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Climate models become substantially more useful for public health when they do more than project temperature or rainfall. They must connect multiple hazards to health outcomes, show which groups are most exposed, represent adaptation and health-system capacity, and express uncertainty in forms that support warnings, budgets and preparedness decisions.
Why climate-health modelling needs to expand
Climate change affects health through several pathways at once: heat, floods and storms, wildfire smoke, drought, food and water stress, infectious-disease suitability, air pollution and disruption of health services. These pathways can compound one another and interact with poverty, age, housing, occupation, migration and access to care. A model that treats temperature as the only exposure can therefore miss both the size and distribution of harm.
The Lancet Countdown’s 2024 assessment illustrates the scale of the challenge. Infants and adults older than 65 experienced an average of 13.8 heatwave days per person in 2023. Heat-related mortality among people older than 65 was 167% higher than in the 1990s; that was 102 percentage points above the increase expected without temperature rise. At least one month of extreme drought affected 48% of global land area in 2023. Climatic suitability for dengue transmission increased between 1951–60 and 2014–23 by 46.3% for Aedes albopictus and 10.7% for Aedes aegypti.
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A 2025 synthesis of modelling priorities, based on a survey of 65 global climate-and-health modelling experts and 36 hybrid-meeting participants, calls for wider causal pathways and health endpoints, interdisciplinary methods, scenario development, adaptation evaluation, early-warning models, intermodel comparison and capacity building. It also argues that the 2014 World Health Organization quantitative risk assessment should be updated to cover more pathways and outcomes.
What an expanded model should represent
| Model layer | What to include | Why it matters for decisions |
|---|---|---|
| Climate and environmental hazards | Heat, precipitation extremes, floods, drought, wildfire smoke, air pollution, water conditions, land use and compound events | Health agencies can see simultaneous or cascading threats rather than a single hazard in isolation |
| Health endpoints | Mortality, heat illness, other morbidity, mental health, infectious disease, nutrition, water-related disease, air-pollution effects and health-service disruption where data support them | Different outcomes require different thresholds, response teams and indicators |
| Population vulnerability | Age, sex, income, education, occupation, migration, urbanisation, housing, baseline disease and access to health care | National averages do not reveal who is at greatest risk or where resources are needed |
| Human and institutional response | Cooling access, warning response, vaccination, vector control, housing, occupational practices and health-system surge capacity | Models can test whether an intervention changes risk under specified conditions |
| Decision outputs | Probabilistic forecasts, thresholds, lead times, uncertainty ranges and equity breakdowns | Officials can connect a result to an alert, budget, deployment plan or preparedness action |
Include non-climate drivers, not just exposure
Climate exposure is only one input to a health outcome. Age structure, income, education, migration, urbanisation, land use, health-care access, baseline disease and adaptive capacity can change risk even when the physical hazard is identical.
Vector-borne disease demonstrates why this matters. The IPCC notes that transmission is influenced by climate as well as population movement, forest clearance, land use, water configuration, population density and predator ecology. A model that maps temperature suitability without these factors can overstate risk in one place and understate it in another.
Vulnerability should be resolved at the finest geography and demographic detail that the data can support. A country-level estimate may be useful for national planning, while a neighbourhood, age, sex or occupation breakdown may be necessary for heat-alert operations or worker protections. Analysts should document where small-area estimates are based on sparse or biased data instead of presenting them as equally certain.
Model adaptation and health-system resilience explicitly
People and institutions do not passively receive climate hazards. They change behaviour, use warnings, seek cooling, vaccinate, control vectors, alter work schedules and strengthen buildings. Health systems open surge capacity, reposition supplies and modify surveillance. If a model holds vulnerability constant, it cannot show whether an intervention prevents illness or simply shifts risk.
WHO’s 2023 operational framework is designed to help health organisations “anticipate, prevent, prepare for, and manage climate-related health risks.” It defines ten components of health-system resilience. A practical model should map its variables and outputs to those components rather than treating resilience as a single score.
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Interventions should be tested under multiple climate and socioeconomic scenarios. Useful questions include:
- How much does earlier heat warning reduce emergency visits when cooling access is unequal?
- How does vaccination or vector control change infectious-disease risk as climatic suitability shifts?
- Which housing, workplace or urban measures protect people who cannot relocate or afford air conditioning?
- How much surge capacity is needed when heat, wildfire smoke or flooding disrupts routine services at the same time?
The 2025 synthesis concludes: “Integrated assessment approaches should be enhanced to better capture human responses to climate change (ie, adaptation) and to reflect the complex linkages among climate change, health, and ecosystems within the context of the Sustainable Development Goals.”
Build interoperable climate, environmental and health data
Data integration is an operational requirement, not an optional enhancement. The WHO–WMO 2023–2033 plan calls for climate, environmental and health information to be generated and used together in public-health decisions.
A usable data system links meteorological observations and forecasts with environmental monitoring, disease surveillance, demographic information and health-service records. Each dataset needs documented geography, time resolution, missingness, definitions and likely sources of bias. Interoperability allows a warning model to use the same location and time standards as hospital, ambulance and laboratory systems.
Data gaps should be visible in the output. Incomplete surveillance can make an apparent decline in disease reflect under-reporting; inconsistent definitions can make two regions look different when they measure different outcomes. Models should preserve provenance and provide quality flags so officials know when an estimate is suitable for an operational trigger and when it is better treated as a planning range.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsSeparate the different kinds of uncertainty
Climate-health projections contain at least three distinct uncertainties:
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- Scenario uncertainty: different future emissions, development, demographic and policy pathways produce different hazards and exposure patterns.
- Parameter uncertainty: the estimated strength of an exposure–response relationship, such as the change in mortality associated with heat, is not known exactly.
- Structural uncertainty: alternative model designs can represent pathways, feedbacks or adaptation differently.
The IPCC also cautions that interacting non-climate drivers can be as important as climate exposure. A single central estimate hides these distinctions. Agencies should receive ranges or probability distributions, the assumptions that produced them and sensitivity analyses showing which inputs drive the result.
Local projections should be described as conditional scenarios or ranges, not as inevitable forecasts. Calibration against observed outcomes, external validation in another place or period, scenario ensembles and intermodel comparison can reveal whether added complexity improves reliability. A more elaborate model that cannot be validated or does not change a decision may be less valuable than a simpler, well-calibrated system.
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| Decision need | Required characteristics | Output officials can use |
|---|---|---|
| Immediate heat or smoke response | Near-real-time observations, short lead times, vulnerable-group stratification and tested thresholds | Probabilistic alert, expected caseload and actions by service area |
| Seasonal infectious-disease planning | Climate forecasts linked to surveillance, vector ecology, population movement and intervention scenarios | Risk range, geographic priorities and timing for vaccination or vector control |
| Infrastructure and service planning | Near-term scenarios, compound hazards, facility locations, capacity and disruption pathways | Resource requirements, surge plans and service-continuity options |
| Long-term adaptation appraisal | Multiple climate and socioeconomic scenarios, distributional impacts, adaptation effectiveness and uncertainty analysis | Comparative consequences, equity impacts and robust investment choices |
Design teams should define the decision before selecting the model. The key questions are the hazard coverage, health endpoints, population resolution, time horizon, lead time, adaptation representation, validation strategy and the policy threshold that will trigger action. This prevents a technically impressive projection from becoming unusable for a health department.
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- Define the decision. Specify whether the product is an early warning, resource-allocation tool, adaptation appraisal or health-system resilience assessment.
- Map the causal pathway. List relevant hazards, environmental conditions, exposure routes, health endpoints and likely confounders or interacting drivers.
- Profile vulnerable groups. Choose demographic, socioeconomic and geographic strata that correspond to unequal exposure, susceptibility or access to protection.
- Assemble linked datasets. Harmonise climate, environmental, surveillance, demographic and service data; record resolution, missingness and bias.
- Add response options. Represent warning uptake, cooling, vaccination, vector control, housing, work practices and service surge capacity as changeable inputs.
- Run scenarios and uncertainty analysis. Separate scenario, parameter and structural uncertainty, and test compound hazards and non-climate drivers.
- Validate and stress-test. Check calibration, perform external validation where possible, compare models and assess whether complexity improves the decision.
- Publish an operational product. Give users thresholds, lead times, probability ranges, equity breakdowns, assumptions and a clear action associated with each alert level.
What responsible interpretation looks like
No single standard architecture has been established for every climate-health question. Models remain constrained by incomplete surveillance, inconsistent definitions, uncertain exposure–response functions, scenario assumptions and structural uncertainty. Results should therefore state the geography, period, population, assumptions and attribution limits alongside every estimate.
The goal is not to predict every illness years in advance. It is to improve decisions: identify who is exposed, anticipate plausible ranges, compare interventions, allocate capacity and make uncertainty explicit. Expansion is worthwhile when it connects evidence to an accountable public-health action; it is not worthwhile merely because it adds variables.
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