Contact and mobility networks
Agent-based models represent households, schools, workplaces and travel, so the effect of contact patterns and public health measures on transmission can be explored under stated parameters.
GCRI · Health Nexus
Complex systems modelling for health
Complex systems methods show how health risk emerges from interactions: people mixing and moving, hospitals sharing patients and staff, supply networks sharing suppliers, and health services depending on power and water. Agent-based and system dynamics models let these interactions be explored as scenarios, with every assumption visible.
01Register
Complex systems modelling for health on the record.
Method recordSTEM method
- MethodComplex systems and agent-based modelling
- Applied inHealth Nexus
- Data4 inputs
- Checks3 validation steps
- Canonical pageSTEM Commons
02Application
What complex systems and agent-based modelling does here.
Complex systems and agent-based modelling study systems whose behaviour emerges from interactions among many components. They help explain nonlinear dynamics, tipping points and collective behaviour. In health and biosecurity, complex systems approaches explore how interacting risks can produce sudden, systemic shifts. They support stress testing of policies against emergent behaviour. The canonical description is on the STEM Commons.
Cascades across health and infrastructure
System dynamics models link power, water, staff and supply buffers to service capacity, tracing how a grid outage or heatwave propagates into hospital functions over hours and days.
Patient transfer networks
Networks of referrals and transfers between hospitals are analysed to see where surges concentrate and how resistant organisms can spread between facilities.
03Geography
Environmental exposure in Africa.
Daytime surface heat, population density 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
- 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
- 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 (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
- Observed (clouds leave gaps)
- Source
- OMI Aura (NASA GES DISC)
- Provider
- NASA GES DISC
- Licence
- NASA open data
- Vintage
- daily; Satellite retrieval; clouds leave gaps
- Newest at build
- 2026-10-10
- Resolution
- about 2 km grid
- Uncertainty
- Satellite column of nitrogen dioxide in the lower atmosphere on the day shown, mostly from combustion; not a ground-level concentration 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 · ESA Copernicus Sentinel-5P TROPOMI · 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
- 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
- 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
- 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
- OMI nitrogen dioxide (Aura) and OMPS aerosol index (Suomi NPP), NASA; distributed by the NASA GES DISC; 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): ESA Copernicus Sentinel-5P TROPOMI (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).
- 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
- Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- 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
- Nitrogen dioxide, lower atmosphere column: OMI Aura, NASA GES DISC · 10 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
04Data
What the method consumes.
Datasets and records used by complex systems and agent-based modelling on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Social contact surveys | Mixing matrices by age and setting | OBSERVED |
| Aggregated mobility data from transport and telecommunications | Movement between places | ESTIMATED |
| Hospital transfer records | Referral network structure | REPORTED |
| Facility dependency inventories | Buffers and failure thresholds | Published statistics |
05Simulator
Stocks, flows, delays and a tipping threshold.
Two stocks, pressure and adaptive capacity, joined by a reinforcing growth loop, a delayed balancing response and damage when pressure outruns capacity; past the threshold the system can tip. 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
- Growth of pressure
- 6.0% a year
- Efficacy of capacity in reducing pressure
- 4.0% a year
- Investment response to perceived pressure
- 20%
- Perception delay
- 4.0 years
- Tipping threshold of pressure
- 60 index
- Amplification past the threshold
- 2.0 times
- Years simulated
- 50 years
Readouts
- Peak pressure
- 113 index
- Capacity at the end
- 186 index
- Years over the threshold
- 15.8 years
Numbers, methods and sources
| Year | Pressure | Capacity | Perceived |
|---|---|---|---|
| 0 | 20.0 | 50.0 | 20.0 |
| 4 | 23.3 | 44.9 | 21.2 |
| 8 | 27.2 | 42.2 | 24.0 |
| 12 | 31.7 | 42.3 | 27.7 |
| 16 | 36.8 | 45.5 | 32.2 |
| 20 | 42.3 | 51.5 | 37.2 |
| 24 | 47.9 | 60.3 | 42.6 |
| 28 | 53.2 | 71.3 | 48.0 |
| 32 | 57.8 | 84.3 | 53.1 |
| 36 | 66.4 | 98.4 | 58.4 |
| 40 | 82.7 | 115.9 | 69.7 |
| 44 | 97.8 | 139.7 | 83.8 |
| 48 | 109.1 | 169.4 | 97.3 |
Methods
- System dynamics Forrester (1961), Industrial Dynamics, MIT Press; Sterman (2000), Business Dynamics
- Critical transitions and tipping points Scheffer and others (2009), Early-warning signals for critical transitions, Nature 461
- Causal loop diagram Sterman (2000), Business Dynamics, McGraw-Hill
- 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
No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme’s own evidence.
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
06Validation
How results are checked.
Models are evaluated by matching multiple stylised facts and patterns at different scales. Sensitivity and robustness analysis test dependence on assumptions.
| Check | How it is done |
|---|---|
| Pattern-oriented validation | Models must reproduce several observed patterns at different scales, not a single curve |
| Global sensitivity analysis | The parameters that drive most of the outcome range are identified and reported |
| Structural alternatives | Results are compared with simpler or differently structured models before conclusions are drawn |
Many plausible models can reproduce the same patterns. Calibration and validation data are often limited.
07Twin and cascade simulator
Model complex systems modelling for 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
08Connections
Topics that rely on this method.
09Sector signature
The sector signature of complex systems modelling for health.
The sectors complex systems modelling for health touches and how strongly they depend on each other: what a study has to observe and model together.
Select corners · hover lines6 / 15 relations
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
10Stewardship
Who maintains the method.
Systems Science and Modelling stewards complex systems and agent-based modelling for the Consortium and reviews how it is applied across platforms.
11Methods and evidence
How complex systems modelling for 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.
Model, equations and assumptions
The model structure used for complex systems modelling for health, the assumptions it makes and how each is tested.
Risk of element i
Ri = f(Hi, Ei, Vi, Ci)hazard, exposure, vulnerability and coping capacityExpected annual loss
EAL = ∑k pk · Lkover events k with annual probability pkCoupled systems
xt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error| Assumption | Why it is made | How it is tested |
|---|---|---|
| Hazard and exposure are independent within an event | Keeps the loss integral tractable | Compare with a copula fit on joint records |
| Couplings are linear near the operating point | Lets shocks propagate through A | Perturb beyond the linear range and compare |
| Vulnerability curves transfer between places | Local curves are rarely available | Validate against observed losses where they exist |
- Inputs
- versioned datasets with provenance
- Outputs
- distributions, never single numbers
- Code
- released with the model card and tests
- Reproduction
- same data, code, environment and seed
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
12Connected
Where this page leads across the Consortium.
13Questions
Questions and answers.
What are the limits of complex systems and agent-based modelling in this field?
Many plausible models can reproduce the same patterns. Calibration and validation data are often limited.
How are results checked?
Models are evaluated by matching multiple stylised facts and patterns at different scales. Sensitivity and robustness analysis test dependence on assumptions.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
ReportsContribute
Contribute evidence from this page.
Reports is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
GCRIHealth NexusGlobal Sector Risk and ResilienceCountry Systems Risk and ResilienceEvidence Brief
- EmailGateway GW-GCRISend it from this pageEvidence, a correction, a case, a method, a review, a translation or profile input. It arrives tagged with this page, so the editor who owns it receives it.
- ZenodoOpen depositDeposit it and submit to the communityPublish your own record on Zenodo with your ORCID iD, then submit it to the Reports community there. Curators review it; the DOI stays yours.
- ReportsGCRISee every way to contributeThe contribution types, how each is reviewed, what credit you receive, the licence and what is never accepted.
Never send personal data, sensitive infrastructure detail or anything you have no right to share. A contribution is reviewed before use; sending it confers no authorship or editorial rights, and sponsorship never buys a finding.
Health Nexus
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Information and enquiries are open today. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.
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