GCRI · Biotechnology and Biosecurity
Lab biosafety and biosecurity
Laboratories that work with biological agents protect their staff, communities and the environment through biosafety, and protect materials and knowledge through biosecurity. This topic, studied at Biotechnology and Biosecurity, a platform of The Global Centre for Risk and Innovation (GCRI), examines research environment controls and their governance: risk assessment, containment, training, culture and oversight, and how these vary across a growing and diverse global network of laboratories.
Lab biosafety and biosecurity on the record.
- PublisherThe Global Centre for Risk and Innovation
- FieldHealth and biosecurity, Food and agriculture
What experts ask.
- How should laboratories assess the risks of their work and match controls to them?
- What role do organisational culture, training and reporting play compared with equipment?
- How is oversight organised for high-containment facilities, and where are the gaps?
- How do new kinds of laboratory, including automated and community laboratories, fit existing frameworks?
- How can incidents and near misses be reported and learned from?
Environmental exposure in West Africa.
Population density, daytime surface heat and haze over West 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 NDVI (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 greenness index over the 8 days to the date shown; an indicator of crop and pasture condition, not a yield estimate or a food security warning.
- 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 West 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
- Vegetation greenness (NDVI), rolling 8 days: MODIS Terra NDVI, NASA LANCE · 13 Oct 2026
Laboratory biorisk management protects people and nature in every domain.
- HealthCouplingClinical and public health labs handle infectious samples daily.What to watchRisk assessment practice
- FoodCouplingAgricultural labs handle plant and animal pathogens.What to watchContainment practice
- BiodiversityCouplingEscapes of organisms can affect ecosystems.What to watchIncident reporting
Practice studies are designed for the Evidence Brief series. The WHO Laboratory Biosafety Manual (fourth edition, 2020) moved to a risk-based approach, and ISO 35001 (2019) sets biorisk management systems. GCRI inspects no laboratory.
- Biosafety lessons from incidents.
- Training evaluations.
Oversight and capacity register.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Oversight and capacity register
Lab biosafety and biosecurity · National committee · Review year 3
- Institutions with a committee, %58Watch
- Staff trained, %70Normal
- Reviews completed39this year
- Incident reports filed6Info
Institutional oversight
| Institution A | Active | Q3 | 89 | Current |
| Research campus | Active | Q2 | 80 | Audit due |
| Biomanufacturing site | Forming | none | 56 | Support |
| Diagnostic network | Active | Q3 | 97 | Current |
Scope note
- Covers
- governance, training, review capacity
- Excludes
- technical methods and agents
- Authority
- national oversight body
- Label
- SURVEY
Drivers and dynamics.
Modern biosafety practice is risk-based: laboratories assess the hazards of the agents and procedures they use, and select controls accordingly. Controls combine facility design, equipment, procedures, protective equipment and training. International guidance, notably from the World Health Organization, describes this approach. Biosecurity adds controls on access to materials, inventory, personnel reliability and information.
Most laboratory incidents involve human and organisational factors rather than equipment failure alone. A culture in which staff report errors and near misses without fear is central to learning. The number of high-containment laboratories has grown worldwide, while oversight arrangements differ widely between countries, and maintenance of facilities, equipment and skilled staff over time is a recurring challenge, particularly where funding is short-term. Process safety principles from process and industrial safety engineering inform this work.
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 West 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
- 5.42 per 1,000 a day
- Day of the peak
- 154
- Share ever infected
- 58%
- 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 | 0.02 | 0.0 |
| 34 | 0.31 | 0.2 |
| 51 | 0.96 | 1.4 |
| 68 | 1.77 | 3.7 |
| 85 | 2.65 | 7.5 |
| 102 | 3.37 | 12.7 |
| 119 | 4.11 | 19.1 |
| 136 | 4.94 | 26.8 |
| 153 | 5.42 | 35.8 |
| 170 | 4.81 | 44.6 |
| 187 | 3.18 | 51.4 |
| 204 | 1.65 | 55.3 |
| 221 | 0.74 | 57.2 |
| 238 | 0.32 | 58.1 |
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 Biotechnology and Biosecurity in Foundry
- Tested at Biotechnology and Biosecurity in Universe
- Codified in Health and Biosecurity in Standards
- Published in Biotechnology and Biosecurity in Reports
- Taught in Faculty of Health and Society in Academy
How it is studied.
- 01
Risk assessment frameworks
Structured methods guide laboratories in assessing and controlling the risks of their work.
- 02
Incident analysis
Studies of reported incidents and near misses identify recurring failure modes.
- 03
Governance comparison
Comparative research examines oversight regimes, licensing and reporting systems.
- 04
Limits
Incident reporting is incomplete, data on facilities is often confidential, and the effectiveness of controls is hard to measure directly.
Model lab biosafety and biosecurity as a system. Run the cascade.
Assembled from the Nexus Standards area profile Biological hazard standards: 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
Systems and couplings
- Biological hazard standards
- Health risk standards Cascade
- Food and agriculture risk standards Cascade
- Biodiversity and nature risk standards Common driver
- Health security and One Health Mechanism
Controls
NS-0Constitution and principlesNS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NRS-H41Hazard family: Animal-to-human spillover (NHS-1 health.zoonotic)NRS-H44Hazard family: Plant disease and pests (NHS-1 health.plant-health)NRS-H43Hazard family: Livestock and aquaculture epizootics (NHS-1 health.animal-health)NRS-H35Hazard family: Airborne and respiratory infection (NHS-1 health.respiratory)NRS-H40Hazard family: Blood-borne and sexually transmitted infection (NHS-1 health.bloodborne)NRS-H60Hazard family: Supply-chain and flow disruption (NHS-1 sys.supply-chain)
Observation
- WHO Disease Outbreak News (listed for reference)
- WAHIS animal health events (listed for reference)
- GBIF occurrence API and downloads (listed for reference)
- International Health Regulations capacity and health emergency preparedness
- Proportion of countries adopting relevant national legislation and adequately resourcing the prevention or control of invasive alien species
Example geography
- West Africa: 59 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
What it informs and who decides.
This evidence informs institutional biosafety policies, national oversight frameworks, training programmes and reporting systems. Institutional biosafety committees, laboratory directors and national authorities decide; GCRI does not inspect, certify or approve laboratories. Governance dialogue is convened by Health and Biosecurity Governance at The Global Risks Forum (GRF). Related topics are dual use and ethics, biological threats and synthetic biology; methods sit on molecular and life sciences and process and industrial safety engineering.
Published analysis on this topic is designed to carry a named subject reviewer, its references and the views of independent specialists, including biosafety professionals and those who disagree.
How lab biosafety and biosecurity 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.
Uncertainty bands
How far the outlook for lab biosafety and biosecurity could spread, shown as ranges rather than a single line.
- Central estimate
- median of the ensemble
- Ranges
- likely and very likely, in IPCC calibrated language
- Ensemble
- members, scenarios and initial conditions stated
- Read with
- the sensitivity of the result to each input
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.
Does the platform certify laboratories?
Is biosafety mainly about equipment?
Where can laboratories find guidance?
Who reviews coverage of this topic?
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