VAST · GPT-5.6-Terra · C2

workflow vastopolis-flu-outbreak-investigation template: loop(characterise → visualise → abstract → assess) → define-unit → define-unit → characterise → contextualise → visualise → abstract → loop(build-model → characterise → visualise → abstract → assess) → generate-knowledge description: "Human-in-the-loop investigation of a suspected flu-like outbreak using geolocated microblogs as a syndromic surveillance proxy. The workflow identifies candidate origins and compares person-to-person, airborne, water-associated, and other local-source explanations while preserving uncertainty."

artifact D_posts : entities origin: given internal structure: elementary embedment: {space, time} features: - id: timestamp value structure: atomic value type: temporal description: "Time at which the message was posted" - id: location value structure: atomic value type: spatial description: "GPS coordinate associated with the mobile device" - id: text value structure: atomic value type: text description: "Microblog message content" description: "Geolocated microblog messages posted during the suspected outbreak period" artifact D_zones : entities origin: given internal structure: region embedment: space features: - id: zone_id value structure: atomic value type: reference description: "Identifier of one of the 13 metropolitan zones" - id: population value structure: atomic value type: numeric description: "Resident population of the zone" - id: geometry value structure: atomic value type: spatial description: "Zone boundary geometry" description: "Metropolitan zones used for population-normalised aggregation and emergency-resource planning" artifact D_geography : entities origin: given internal structure: elementary embedment: space features: - id: geographic_feature_type value structure: atomic value type: categorical description: "Highway, hospital, landmark, water body, or other map feature" - id: geometry value structure: atomic value type: spatial description: "Spatial geometry of the geographic feature" description: "Reference map features, including highways, hospitals, landmarks, and water bodies" artifact D_weather : entities origin: given internal structure: elementary embedment: time features: - id: wind_speed value structure: atomic value type: numeric description: "Observed daily wind speed" - id: wind_direction value structure: atomic value type: spatial description: "Observed daily wind direction" description: "Daily weather observations during the investigation period" artifact K_epidemiology : knowledge(D_posts, D_zones, D_weather) origin: given representation form: "domain rules and expected signatures" description: "Public-health knowledge about flu-like symptom language, plausible reporting biases, plausible transmission signatures, and the distinction between syndromic signals and confirmed infections" artifact S_relevance : specification origin: given representation form: "method choice and thresholds" description: "Initial rules and model settings for identifying posts likely to describe flu-like illness, including symptom terms, negation handling, language variants, confidence threshold, and exclusion criteria" artifact S_hypothesis : specification origin: given representation form: "comparative modelling configuration" description: "Initial temporal lags, spatial neighbourhood definitions, candidate transmission mechanisms, uncertainty treatment, and criteria for comparing person-to-person, airborne, water-associated, and local-source explanations"

loop L_relevance_refinement: purpose: "Develop an interpretable and sufficiently reliable syndromic signal from unstructured microblog text" until: "The analyst judges that illness-related messages are adequately distinguished from unrelated, historical, sarcastic, and non-local posts" body: transform T_score_posts : intent: characterise manner: "text encoding and illness relevance scoring" input: D_posts, K_epidemiology, S_relevance output: F_illness_score actor: hybrid description: "Extract symptom expressions, illness mentions, negation, severity language, and relevance confidence from post text" artifact F_illness_score : feature(D_posts) value structure: vector value type: {numeric, categorical} representation form: "illness relevance score and linguistic indicators" description: "Per-post probability of describing a current local flu-like illness and associated symptom-language indicators" transform T_visualise_posts : intent: visualise manner: "linked text and map inspection" input: D_posts, F_illness_score output: V_post_relevance actor: machine description: "Show sampled messages, relevance scores, locations, and posting times so the analyst can inspect false positives and omissions" artifact V_post_relevance : visualisation(D_posts, F_illness_score) layout: "linked message list, map, and time histogram" form: "text records, coloured map points, and bars" encoding: "colour indicates illness relevance score; map position comes from GPS coordinates; time histogram shows posting volume" description: "Interactive inspection view for validating the syndromic message-identification process" transform T_interpret_language : intent: abstract manner: "perception-and-interpretation" input: V_post_relevance, F_illness_score, K_epidemiology output: P_language_patterns actor: human description: "Identify meaningful symptom vocabulary, ambiguous phrases, reporting artefacts, and systematic false-positive message types" artifact P_language_patterns : pattern(D_posts, F_illness_score) representation form: "annotated linguistic categories" description: "Observed illness-language patterns and misleading message categories relevant to flu-like syndromic surveillance" transform T_assess_relevance : intent: assess manner: "judge classification adequacy" input: V_post_relevance, P_language_patterns, F_illness_score output: K_relevance_quality, S_relevance_r actor: human description: "Assess whether the relevance signal is sufficiently credible for aggregate surveillance and, if needed, formulate revised scoring rules or thresholds" artifact K_relevance_quality : knowledge(F_illness_score, V_post_relevance) representation form: "quality judgment" description: "Analyst judgment about the adequacy, bias, and limitations of the illness-message relevance classification" artifact S_relevance_r : specification representation form: "revised method choice and thresholds" description: "Updated linguistic rules, confidence threshold, or exclusion criteria for illness-message identification" if K_relevance_quality indicates refinement needed: then: assign: S_relevance := S_relevance_r else: exit loop L_relevance_refinement end loop L_relevance_refinement

transform T_select_illness_posts : intent: define-unit manner: "filter by illness relevance" input: D_posts, F_illness_score, S_relevance output: D_illness_posts actor: hybrid description: "Retain posts judged sufficiently likely to describe a current flu-like illness" artifact D_illness_posts : entities internal structure: elementary embedment: {space, time} features: - id: illness_confidence value structure: atomic value type: numeric description: "Confidence that the post describes flu-like illness" - id: symptom_terms value structure: list value type: text description: "Extracted symptom and illness expressions" description: "Filtered geolocated posts forming the syndromic surveillance signal for the outbreak investigation" transform T_aggregate_zone_days : intent: define-unit manner: "space-time aggregation by zone and day" input: D_illness_posts, D_zones output: D_zone_days actor: machine description: "Aggregate illness-related posts into daily episodes for each city zone, retaining constituent messages for drill-down inspection" artifact D_zone_days : entities internal structure: episode embedment: {space, time} features: - id: zone_id value structure: atomic value type: reference description: "Zone represented by the zone-day episode" - id: date value structure: atomic value type: temporal description: "Calendar day represented by the episode" description: "Zone-by-day syndromic surveillance episodes containing illness-related microblog messages" transform T_characterise_outbreak : intent: characterise manner: "population-normalised outbreak profiling" input: D_zone_days, D_zones output: F_outbreak_signal actor: machine description: "Compute illness-post counts, population-normalised rates, temporal changes, excess activity, and uncertainty caused by low volume" artifact F_outbreak_signal : feature(D_zone_days) value structure: vector value type: numeric representation form: "daily syndromic incidence profile" description: "Counts, rates per population, growth measures, anomaly scores, and uncertainty indicators for each zone-day" transform T_characterise_context : intent: characterise manner: "derive environmental and propagation relations" input: D_zone_days, D_zones, D_geography, D_weather output: F_exposure_context, F_propagation_relations actor: machine description: "Derive zone-day descriptors and lagged relations involving population density, spatial adjacency, highways, hospitals, water proximity, wind direction, and wind speed" artifact F_exposure_context : feature(D_zone_days) value structure: vector value type: {numeric, categorical} description: "Population, geographic, weather, and infrastructure context associated with each zone-day" artifact F_propagation_relations : feature(D_zone_days) value structure: relational configuration representation form: "directed lagged zone-day relation graph" description: "Candidate propagation relations between zone-days based on adjacency, transport connectivity, wind alignment, and water proximity"

transform T_arrange_zone_days : intent: contextualise manner: "map-based" input: D_zone_days, D_zones output: A_zone_map actor: machine description: "Place each zone-day episode in its corresponding metropolitan zone so time-filtered outbreak maps can be inspected" artifact A_zone_map : arrangement(D_zone_days) context: D_zones principle: "zone identifier mapped to zone geometry" description: "Map arrangement of zone-day outbreak episodes" transform T_visualise_situation : intent: visualise manner: "coordinated spatiotemporal outbreak display" input: A_zone_map, F_outbreak_signal, F_exposure_context, D_geography, D_weather output: V_outbreak_situation actor: machine description: "Display animated and time-linked maps, zone time series, wind vectors, population-normalised rates, highways, hospitals, and water bodies" artifact V_outbreak_situation : visualisation(A_zone_map, F_outbreak_signal, F_exposure_context, D_geography, D_weather) layout: "linked metropolitan map, zone time series, and temporal overview" form: "choropleth regions, proportional symbols, line graphs, and wind vectors" encoding: "zone colour and symbol size show population-normalised illness activity; time controls select dates; overlays show geographic features and observed wind direction" description: "Interactive situational-awareness display for locating early sustained activity and visually inspecting possible spread pathways" transform T_abstract_observed_patterns : intent: abstract manner: "perception-and-interpretation" input: V_outbreak_situation, F_outbreak_signal, F_propagation_relations output: P_observed_diffusion actor: human description: "Identify candidate source zones, timing of sustained excess, wave-like spread, spatial clusters, anomalies, and patterns inconsistent with a single simple outbreak explanation" artifact P_observed_diffusion : pattern(D_zone_days, F_outbreak_signal) representation form: "candidate foci and spatiotemporal pattern descriptions" description: "Analyst-identified candidate origin areas and observed diffusion structures, explicitly distinguished from confirmed sources"

loop L_hypothesis_comparison: purpose: "Compare plausible transmission mechanisms and determine whether the available evidence supports a defensible operational conclusion" until: "A best-supported explanation, or a justified conclusion that the data cannot distinguish mechanisms, has been reached with documented uncertainty" body: transform T_build_mechanism_models : intent: build-model manner: "fit competing spatiotemporal transmission models" input: F_outbreak_signal, F_exposure_context, F_propagation_relations, P_observed_diffusion, K_epidemiology, S_hypothesis output: M_mechanism_models actor: hybrid description: "Fit and compare models representing person-to-person diffusion, wind-associated airborne spread, water-associated clustering, and localized or other point-source mechanisms" artifact M_mechanism_models : model(F_outbreak_signal, F_exposure_context, F_propagation_relations) model type: "comparative spatiotemporal explanatory models" representation form: "set of competing lagged diffusion and source models" description: "Alternative formal explanations of observed zone-day illness activity; model comparison measures evidence rather than proving a causal transmission route" transform T_characterise_model_evidence : intent: characterise manner: "compute model fit and diagnostic evidence" input: D_zone_days, F_outbreak_signal, F_exposure_context, F_propagation_relations, M_mechanism_models output: F_hypothesis_evidence actor: machine description: "Calculate comparative fit, residuals, uncertainty, predicted propagation, and local evidence for each transmission hypothesis" artifact F_hypothesis_evidence : feature(D_zone_days) value structure: vector value type: numeric representation form: "hypothesis fit, residual, and uncertainty profiles" description: "Zone-day evidence indicating how well competing explanations account for observed syndromic activity" transform T_visualise_hypothesis_evidence : intent: visualise manner: "comparative model diagnostics display" input: A_zone_map, F_outbreak_signal, F_hypothesis_evidence, M_mechanism_models, D_geography, D_weather output: V_hypothesis_evidence actor: machine description: "Compare observed and predicted spread, residual maps, temporal lags, downwind alignment, water proximity, and transport connectivity in coordinated views" artifact V_hypothesis_evidence : visualisation(A_zone_map, F_outbreak_signal, F_hypothesis_evidence, M_mechanism_models, D_geography, D_weather) layout: "linked observed-versus-predicted maps, timelines, and hypothesis comparison panels" form: "choropleths, flow arrows, residual marks, line graphs, and comparative score plots" encoding: "map panels show observed and model-predicted zone activity; arrows show candidate propagation relations; colour and residual marks show local model agreement or disagreement" description: "Interactive evidence display used to inspect whether apparent transmission signatures are coherent, robust, and plausible" transform T_interpret_mechanism_evidence : intent: abstract manner: "perception-and-interpretation" input: V_hypothesis_evidence, F_hypothesis_evidence, K_epidemiology output: P_mechanism_evidence actor: human description: "Interpret model evidence in public-health context; identify support, contradictions, confounding, and uncertainty for each candidate transmission mechanism" artifact P_mechanism_evidence : pattern(M_mechanism_models, F_hypothesis_evidence) representation form: "ranked mechanism evidence statements" description: "Interpreted regularities supporting or weakening person-to-person, airborne, water-associated, or local-source explanations" transform T_assess_hypotheses : intent: assess manner: "evaluate explanatory adequacy and operational usefulness" input: V_hypothesis_evidence, P_mechanism_evidence, M_mechanism_models, K_epidemiology output: K_hypothesis_assessment, S_hypothesis_r actor: human description: "Judge whether model assumptions, evidence, uncertainty, and visual explanations are adequate for a response decision; formulate revised lags, constraints, or comparison settings if needed" artifact K_hypothesis_assessment : knowledge(M_mechanism_models, P_mechanism_evidence) representation form: "quality judgment and uncertainty assessment" description: "Assessment of whether the evidence adequately distinguishes mechanisms, identifies a plausible candidate source, and supports emergency action" artifact S_hypothesis_r : specification representation form: "revised comparative modelling configuration" description: "Updated temporal lags, spatial relations, constraints, or robustness checks required for another hypothesis-comparison cycle" if K_hypothesis_assessment indicates further comparison needed: then: assign: S_hypothesis := S_hypothesis_r else: exit loop L_hypothesis_comparison end loop L_hypothesis_comparison

transform T_generate_response_knowledge : intent: generate-knowledge manner: "formulate-statements and recommendations" input: P_observed_diffusion, P_mechanism_evidence, K_hypothesis_assessment, K_relevance_quality, M_mechanism_models, D_zones, D_geography output: K_outbreak_assessment, K_response_recommendations, K_confirmation_data_needs actor: human description: "Formulate a public-health assessment that states candidate origin zones, ranked transmission explanations, confidence and caveats, recommended deployment actions, and the additional evidence needed to validate the conclusions" artifact K_outbreak_assessment : knowledge(P_observed_diffusion, P_mechanism_evidence, M_mechanism_models) representation form: "qualified situational assessment" description: "Candidate outbreak origin area or areas, timing of emergence, ranked transmission explanations, supporting evidence, contradictory evidence, and explicit uncertainty" artifact K_response_recommendations : knowledge(K_outbreak_assessment, D_zones, D_geography) representation form: "prioritised emergency-response recommendations" description: "Recommended deployment of testing, clinical capacity, communications, environmental investigation, and surveillance resources conditional on the best-supported mechanism and affected zones" artifact K_confirmation_data_needs : knowledge(K_outbreak_assessment) representation form: "data-collection recommendations" description: "Required confirmation data, such as confirmed case and death line lists, laboratory results, contact information, mobility data, water-quality samples, water-network information, and environmental sampling results"