been won, so the question facing One Health in 2026 is becoming a different one: how do we make it work?
That question sits at the centre of the 9th World One Health Congress, taking place in Lisbon from 4–7 September 2026, and of new work released by the One Health High-Level Expert Panel (OHHLEP) as the Congress opened. [1,2]
The World Organisation for Animal Health (WOAH) has characterised the challenge unusually clearly: data remain on separate platforms, expertise remains divided between institutions, funding remains allocated through sector-specific budgets, and global frameworks exist but translating them into durable national systems remains difficult. [3] This is not primarily a failure of scientific understanding — it is an operating-model problem. And underneath it lies an even more difficult problem: prevention has to compete for money against events that have already happened. That may be one of the most important challenges facing One Health.
From One Health as a concept to One Health as infrastructure
The currently accepted OHHLEP definition describes One Health as an integrated, unifying approach that aims to sustainably balance and optimise the health of people, animals and ecosystems. [4] That definition matters, but a definition cannot itself integrate a surveillance system, establish which ministry owns a risk, determine who pays when an intervention primarily benefits another sector, reconcile veterinary, public-health and environmental datasets, create an information-sharing agreement, or ensure that a prevention programme survives the disappearance of emergency funding. These are institutional questions.
The Quadripartite organisations — FAO, UNEP, WHO and WOAH — attempted to address them through the One Health Joint Plan of Action (OH JPA), originally established for 2022–2026 and recently extended through 2029. [5] The extension matters because it signals that One Health implementation is not a short programme with a defined endpoint, but requires sustained institutional development.
But OHHLEP’s newly published analysis suggests that implementation continues to encounter substantial systemic barriers. Drawing on more than 260 inputs, OHHLEP has identified ten interconnected recommendations spanning five strategic domains: governance, finance, data, community engagement and workforce capacity. [2] These domains are revealing — none is fundamentally about proving that humans, animals and ecosystems are connected. They concern the machinery required to act on that knowledge.
The problem of institutional boundaries
Consider a hypothetical zoonotic risk detected in livestock. The veterinary authority may bear the cost of surveillance. Farmers may bear the cost of movement restrictions or culling. An agriculture ministry may fund compensation. A public-health system may receive much of the eventual benefit if human infections are prevented. Environmental agencies may possess information about wildlife hosts. Local authorities may be responsible for implementation. National government may ultimately receive the macroeconomic benefit of avoiding a larger outbreak — yet while the biological system is connected, the accounting system is not.
This creates a fundamental problem for One Health investment: if Department A pays while Department B receives the measurable benefit, conventional departmental budgeting can systematically undervalue prevention, and the problem becomes even harder when the primary benefit is something that never occurs. A hospital can count admissions, a laboratory can count positive samples, a veterinary authority can count infected herds, and a government can calculate compensation payments — but how does a finance ministry count the outbreak that did not happen? That question sits at the heart of the economics of prevention.
Prevention suffers from an evidence paradox
Successful prevention frequently makes itself invisible. If surveillance identifies an emerging pathogen and action prevents wider transmission, the counterfactual — what would have happened without intervention — cannot subsequently be observed, and the better prevention works, the less dramatic its outcome may appear. This produces what might be called a prevention paradox of evidence: response generates observable expenditure and observable outcomes, while prevention often generates expenditure against an unobservable counterfactual. That asymmetry matters politically.
After an outbreak begins, governments can readily justify emergency expenditure: hospitals are filling, animals are dying, trade is disrupted, farmers are demanding compensation, images appear in the media, and economic losses accumulate. The benefits of intervention are immediate and visible. Before an outbreak, the proposition is different — governments are asked to spend money today to reduce the probability or magnitude of something that might happen tomorrow, and if prevention succeeds, critics may subsequently argue that the expenditure was unnecessary because the feared event never occurred.
This helps create the familiar cycle of crisis → investment → control → declining attention → declining investment → vulnerability → crisis. The World Bank has described precisely this problem as a cycle of panic, neglect and chronic underinvestment in prevention. [6]
The economic case is already substantial
The difficulty is not that prevention lacks an economic rationale. The World Bank has estimated that prevention based on One Health principles would require approximately US$10.3–11.5 billion annually, compared with substantially larger expenditure associated with pandemic preparedness and vastly larger losses from pandemic events themselves. [7] COVID-19 demonstrated the scale of the asymmetry: the World Bank estimated that global output fell by approximately US$3.6 trillion in 2020, alongside the enormous direct health, fiscal and societal consequences of the pandemic. [6]
More recent work has strengthened the investment argument. In August 2026, the FAO Investment Centre, working with WHO and WOAH, published a new assessment of the financial and economic returns from investing in One Health. [8] The analysis considers potential benefits across disease burden, healthcare expenditure, environmental contamination and antimicrobial resistance, yet it also identifies an important weakness in the existing evidence base: the benefits of One Health are widely accepted, but quantitative evidence demonstrating its additional economic value remains incomplete.
That is important. One Health increasingly has a compelling scientific case; what it still needs is a sufficiently mature investment case.
Prevention needs an accounting language
This suggests that One Health may need something analogous to the frameworks routinely used for other forms of public investment. Infrastructure programmes do not simply state that roads are useful — investment cases attempt to estimate a chain from capital expenditure, through infrastructure created and utilisation, to economic benefit. Energy projects estimate a chain from investment, through generation capacity and energy produced, to revenues or cost savings and return. Businesses routinely analyse investment, capability, output and financial return in much the same way.
One Health prevention should increasingly be capable of articulating a comparable chain: investment → capability created → risk modified → health/economic consequences → value generated. For example, €5 million invested in expanded veterinary surveillance might produce greater diagnostic coverage and faster detection, which reduces the probability of uncontrolled transmission, which in turn means fewer infected holdings, human exposures or movement restrictions, and therefore lower response, healthcare, compensation and trade costs.
That does not mean pretending the counterfactual is certain. It means explicitly modelling uncertainty — and that distinction is crucial.
Introducing the OHS Prevention Ledger
At One Health Security, we believe one way of improving this conversation is to make the economics of prevention more visible. We are therefore developing an experimental framework we call the OHS Prevention Ledger, intended to provide a structured way of describing the relationship between prevention expenditure and the capabilities, risks and potential losses associated with it.
At its simplest, the chain runs from prevention investment, through capability created and risk reduction, to an observed outcome and the costs potentially avoided as a result. Importantly, the Ledger will distinguish between different levels of evidence: a surveillance programme may have an exact recorded cost (an observed value), the number of laboratories created may also be observed, and the reduction in diagnostic turnaround time can be measured — but the probability that those improvements prevented a major outbreak may need to be modelled, and the economic consequences of the outbreak that did not occur are necessarily counterfactual. Those numbers should never be presented as though they have equal epistemic status, so the Ledger will distinguish explicitly between measured, estimated, modelled and counterfactual values. That distinction is essential if economic arguments for prevention are to remain scientifically credible.
What would a Prevention Ledger contain?
A worked Ledger could begin with six components.
1. Investment
What was actually spent? This might include surveillance, laboratory capacity, workforce, vaccination, biosecurity, wildlife monitoring, environmental surveillance, data infrastructure, training and compensation mechanisms. Where possible, these should be observed financial values rather than estimates.
2. Capability
What did that expenditure create? Examples include additional samples processed, increased geographic surveillance coverage, shorter diagnostic turnaround, trained epidemiologists, genomic sequencing capacity, improved farm biosecurity, increased vaccination coverage and interoperable surveillance systems. This is important because prevention spending should not be treated as disappearing expenditure — it creates assets and capabilities.
3. Risk modification
How did the capability alter risk? Possible measures include changes in the probability of emergence, the probability of detection, time to detection, the probability of onward transmission, the effective reproduction number, outbreak duration, geographic spread, and mortality or morbidity. This is where epidemiological modelling becomes particularly important.
4. Observed outcome
What actually happened? For example: an outbreak detected at five holdings, 12 human cases, no international spread, containment achieved in six weeks. These are observed facts.
5. Counterfactual
What might plausibly have happened without the intervention? This must be modelled rather than asserted. A credible analysis might therefore present conservative, central and high-impact scenarios rather than a single headline number.
6. Economic consequence
The final stage estimates potential avoided costs, which could include healthcare expenditure, livestock mortality, production losses, culling, compensation, surveillance escalation, trade restrictions, tourism effects, workforce absence, emergency response and wider macroeconomic losses.
The output would not claim “this programme saved €500 million.” Instead, it might conclude: “An observed €8 million prevention investment created specified surveillance capabilities. Under the central counterfactual model, these capabilities were associated with an estimated €120–180 million reduction in expected losses.” That is a much more defensible statement.
From avoided costs to expected loss
There is an additional refinement that matters: the value of prevention cannot simply be calculated as the total cost of the worst possible outbreak, because risk is a combination of probability and consequence. If an event capable of causing €10 billion in losses has only a 1% probability of occurring over a particular period, its expected loss is not €10 billion. A simplified formulation is Expected Loss = Probability × Consequence. Prevention changes one or both components, so Value of Prevention ≈ Expected Loss before intervention − Expected Loss after intervention.
If a programme reduces annual outbreak probability from 5% to 2%, and the expected consequence of an outbreak is €1 billion, then before intervention the expected annual loss is 0.05 × €1bn = €50m, and after intervention it is 0.02 × €1bn = €20m — an estimated reduction in expected loss of €30m per year. If the intervention costs €5 million annually, that produces an indicative benefit-cost ratio of 6:1.
Real analyses are considerably more complicated: probabilities are uncertain, consequences have distributions, interventions interact, benefits may occur across multiple sectors and years, discount rates matter, distributional effects matter, and some outcomes — biodiversity, animal welfare, social trust or lives lost — cannot responsibly be collapsed into a single monetary value without careful qualification. But uncertainty is not an argument against analysis. It is an argument for making assumptions visible.
The accounting problem is also a governance problem
A Prevention Ledger could reveal something else: the institution paying for prevention may not be the institution receiving the benefit. Imagine that veterinary surveillance costs €10 million annually. The benefits might appear as €5m in reduced livestock losses, €15m in avoided public-health expenditure, €20m in protected agricultural exports, and €5m in reduced emergency-response expenditure. From the agriculture ministry’s budget alone, the investment might look marginal; from the government’s consolidated balance sheet, it could look highly attractive; from a societal perspective, it may be more attractive still.
This is why One Health cannot simply require ministries to “collaborate” — our analysis of Salmonella and the governance gap looks at exactly this failure mode in practice. Financing mechanisms themselves may need to become One Health mechanisms. Shared risks may require shared budgets.
Data integration becomes economic infrastructure
The Congress discussions also expose another issue. WOAH notes that One Health information frequently remains divided across separate platforms and institutions. [3] This is usually discussed as a surveillance problem, but it is also an economics problem — a point we have explored in relation to AI-driven outbreak detection: if animal-health, human-health and environmental data cannot be linked, it becomes much harder to demonstrate that an intervention in one sector generated benefits in another.
Integrated information systems therefore do more than improve outbreak detection — they create the evidence necessary to evaluate prevention, which can subsequently support investment. The relationship becomes circular: investment → capability → data → evidence → demonstrated value → future investment. Poor information architecture breaks that cycle.
Prevention is not the absence of response
There is also a danger in framing prevention purely as a cheaper alternative to emergency response. A resilient One Health system requires both: surveillance without response capacity achieves little, and early detection without laboratories, epidemiology, communication, veterinary capacity, healthcare systems and political authority cannot contain a serious event.
The objective is therefore not prevention instead of preparedness, but to shift investment further upstream while maintaining the capacity to respond when prevention fails. That is an important distinction — and it is closely related to one we have made before: epidemiological recovery is not the same thing as preparedness for the next emergency. Risk can be reduced. It cannot be eliminated.
The next phase of One Health
The Lisbon Congress may therefore mark an important transition. The first era of One Health was largely about recognition — that human, animal and environmental health are connected. The next era has to be operational: how do institutions behave as though they are connected? That requires governance, interoperable information, workforce capacity, political authority and, above all, financing.
WOAH’s message from Lisbon is that One Health cannot survive as a collection of declarations and temporary projects. [3] The new OHHLEP implementation analysis reaches a similar conclusion through its focus on governance, finance, data, communities and workforce. [2] The challenge now is therefore not another definition. It is an operating model. And any sustainable operating model eventually encounters the same question: who pays?
One Health needs to be able to answer that question with more than the observation that prevention is desirable. It needs to demonstrate what an investment purchased, what capability it created, what risk changed, how confident we are that it changed, who benefited, and what economic consequences plausibly followed. That is what we intend to explore with the OHS Prevention Ledger, alongside our reporting from Lisbon on how the Congress’s opening conversation shifted from principle to investment and from principle to delivery.
Because if prevention remains economically invisible, it will remain politically vulnerable. And waiting until the cost of a threat becomes visible is precisely what prevention is supposed to prevent.
Questions & Answers
What is the 9th World One Health Congress?
A four-day international conference held in Lisbon, Portugal from 4–7 September 2026, bringing together the Quadripartite organisations (WHO, FAO, UNEP and WOAH) and researchers, policymakers and practitioners working across human, animal and environmental health.
What is OHHLEP, and what did it just publish?
The One Health High-Level Expert Panel is an advisory body to the Quadripartite organisations. As the Lisbon Congress opened, it published an analysis drawing on more than 260 inputs, setting out ten recommendations across governance, finance, data, community engagement and workforce capacity for accelerating implementation of the One Health Joint Plan of Action.
Why is prevention so hard to fund compared with emergency response?
Successful prevention often makes itself invisible — an outbreak that never happens generates no hospital admissions, no compensation payments and no images for the media, while response to an active crisis produces visible, easily justified expenditure. This creates a persistent bias towards funding response after harm becomes visible rather than capability before it does.
What is the OHS Prevention Ledger?
An experimental framework we are developing to make the economics of prevention more transparent, tracing investment through to the capability it creates, the risk that capability reduces and the outcomes potentially avoided as a result — while explicitly distinguishing measured, estimated, modelled and counterfactual values rather than collapsing everything into a single return-on-investment figure.
How much would One Health-based prevention cost globally?
The World Bank has estimated that prevention based on One Health principles would require approximately US$10.3–11.5 billion annually — a fraction of the roughly US$3.6 trillion the World Bank estimated the global economy lost in 2020 alone because of COVID-19.
Why can’t the value of prevention just be the cost of the worst-case outbreak avoided?
Because risk combines probability and consequence, not consequence alone. A catastrophic event with a low probability of occurring has a much smaller expected loss than its worst-case cost, so the value of prevention is better estimated as the reduction in expected loss (probability multiplied by consequence) that an intervention achieves.
What does One Health need now, according to this analysis?
Not another definition of One Health, but an operating model — governance that assigns responsibility, interoperable data, adequate workforce capacity, political authority, and financing mechanisms that can answer the question of who pays when the institution bearing the cost is not the one receiving the benefit.
References
- World Health Organization (2026). The 9th World One Health Congress. Lisbon, Portugal, 4–7 September 2026. WHO, FAO, UNEP and WOAH are participating as the Quadripartite organisations.
- One Health High-Level Expert Panel (OHHLEP) (2026). Enablers and Barriers to Implementing the Quadripartite One Health Joint Plan of Action: Recommendations to the Quadripartite in Accelerating Its Roll-Out. 4 September 2026. Drawing on more than 260 inputs, the analysis sets out ten interconnected recommendations across governance, finance, data, community engagement and workforce capacity.
- World Organisation for Animal Health (WOAH) (2026). Strengthening One Health Through Coordinated Action on Implementation, Science, Policy and Financing. A Quadripartite statement identifying fragmented data, institutional separation and sector-specific financing among the practical barriers to durable One Health implementation.
- World Health Organization. One Health. WHO describes One Health as an integrated, unifying approach intended to sustainably balance and optimise the health of people, animals and ecosystems.
- World Health Organization (2026). Quadripartite Collaboration Extends the One Health Joint Plan of Action to 2029. 14 August 2026.
- World Bank (2022). Prevent Rather Than Fight the Next Pandemic with a One Health Approach. 24 October 2022. Describes chronic underinvestment in prevention and the cycle of panic and neglect that follows major health emergencies, and estimates that COVID-19 reduced global output by approximately US$3.6 trillion in 2020.
- World Bank (2022). One Health Approach Can Prevent the Next Pandemic. 24 October 2022. Estimated annual One Health-oriented prevention requirements at approximately US$10.3–11.5 billion, compared with substantially greater preparedness requirements and potential pandemic losses.
- FAO Investment Centre, WHO & WOAH (2026). People, Animals, Plants, Ecosystems – Making the Case for Investing in One Health. 25 August 2026. Examines financial and economic returns from One Health investment and identifies the need for stronger quantitative evidence linking integrated prevention to economic returns.
Key Takeaways
- The intellectual case for One Health — that human, animal and environmental health are connected — has largely been won; the 9th World One Health Congress in Lisbon (4-7 September 2026) is now focused on a harder question: how to make it work operationally.
- OHHLEP's new implementation analysis, drawn from over 260 inputs, identifies governance, finance, data, community engagement and workforce capacity — not scientific understanding — as the real barriers.
- Prevention suffers from an evidence paradox: successful prevention makes itself invisible, since a prevented outbreak leaves no hospital admissions, no compensation payments and no images for the media, while response to an active crisis is always visible and easy to fund.
- The economic case is already substantial — the World Bank estimates One Health-based prevention would cost roughly US$10.3-11.5 billion a year, against an estimated US$3.6 trillion global output loss from COVID-19 in 2020 alone.
- One Health Security is developing an experimental OHS Prevention Ledger to trace prevention investment through to capability created, risk reduced and outcomes avoided, distinguishing clearly between measured, estimated, modelled and counterfactual values.
- Because risk is probability multiplied by consequence, the value of prevention should be measured as a reduction in expected loss, not the cost of the worst-case outbreak avoided — and because the institution paying for prevention is often not the one receiving the benefit, financing mechanisms themselves may need to become One Health mechanisms.