GCRI · Health Nexus
Methods and uncertainty
The methods Health Nexus relies on: their equations and assumptions, the data they need, how they are validated and the limits of what they can show.
Methods and uncertainty on the record.
- 01FieldsHealth and biosecurity
- 02STEM methods7 canonical methods applied
- 03Topics8 subject pages
- 04Counterparts2 platforms in shared fields
- 05PublisherThe Global Centre for Risk and Innovation
The methods in use.
- 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
- 03Probability and statisticsSurvey-weighted and small-area estimation · Expected baselines for excess mortalityApplied here
- 04Complex systems and agent-based modellingContact and mobility networks · Cascades across health and infrastructureApplied here
- 05Causal inference and impact evaluationEvaluating public health measures · Exposure-response for heat and airApplied here
- 06Data engineering, interoperability and stewardshipLinking health information systems · Privacy-preserving publicationApplied here
- 07Biotechnology platformsCountermeasure manufacturing capacity · Diagnostic readinessApplied here
Each method, its role in this field and how it is checked.
- EpidemiologyRole hereEpidemiology is the core science of this platform: it describes who becomes ill, where and when, identifies the determinants of disease, and evaluates what prevents it.Checks appliedSurveillance attribute review; Independent stream comparison; Case definition stability; Out-of-sample scoring
- Life sciencesRole hereMolecular and life sciences characterise the pathogens, hosts and vectors behind health threats: their genomes, how they evolve, how they resist drugs and how they move between species.Checks appliedExternal quality assessment; Sampling representativeness; Phylogenetic robustness
- Probability statisticsRole hereProbability and statistics turn incomplete, delayed and biased health data into estimates with stated uncertainty.Checks appliedDesign-based variance; Posterior predictive checks; Sensitivity to priors and baselines
- Complex systemsRole hereComplex 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.Checks appliedPattern-oriented validation; Global sensitivity analysis; Structural alternatives
- Causal inferenceRole hereCausal inference asks whether an intervention or exposure actually changed health outcomes, compared with what would otherwise have happened.Checks appliedPre-trend and placebo tests; Sensitivity to unmeasured confounding; Replication across settings
- Data engineeringRole hereData engineering, interoperability and stewardship decide whether health evidence can be combined at all.Checks appliedReconciliation against source; Schema and terminology validation; Disclosure review; Licence and consent audit
- BiotechnologyRole hereBiotechnology platforms determine how quickly diagnostics, vaccines and therapeutics can be developed, manufactured and delivered when a new threat appears.Checks appliedRegulatory status verification; Input dependency tracing; Biosecurity review
How the process runs.
Process
Methods and uncertainty: Source to evidence record
A source is registered and assessed, its record labelled and classified, and a Digital Evidence Passport carries provenance; a correction reaches every dependent record.
01 Intake
- 01Identify the sourceSource · The source stewardDiscovery is not ingestion. Source identification records the source class, steward, institutional role and jurisdiction.
- 03Register the sourceStepRegistration makes a discovered source a governed participant in the evidence lifecycle. It implies no endorsement or certification.
02 Assessment
- 04Jurisdiction and rights classificationGateRights classification identifies licence, consent, confidentiality and reuse limits. The result determines access and routing.
- 05Evidence quality assessmentStepQuality assessment decides whether a record is fit for a defined use. Quality is multidimensional and never a single score.
- 06Evidence labelStepEvery finding carries one of nine evidence labels. Missing data is shown as missing, never as zero.
03 Record
- 07Evidence classificationRecordClassification gives records lifecycle states, such as source-verified, public-safe or restricted. States can coexist and set evidence use.
- 08Source release and identifierRecord · NXS-GRX (canonical data, provenance and risk)A source release records rights, temporal coverage and source lineage. A UNI is allocated through a catalogue and never typed by hand.
- 09Digital Evidence PassportRecordThe passport records provenance, quality, rights, jurisdiction, permitted use and history, so evidence keeps its context when it travels.
04 Use and correction
- 02Correction reaches dependent recordsStep · NXS-GRX (canonical data, provenance and risk)A source correction propagates to dependent cuts and products without rewriting old results.
- 10Public-safe reviewGatePublic-safe review decides whether a record can be shared publicly. It is a distinct stage and is not validation.
Data protocols: evidence lifecycle · Data protocols: core data objects and evidence records · The Global Risks Index (NXS-GRX)Designed programme process, not a record of operation. No cycle has run and every programme is at service level R0. A gate is a programme decision, never a public-authority approval, certification or deployment authorisation. The data-protocol pages describe a design, not a running system.
Where uncertainty in health analysis comes from, and how it is shown.
| Source | Why it matters here | How it is reported |
|---|---|---|
| Case ascertainment | Reported cases depend on testing and care-seeking | Corrections for reporting, with assumptions stated |
| Transmission parameters | Estimated early in an outbreak from few data | Ranges, updated as data accrue |
| Behaviour | Contact patterns and compliance change during an epidemic | Scenarios rather than fixed parameters |
| Health-system capacity | Staffed beds differ from physical beds | Capacity ranges from facility data |
| Excess mortality baselines | Depend on the counterfactual model | Multiple baselines compared |
Health evidence is graded with GRADE where recommendations are concerned, and modelled projections are always labelled as scenarios.
How uncertainty is reported.
- 01
State assumptions
Each analysis lists its assumptions and the data it depends on.
- 02
Quantify where possible
Ranges and confidence are reported alongside central estimates.
- 03
Validate and compare
Models are checked against observations and, where possible, against other models.
- 04
Publish limits
What an analysis cannot show is stated beside what it can.
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.
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