GCRI · Health Nexus
Health supply chains
Medicines, vaccines, diagnostics, oxygen and medical devices reach patients through long chains of manufacturers, regulators, procurers, warehouses and carriers, many of which cross borders and depend on few producers. A failure anywhere along the chain becomes a stock-out at the facility. This page sets out how supply risk develops and propagates, how it is measured, and what the evidence serves for the institutions that procure, store and regulate health products.
Health supply chains on the record.
Health supply chains: how the risk develops.
Medicines, cold chain and health logistics. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Concentration upstream
Active pharmaceutical ingredients, key starting materials, vaccine inputs and some devices come from a small number of manufacturers and countries. A plant shutdown, a quality failure or an export restriction can affect supply in many markets at once.
The cold chain is a dependency chain
Vaccines and many biologics must stay within set temperature ranges from manufacture to administration. Power outages, failed refrigerators and heat in transport can degrade potency, often without any visible change to the product.
Information gaps hide shortages
When facilities report stock late or not at all, shortages are discovered when patients are turned away. Weak demand data drive both stock-outs and expiry, and panic ordering in a crisis amplifies both.
Quality failures enter weak systems
Substandard and falsified products enter where regulatory capacity is thin and genuine supply is short, and shortages create the demand for them. The harm includes treatment failure and, for antimicrobials, the selection of resistance.
Environmental exposure in Africa.
Population density, daytime surface heat and night lights 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
- Source
- VIIRS Black Marble (VNP46A2) (NASA LAADS DAAC)
- Provider
- NASA LAADS DAAC
- Licence
- NASA open data
- Vintage
- daily; Satellite radiance, corrected for moonlight and atmosphere
- Newest at build
- 2026-10-12
- Resolution
- about 500 m grid
- Uncertainty
- Satellite night-time radiance for the date shown; lights are a proxy for electricity use and outages, not a measurement of supply or access.
- 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
- 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
- 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
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
- VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (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 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
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Aerosol optical depth, daily: VIIRS NOAA-20 Deep Blue AOD, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
Medicines and supplies depend on industry, energy and trade.
| Domain | How it couples here | What to watch |
|---|---|---|
| Energy | Temperature-controlled storage and transport need power and fuel. | Cold chain temperature excursions |
| Food | Therapeutic and fortified foods share logistics with food aid. | Pipeline breaks for therapeutic foods |
| Water | Manufacturing needs high-purity water; floods disrupt plants and warehouses. | Production sites in flood zones |
| Biodiversity | Some active ingredients and raw materials come from natural sources. | Sourcing of biological raw materials |
Supply risk concentrates in active pharmaceutical ingredients made at few sites. The WHO Model List of Essential Medicines (24th list, 2025) defines the priority set; storage and distribution follow WHO good practices (Technical Report Series 1025, Annex 7, 2020); national regulatory capacity is assessed with the WHO Global Benchmarking Tool.
Health supply continuity.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Health supply continuity
Health supply chains · Province B · Month 1
Tracer commodities
| Oral rehydration salts | 3.1 | 1 | 6 | Adequate |
| First-line antibiotic | 1.6 | 6 | 8 | Low |
| Insulin | 2 | 4 | 9 | Adequate |
| Rapid diagnostic tests | 0.2 | 10 | 8 | Critical |
Order in transit
- Order
- PO-53806
- Route
- Port of entry to central store
- ETA
- 12 days
- Cold chain
- Monitored
Facilities stocked out by district
Supply chain evidence.
Supply chain dependency studies are designed for the Strategic Systems series. The Global Centre for Risk and Innovation (GCRI) does not procure or approve medicines. Useful contributions: stock-out records from facilities, shortage notifications and sourcing concentration data, shared under terms.
A shock through an input-output economy.
A loss of output in one sector propagates through the technical coefficients to its suppliers and customers, and recovers at the rate you set; the coefficients are teaching values, not national accounts. 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
- Sector hit first (position in the list)
- 0
- Size of the shock
- 30% of its output
- Duration of the shock
- 30 days
- Recovery rate
- 0.08 per day
- Days simulated
- 180 days
Readouts
- Output lost over the run
- 0.83% of a year’s output
- Total over direct (multiplier)
- 1.82 times
- Most affected other sector
- Retail
- Its peak inoperability
- 5.1%
Numbers, methods and sources
| Sector | Peak inoperability (%) | Output weight (%) |
|---|---|---|
| Health care | 30.63 | 18.6 |
| Pharmaceuticals | 3.70 | 21.9 |
| Transport | 1.75 | 14.8 |
| Hospitality | 0.28 | 8.2 |
| Retail | 5.12 | 17.8 |
| Public services | 0.22 | 18.6 |
Methods
- Input-output analysis Leontief (1936); Miller and Blair (2009), Input-Output Analysis, Cambridge University Press
- Inoperability input-output model Haimes and Jiang (2001), Journal of Infrastructure Systems 7
- Dynamic inoperability and recovery Lian and Haimes (2006), Systems Engineering 9
- 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:
- Seaports Natural Earth
- Maritime chokepoints Nexus (named straits and canals at their published coordinates)
- Railways 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
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 |
|---|---|---|---|---|
| Tracer medicine availability | percent of facilities with the item in stock | monthly | Logistics systems and facility surveys | OBSERVED |
| Stock-out duration | days per stock-out event | monthly | Logistics management information systems | REPORTED |
| Months of stock on hand | months of average consumption | monthly | Central and regional warehouse records | Published statistics |
| Cold chain equipment functionality | percent of equipment functional | quarterly | Cold chain equipment inventories | REPORTED |
| Temperature excursions | excursions per monitored unit | continuous | Remote temperature monitoring devices | OBSERVED |
| Supplier concentration | count of qualified suppliers per product | per review | Prequalification and registration records | Published statistics |
| Active shortage notices | notices | weekly | Regulatory shortage registers | Published statistics |
| Substandard and falsified product reports | reports | monthly | National regulators and the WHO global surveillance system | REPORTED |
Model health supply chains as a system. Run the cascade.
Assembled from the Nexus Standards area profile Supply chain and trade risk: 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
- Dam failure
- River flood (set off downstream)
- Flash flood (set off downstream)
- Mass displacement (set off downstream)
- Widespread power outage (set off downstream)
- Water supply shortfall (set off downstream)
Systems and couplings
- Supply chain and trade risk
- Food security and nutrition risk Cascade
- Oceans and coasts risk standards Exposure
- Geopolitical and security risk Common driver
- Cyber and digital risk standards 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)NS-12Software, model and AI/ML artefacts (supply chain, model cards, dataset sheets)NRS-H60Hazard family: Supply-chain and flow disruption (NHS-1 sys.supply-chain)NRS-X4Trade, transport and supply chains riskNRS-H89Hazard family: Transport network disruption (NHS-1 infra.mobility)NRS-K38Domain pack profile: Supply chain trade (DOMAIN-038)NRS-H43Hazard family: Livestock and aquaculture epizootics (NHS-1 health.animal-health)NRS-M10Method fabric: Markets, trade and supply chains
Observation
- IMF PortWatch port activity and chokepoint disruptions (listed for reference)
- Global Supply Chain Pressure Index (listed for reference)
- OECD Data Explorer (SDMX API) (listed for reference)
- World Bank Open Data and World Development Indicators API (listed for reference)
- Developing countries’ and least developed countries’ share of global exports
- Passenger and freight volumes, by mode of transport
Example geography
- The Strait of Malacca: 227 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
- Surface soil moisture (SMAP), NASA GIBS
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 |
|---|---|---|
| WHO Prequalification lists | Prequalified medicines, vaccines, diagnostics and immunisation devices with their manufacturers | Public records, WHO terms of use |
| Global Fund Price and Quality Reporting | Transaction-level procurement of health products by grant recipients | Public, Global Fund terms of use |
| UN Comtrade | International trade flows in pharmaceutical products and medical devices by commodity code | Free access with registration, terms of use |
| National medicines regulators’ shortage registers | Active and resolved shortage notices by product | Public records, terms per regulator |
| WHO medical product alerts | Public alerts on substandard and falsified medical products | Public records, WHO terms of use |
| National logistics management information systems | Stock, consumption and orders by facility, for example in DHIS2 or OpenLMIS | By agreement with each ministry of health |
| UNICEF Supply Division market notes | Supply and demand outlooks for vaccines and essential supplies | Public reports, UNICEF terms of use |
The sector signature of health supply chains.
The sectors health supply chains 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
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 |
|---|---|---|
| Central medical stores and procurement agencies | How much buffer stock to hold, where, and from how many suppliers | Supplier concentration; lead-time distributions; stock-out and expiry evidence |
| National medicines regulatory authorities | Which shortages need regulatory flexibility, alternative sources or import authorisation | Shortage registers against demand and qualified supplier lists |
| National immunisation programmes | Where cold chain equipment must be replaced or protected against outages | Equipment functionality, temperature excursion records and power reliability |
| Regional bodies and pooled procurers | Whether to invest in regional manufacturing or shared stockpiles | Concentration and trade dependency evidence; surge scenarios with lead times |
Outputs this topic produces
- Supply dependency maps from ingredient to facility
- Stock-out and expiry risk evidence by product and region
- Cold chain vulnerability profiles against outages and heat
- Surge manufacturing and stockpile scenarios for medical countermeasures
How health supply chains 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 health supply chains: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
What experts ask.
Supply-chain questions trace a product back to its points of failure.
- Where are active ingredients, finished products and critical components made, and how concentrated is production?
- Which products are essential and have no therapeutic substitute?
- How long do stocks last at national, regional and facility level under surge demand?
- Where can the cold chain break between manufacturer and patient?
- How do export restrictions, transport disruption or quality failures turn into shortages?
Mechanisms and drivers.
Generic medicines are often produced at low margins by few manufacturers, and production of key starting materials can be concentrated geographically. A quality failure, factory shutdown or trade restriction at one point can therefore affect many countries. Lean inventory practices reduce cost but also reduce the buffer available when demand rises.
Demand shocks and supply shocks frequently coincide in a crisis. Panic buying and stockpiling by some purchasers can create shortages elsewhere. Last-mile distribution depends on transport, power for refrigeration and trained staff, so a working national supply can still fail to reach a clinic.
How it is studied, and where the methods stop.
| Method or data | What it shows | Limits |
|---|---|---|
| Supply-chain mapping | Where products and components come from | Upstream supplier data is often confidential or incomplete |
| Shortage reports and registries | Which products are short and for how long | Reporting rules differ between jurisdictions |
| Inventory and logistics modelling | How stocks behave under surge and disruption | Depends on demand assumptions that change in crises |
| Cold-chain temperature monitoring data | Where temperature excursions occur | Coverage of the last mile is often weak |
How it is studied, and what the methods cannot say.
- 01Data engineering, interoperability and stewardshipLinking health information systems · Privacy-preserving publicationApplied here
- 02Complex systems and agent-based modellingContact and mobility networks · Cascades across health and infrastructureApplied here
- 03Probability and statisticsSurvey-weighted and small-area estimation · Expected baselines for excess mortalityApplied here
- 04Biotechnology platformsCountermeasure manufacturing capacity · Diagnostic readinessApplied here
- 05Epidemiology and public health scienceSurveillance design and evaluation · Nowcasting under reporting delayApplied 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.
Questions and answers.
Why do shortages happen even when products are approved?
What is a substandard or falsified product?
Does Health Nexus recommend suppliers or products?
Why are cheap medicines often the ones in short supply?
Is stockpiling the answer?
Does Health Nexus procure or distribute medical products?
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