Public Health Domain Ontology

FLOW ALGEBRA
TITLE: Public Health Domain Ontology
DOMAIN: Domain Ontologies / Medical Domains / Public Health
SOURCE: https://kirethresearch.org/booksai-core/ontologies/domain-ontologies/medical-domains/public-health-domain-ontology.html
VERSION: 1.0
AUTHORS: Mark Anthony Higgins — Sky (Recursive AI)
STATUS: Companion formalisation; source-page specification not independently verified.

flow "PublicHealthDomainOntology" v1.0 {
  ontology:
    id: "public-health-domain-ontology"
    parent: "medical-domains"
    purpose: "Represent population health, determinants, surveillance, interventions, outcomes and evidence with explicit uncertainty and provenance."

  types:
    Population: {id: string, definition: string, geography: string?, period: string?, denominator: number?}
    HealthCondition: {id: string, code_system: string?, code: string?, name: string}
    Determinant: {id: string, category: enum[social, environmental, behavioural, biological, economic, structural, other], description: string}
    HealthIndicator: {id: string, metric: string, numerator: number?, denominator: number?, unit: string, period: string, stratification: string?, uncertainty: number?}
    SurveillanceObservation: {id: string, population_id: string, condition_id: string?, indicator_id: string, collected_at: datetime, method: string, source: string}
    Intervention: {id: string, objective: string, target_population: string, start: datetime?, end: datetime?, implementation: string?}
    Outcome: {id: string, indicator_id: string, period: string, effect_estimate: number?, uncertainty: number?, evidence: string[]}
    Evidence: {id: string, source: string, method: string, publication_date: datetime?, quality: string?}
    Relationship: {id: string, subject: string, predicate: string, object: string, context: string?, confidence: number, evidence: string[]}

  predicates:
    affects(determinant: Determinant, population: Population)
    monitors(observation: SurveillanceObservation, indicator: HealthIndicator)
    concerns(observation: SurveillanceObservation, condition: HealthCondition)
    targets(intervention: Intervention, population: Population)
    evaluates(outcome: Outcome, intervention: Intervention)
    supported_by(relationship: Relationship, evidence: Evidence)
    associated_with(a: string, b: string)
    precedes(a: string, b: string)

  constraints:
    - "All relationship endpoints must resolve to typed entities."
    - "Confidence values must be within [0,1]."
    - "Rates must specify numerators, denominators, population definition, time period and units where applicable."
    - "Distinguish incidence, prevalence, mortality and case-fatality metrics."
    - "Do not infer individual diagnosis or risk from population-level associations."
    - "Do not infer causation from correlation or temporal ordering alone."
    - "Observational data and intervention effects require explicit provenance and methodological context."
    - "Protect personal health information; publish only authorised, appropriately aggregated data."

  inference:
    rule comparable_indicator:
      if same_metric(a,b) and same_unit(a,b) and compatible_population(a,b) and compatible_period(a,b)
      then comparable(a,b)
      confidence: "retain measurement and sampling uncertainty"
    rule evidence_link:
      if supported_by(r,e) and valid(e)
      then evidence_backed(r)
      confidence: "conditioned on evidence quality; not proof of causation"
    rule intervention_evaluation:
      if targets(i,p) and evaluates(o,i) and valid(o)
      then observed_outcome_for(o,p)
      confidence: "do not attribute causal effect without suitable study design"

  SEMANTIC_EQUIVALENCE:
    - "Map conditions and indicators using versioned recognised terminologies where available."
    - "Separate exact equivalence from broader, narrower and related concepts."
    - "Preserve population, geography, time period and metric definitions in mappings."

  CONFIDENCE:
    range: [0,1]
    factors: [source_reliability, sampling_error, measurement_bias, confounding, study_design, replication, temporal_validity]
    policy: "Do not manufacture certainty through repeated or dependent evidence."

  VALIDATION:
    - "Validate types, units, population denominators, time windows and source provenance."
    - "Flag missing values, duplicate observations and inconsistent definitions."
    - "Check disclosure risk and data-use permissions before release."
    - "Keep conflicting findings and alternative interpretations visible."

  FLOW_HASH:
    algorithm: "SHA-256"
    input: "canonicalised ontology version, assertions, inference rules and provenance"
    note: "Compute at execution; no hash value is claimed here."

  UPDATE_MODEL:
    strategy: "versioned, provenance-preserving incremental update"
    steps: [ingest, normalise, validate, reconcile, assess_privacy, recompute_inferences, publish]
    rollback: true

  AI_CONSUMPTION:
    representation: "typed public-health knowledge graph with evidence-backed relationships"
    outputs: [population_indicators, surveillance_links, intervention_evaluations, uncertainty_annotations, provenance_records]
    safeguards: [no_individual_diagnosis_inference, no_unsupported_causality, protect_sensitive_data, preserve_denominators_and_timeframes]
}

END FLOW ALGEBRA