GCRI · Health Nexus
One Health
Many emerging infectious diseases in people originate in animals, and the health of people, animals, plants and ecosystems is linked through shared pathogens, shared environments, food and antimicrobial use. One Health treats these as one system governed by several sectors. This page sets out how risk moves across the human, animal and environmental interfaces, how it is observed, and what joint evidence serves for the ministries and services that share responsibility for it.
One Health on the record.
One Health: how the risk develops.
Human, animal and ecosystem interdependence. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Spillover at the interface
Pathogens pass from wildlife or livestock to people where contact is frequent: land conversion, wildlife trade, intensive farming near forest edges and live animal markets. Each spillover is rare; the conditions that multiply contact are not.
Amplification in animals
Livestock and peri-domestic animals can amplify a pathogen before it reaches people, as with avian influenza in poultry or Rift Valley fever in ruminants after heavy rains. Animal outbreaks often precede human cases, which gives them warning value.
Resistance moves between sectors
Antimicrobial use in people, animals and crops selects resistant bacteria and resistance genes, which circulate through food, water, wastewater and soils. Resistance found in one sector cannot be explained or controlled within that sector alone.
Ecosystems set the conditions
Temperature, rainfall, land use and biodiversity loss change the range and abundance of vectors and reservoir hosts. A changing climate shifts where diseases such as dengue, malaria and tick-borne infections can establish and persist.
Environmental exposure in Africa.
Population density, daytime surface heat and haze over Africa, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Environmental exposure, for this page
About this layer
- Evidence
- Estimated
- Source
- Gridded Population of the World v4.11 (NASA SEDAC (CIESIN, Columbia University))
- Provider
- NASA SEDAC (CIESIN, Columbia University)
- Licence
- CC BY 4.0
- Vintage
- null; Census-based estimate, static (2020)
- Resolution
- about 1 km grid
- Uncertainty
- Census-based estimate of people per square kilometre in 2020, shown as exposure context; not a count of people affected by any hazard.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (persistent cloud still leaves gaps)
- Source
- VIIRS NOAA-20 Level 3, 8-day (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- 8-day; Satellite 8-day composite; persistent cloud still leaves gaps
- Newest at build
- 2026-09-14
- Resolution
- about 1 km grid
- Uncertainty
- Satellite skin temperature of the ground over the 8 days ending on the date shown, a heat exposure indicator; not air temperature, heat index or a heat warning.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- VIIRS NOAA-20 Deep Blue AOD (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite retrieval; clouds leave gaps
- Newest at build
- 2026-10-14
- Resolution
- about 2 km grid
- Uncertainty
- Satellite measure of haze, smoke and dust through the whole air column on the day shown; an exposure indicator, not ground-level PM2.5, an air quality index or a health warning.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS NOAA-20 corrected reflectance (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite image
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite image. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled (latest year GIBS publishes)
- Source
- MODIS Terra and Aqua land cover (MCD12Q1) (NASA LP DAAC)
- Provider
- NASA LP DAAC
- Licence
- NASA open data
- Vintage
- yearly; Satellite classification; latest year GIBS publishes
- Newest at build
- 2024-01-01
- Resolution
- about 500 m grid
- Uncertainty
- Satellite land cover classes for the year shown, including croplands; a classification with known errors, not a farm register.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- MODIS Terra EVI (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite index; clouds leave gaps
- Newest at build
- 2026-10-13
- Resolution
- about 250 m grid
- Uncertainty
- Satellite vegetation index for the 8 days to the date shown, a habitat condition indicator; not a measure of species or biodiversity.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Environmental exposure, framed on Africa.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA JPL OPERA DIST-ALERTSources and licences
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS and VIIRS atmosphere products, NASA LANCE and LAADS DAAC; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET Events are indicative, not authority warnings.
- Observed (EO): NASA JPL OPERA DIST-ALERT (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- Air quality forecast, 5 days, PM2.5 (CAMS via Open-Meteo): Contains modified Copernicus Atmosphere Monitoring Service information 2026.
- Past year, daily temperature (ERA5 via Open-Meteo): Contains modified Copernicus Climate Change Service information 2026.
- Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
- Optional layers (14 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
- Aerosol optical depth, daily: VIIRS NOAA-20 Deep Blue AOD, NASA LANCE · 13 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Enhanced vegetation index, rolling 8 days: MODIS Terra EVI, NASA LANCE · 13 Oct 2026
One Health is itself a coupling of people, animals and ecosystems.
| Domain | How it couples here | What to watch |
|---|---|---|
| Biodiversity | Land-use change and wildlife trade bring people into contact with new pathogens. See ecosystem integrity. | Spillover events by land-use context |
| Food | Livestock disease threatens supply and can cross to people. See food system shocks. | Animal disease notifications in WAHIS |
| Water | Shared water sources carry pathogens and antimicrobial residues. | Antimicrobial resistance in environmental samples |
| Energy | Cold chains for vaccines and diagnostics, for people and animals. | Veterinary cold chain coverage |
The One Health High-Level Expert Panel’s definition of 2021, adopted by FAO, UNEP, WHO and WOAH, describes an integrated approach that aims to balance and optimise the health of people, animals and ecosystems. Jones and colleagues (2008) showed that most emerging infectious disease events are zoonotic and that most of those originate in wildlife.
SEIR on a network of places.
An outbreak seeded in one place spreads through a small-world network by the share of contacts people make elsewhere; play it to see the order of arrival and how the timing of measures moves the peak. Teaching model with stated illustrative values, not a prediction. The map on this page shows Africa; the model itself is not tied to a place.
Controls
- Basic reproduction number R0
- 2.5
- Latent period
- 3.0 days
- Infectious period
- 5.0 days
- Share of contacts made in other places
- 8%
- Places in the network
- 24
- Measures start on day
- 40
- Contact reduction from measures
- 40%
- Days simulated
- 240 days
Readouts
- Peak new infections
- 5.70 per 1,000 a day
- Day of the peak
- 124
- Share ever infected
- 59%
- Places reached
- 100%
- Reproduction number under measures
- 1.50
Numbers, methods and sources
| Day | New infections per 1,000 | Cumulative infected (%) |
|---|---|---|
| 0 | 0.00 | 0.0 |
| 17 | 0.03 | 0.0 |
| 34 | 0.45 | 0.3 |
| 51 | 1.37 | 2.0 |
| 68 | 2.53 | 5.3 |
| 85 | 3.94 | 10.9 |
| 102 | 5.12 | 18.7 |
| 119 | 5.67 | 28.0 |
| 136 | 5.51 | 37.6 |
| 153 | 4.49 | 46.2 |
| 170 | 2.84 | 52.4 |
| 187 | 1.45 | 55.9 |
| 204 | 0.65 | 57.6 |
| 221 | 0.28 | 58.3 |
| 238 | 0.12 | 58.6 |
Methods
- SEIR compartments Kermack and McKendrick (1927), Proc. R. Soc. A 115
- Metapopulation coupling by mobility Balcan and others (2009), PNAS 106
- Small-world network Watts and Strogatz (1998), Nature 393
- Modelling infectious diseases Keeling and Rohani (2008), Princeton University Press
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page’s map can draw:
- Cities Natural Earth
- Airports Natural Earth
- Major roads Natural Earth
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Health Nexus in Foundry
- Tested at Health Nexus in Universe
- Codified in Health and Biosecurity in Standards
- Published in Health Nexus in Reports
- Taught in Faculty of Health and Society in Academy
One Health evidence and where it goes.
Integrated surveillance analyses are designed for the Observatory Signal series and the Global Systems Risk and Resilience series. Disease notification stays with national authorities under the International Health Regulations and WOAH rules. Useful contributions: linked human, animal and environmental surveillance cases, and evaluations of joint investigations.
Model One Health as a system. Run the cascade.
Assembled from the Nexus Standards area profile Health security and One Health: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Antimicrobial resistance
- Tuberculosis surge (set off downstream)
- Typhoid and paratyphoid fever (set off downstream)
- Health-system overload (set off downstream)
Systems and couplings
- Health security and One Health
- Biological hazard standards Mechanism
- Biodiversity and nature risk standards Common driver
- Food and agriculture risk standards Mechanism
- Global and systemic risk Cascade
Controls
NS-0Constitution and principlesNS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-9Contextual UNIs (deployments, instances, programme instances)NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NRS-H83Hazard family: Health system capacity failure (NHS-1 health.system)NRS-H42Hazard family: Rare and persistent human infections (NHS-1 health.other-human)NRS-H41Hazard family: Animal-to-human spillover (NHS-1 health.zoonotic)NRS-H43Hazard family: Livestock and aquaculture epizootics (NHS-1 health.animal-health)NRS-D3Humanitarian response and resilient recoveryNRS-H46Hazard family: Environmental and product harm to people (NHS-1 health.harm)
Observation
- WHO Disease Outbreak News (listed for reference)
- WAHIS animal health events (listed for reference)
- WorldPop gridded population (listed for reference)
- International Health Regulations capacity and health emergency preparedness
- Percentage of bloodstream infections due to selected antimicrobial-resistant organisms
Example geography
- West Africa: 59 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Notified animal disease events | events per reporting period | immediate and six-monthly | National veterinary services reporting to WOAH | Published statistics |
| Human cases of priority zoonoses | notified cases | weekly | Notifiable disease surveillance | REPORTED |
| Antimicrobial use in animals | milligrams of active ingredient per kilogram of animal biomass | annual | National reports to WOAH ANIMUSE | Published statistics |
| Antimicrobial resistance in priority bacteria | percent of tested isolates resistant | annual | Laboratory surveillance reported to WHO GLASS | OBSERVED |
| Vector suitability | dimensionless index | monthly | Climate-driven ecological niche models | MODELLED |
| Forest loss at the human-animal interface | square kilometres | annual | Satellite land-cover change | OBSERVED |
| Joint multisectoral investigations | count of investigations conducted jointly | annual | One Health coordination mechanism records | REPORTED |
The sector signature of One Health.
The sectors One Health touches and how strongly they depend on each other: what a study has to observe and model together.
Signature
Health and its partners
Health gives workforce for operations to infrastructure (0.3), farm labour to food (0.3) and utility workforce to water (0.2).
W · E · I · F · H · B
- Sectors
- 4 of 6
- Relations
- 6 of 15
- Coupling
- 0.42 share of total
Strongest relations in play
- Water → Food · irrigation0.8
- Food → Health · nutrition0.7
- Infrastructure → Water · pipes, dams and treatment works0.6
- Water → Health · drinking water and sanitation0.7
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| WOAH World Animal Health Information System (WAHIS) | Notifications by national veterinary services of animal disease events and six-monthly status reports | Public, WOAH terms of use |
| FAO EMPRES Global Animal Disease Information System (EMPRES-i) | Animal disease events, zoonoses included, compiled from national veterinary reports and other sources | Public, FAO terms of use |
| WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) | National resistance and use data in human health | Open, attribution, WHO terms of use |
| WOAH ANIMUSE | Antimicrobial use in animals reported by national authorities | Public summaries, WOAH terms of use |
| Global Biodiversity Information Facility (GBIF) | Occurrence records for reservoir hosts and vectors | Open, licence per record (CC0, CC BY or CC BY-NC) |
| Hansen Global Forest Change | Annual forest loss and gain mapped from Landsat | Free and open, attribution (CC BY 4.0) |
| INSDC sequence archives (GenBank, ENA, DDBJ) | Pathogen genomes from human, animal and environmental samples | Free and unrestricted access |
How One Health is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
System model
The couplings between the systems in One Health: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
Who decides, and what the evidence serves.
Health Nexus prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| National One Health coordination mechanisms | Which zoonoses to prioritise, and how ministries share surveillance and response | Joint risk assessments with each sector’s evidence labelled; prioritisation inputs |
| Chief veterinary officers and veterinary services | When an animal outbreak warrants control measures with human health implications | Animal event reports beside human surveillance and sequence relatedness |
| Ministries of health and agriculture | Where to restrict or steward antimicrobial use across human and animal sectors | Resistance and use data compared across sectors, with comparability stated |
| Environment and wildlife authorities | Which land-use and wildlife trade practices raise spillover risk | Interface maps and host occurrence evidence, with sampling gaps shown |
Outputs this topic produces
- Joint risk assessments that combine human, animal and environmental evidence
- Spillover interface maps by land use, livestock density and host range
- Antimicrobial resistance and use evidence linked across sectors
- One Health evidence packs for multisectoral coordination mechanisms
What experts ask.
The central questions concern spillover, resistance and coordination.
- Where and under what conditions do pathogens move between animals and people?
- How do land-use change, farming practices and wildlife trade alter spillover risk?
- How does antimicrobial use in human medicine, livestock and aquaculture drive resistance?
- Can surveillance in animals and the environment give earlier warning than human case reports?
- Which institutions must share data and decisions for a One Health approach to work?
Mechanisms and drivers.
Many emerging infectious diseases in people originate in animals. Spillover depends on contact between species, which is shaped by habitat loss, agricultural expansion, livestock density and the handling and trade of animals. Once a pathogen reaches people, its spread depends on transmissibility, population mobility and the response.
Antimicrobial resistance follows a different but connected mechanism: every use of an antimicrobial selects for resistant organisms, and resistance genes move between bacteria and between humans, animals and the environment through water, soil, food and waste. The result is a slow-moving threat to the effectiveness of routine medicine.
How it is studied, and where the methods stop.
| Method or data | What it shows | Limits |
|---|---|---|
| Integrated surveillance across human, animal and environmental sectors | Earlier and broader detection of shared threats | Sectors use different systems, definitions and reporting incentives |
| Wastewater and environmental sampling | Community-level signals of pathogens and resistance | Signals need careful interpretation and do not identify cases |
| Genomic sequencing | Pathogen origins, relatedness and resistance genes | Sampling is uneven across regions and species |
| Ecological and land-use modelling | Where conditions favour spillover | Identifies risk, not events; validation is difficult |
How it is studied, and what the methods cannot say.
- 01Epidemiology and public health scienceSurveillance design and evaluation · Nowcasting under reporting delayApplied here
- 02Molecular and life sciencesGenomic surveillance of pathogens · Cross-species linkage in One HealthApplied here
- 03Complex systems and agent-based modellingContact and mobility networks · Cascades across health and infrastructureApplied here
- 04Data engineering, interoperability and stewardshipLinking health information systems · Privacy-preserving publicationApplied here
- 05Probability and statisticsSurvey-weighted and small-area estimation · Expected baselines for excess mortalityApplied here
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
Where this page leads across the Consortium.
Questions and answers.
What is One Health?
Can spillover be predicted?
Why link antimicrobial resistance across sectors?
Is One Health only about pandemics?
Why is coordination so difficult?
Does Health Nexus collect field samples?
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