VAST · Claude Sonnet 5 · C3

workflow vastopolis-outbreak-source-and-transmission-analysis template: define-unit → loop(characterise → visualise → assess → generate-knowledge → define-unit) → contextualise → characterise → contextualise → visualise → abstract → loop(define-unit → define-unit → visualise → assess) → generate-knowledge → loop(generate-knowledge → characterise → contextualise → visualise → abstract → assess → generate-knowledge) → generate-knowledge description: "Public-health investigation of a flu-like outbreak in Vastopolis: extract validated illness reports from geolocated microblogs, visualise their spatio-temporal spread across the city's 13 zones, localise a fine-grained outbreak origin via spatio-temporal clustering, and iteratively test person-to-person, airborne, waterborne, and point-source transmission hypotheses against weather, hydrography, road-network, and text evidence to guide resource deployment"

artifact D_microblogs : entities origin: given internal structure: elementary embedment: {set, space, time} features: - id: f_text value structure: atomic value type: text description: "Message text content" description: "Geolocated, timestamped microblog messages posted by residents over the outbreak period" artifact D_zones : entities origin: given internal structure: region embedment: space features: - id: f_population value structure: atomic value type: numeric description: "Resident population of the zone" description: "13 administrative city zones providing spatial context and population denominators" artifact D_water_bodies : entities origin: given internal structure: region embedment: space features: - id: f_water_type value structure: atomic value type: categorical description: "Water body type (river, lake, reservoir)" description: "Water bodies within the metropolitan area" artifact D_road_network : entities origin: given internal structure: formation embedment: space features: - id: f_road_type value structure: atomic value type: categorical description: "Road classification (highway, arterial, local)" description: "Highway/road network providing a connectivity structure for person-to-person diffusion analysis" artifact D_landmarks : entities origin: given internal structure: elementary embedment: space features: - id: f_category value structure: atomic value type: categorical description: "Landmark category (hospital, market, venue, other)" - id: f_name value structure: atomic value type: categorical description: "Landmark name" description: "Hospitals and other notable landmarks, used both as map context and as candidate point-source locations" artifact D_weather : entities origin: given internal structure: elementary embedment: time features: - id: f_wind_speed value structure: atomic value type: numeric description: "Daily wind speed" - id: f_wind_direction value structure: atomic value type: numeric description: "Daily prevailing wind direction (degrees)" description: "Daily city-wide weather observations over the outbreak period"

artifact S_extraction_criteria : specification origin: given representation form: "keyword list and classifier threshold" description: "Initial symptom-keyword list and confidence threshold for flagging microblog messages as flu-like illness reports" transform T_extract_reports : intent: define-unit manner: "keyword/symptom-based text classification" input: D_microblogs, S_extraction_criteria output: D_illness_reports actor: hybrid description: "Flag microblog messages plausibly reporting flu-like symptoms and extract them as illness-report entities" artifact D_illness_reports : entities internal structure: elementary embedment: {set, space, time} features: - id: f_keywords value structure: list value type: categorical description: "Matched symptom keywords" - id: f_confidence value structure: atomic value type: numeric description: "Classifier confidence that the message reports genuine illness" description: "Geolocated, timestamped candidate illness reports derived from the microblog stream" loop L_extraction_refinement: purpose: "Refine symptom-keyword/classifier criteria so that extracted reports neither miss genuine cases nor include irrelevant chatter" until: "The analyst judges the report set to be a sufficiently accurate and complete representation of flu-like illness reports" body: transform T_summarise_reports : intent: characterise manner: "compute daily report counts and a stratified sample of flagged and excluded messages" input: D_illness_reports, D_microblogs output: F_report_sample actor: machine description: "Prepare daily counts and a sample of borderline classifications for visual inspection" artifact F_report_sample : feature(D_illness_reports) value structure: vector value type: {numeric, text} description: "Daily report counts and sampled flagged/excluded message texts with matched keywords highlighted" transform T_visualise_sample : intent: visualise manner: "map and chronological list of sampled messages" input: F_report_sample, D_illness_reports output: V_report_sample actor: machine description: "Display sampled messages on the map and in a chronological list, with matched keywords highlighted" artifact V_report_sample : visualisation(D_illness_reports, F_report_sample) layout: "linked map and chronological message list" form: "point markers on map; text list with highlighted keywords" encoding: "marker position from message coordinates; marker colour from classifier confidence; list highlighting from matched keyword terms" description: "Interface supporting inspection of extraction quality in geographic and textual context" transform T_assess_extraction : intent: assess manner: "evaluate completeness and precision of the extracted report set against domain knowledge of symptom language" input: V_report_sample output: K_extraction_quality actor: human description: "Judge whether current criteria adequately separate genuine illness reports from noise" artifact K_extraction_quality : knowledge(D_illness_reports) representation form: "quality judgment" description: "Assessment of extraction precision/recall and whether criteria adjustment is needed" if K_extraction_quality indicates refinement needed: then: transform T_revise_criteria : intent: generate-knowledge manner: "adjust keyword list, threshold, or exclusion terms" input: K_extraction_quality, S_extraction_criteria output: S_extraction_criteria_r actor: human description: "Revise extraction criteria based on observed errors" artifact S_extraction_criteria_r : specification representation form: "keyword list and classifier threshold" description: "Updated extraction criteria" transform T_reextract_reports : intent: define-unit manner: "keyword/symptom-based text classification, re-applied" input: D_microblogs, S_extraction_criteria_r output: D_illness_reports_r actor: hybrid description: "Re-extract illness reports under revised criteria" artifact D_illness_reports_r : entities internal structure: elementary embedment: {set, space, time} features: - id: f_keywords value structure: list value type: categorical description: "Matched symptom keywords" - id: f_confidence value structure: atomic value type: numeric description: "Classifier confidence" description: "Revised illness-report set" assign: S_extraction_criteria := S_extraction_criteria_r D_illness_reports := D_illness_reports_r else: exit loop L_extraction_refinement end loop L_extraction_refinement

transform T_assign_zone : intent: contextualise manner: "map-based, point-in-polygon assignment" input: D_illness_reports, D_zones output: A_reports_zone actor: machine description: "Assign each illness report to its containing city zone" artifact A_reports_zone : arrangement(D_illness_reports) context: D_zones principle: "point-in-polygon assignment to zone polygons" description: "Zone membership of every illness report" transform T_compute_daily_incidence : intent: characterise manner: "aggregate per-zone, per-day counts and normalise by population" input: D_illness_reports, A_reports_zone, D_zones output: F_daily_incidence actor: machine description: "Compute a population-normalised daily incidence rate per zone" artifact F_daily_incidence : feature(D_zones) value structure: matrix value type: numeric representation form: "time series per zone" description: "Daily, population-normalised illness-report incidence per zone over the outbreak period" transform T_arrange_spread : intent: contextualise manner: "map-based arrangement with daily temporal sequencing" input: D_zones, F_daily_incidence, D_illness_reports output: A_spread actor: machine description: "Arrange zones geographically and sequence them by day to support a small-multiples spread overview" artifact A_spread : arrangement(D_illness_reports) context: D_zones principle: "geographic zone positions crossed with daily temporal sequencing" description: "Spatio-temporal arrangement of illness reports and zone-level incidence" transform T_visualise_spread : intent: visualise manner: "small-multiples choropleth map sequence with point overlay" input: A_spread, F_daily_incidence, D_illness_reports output: V_spread_overview actor: machine description: "Display one map panel per day showing zone incidence and individual reports" artifact V_spread_overview : visualisation(A_spread, F_daily_incidence, D_illness_reports) layout: "chronologically ordered sequence of small map panels" form: "choropleth zone polygons with overlaid point markers" encoding: "zone fill intensity from normalised daily incidence; point position from report coordinates; point colour/size from symptom confidence" description: "Overview enabling visual tracing of the outbreak's spatial and temporal progression" transform T_read_spread : intent: abstract manner: "perception-and-interpretation of spatial and temporal progression" input: V_spread_overview output: P_spread_pattern actor: human description: "Identify the general spread trajectory and the earliest affected zone(s)/day(s) as candidates for the outbreak origin" artifact P_spread_pattern : pattern(D_illness_reports, F_daily_incidence) representation form: "described spread trajectory" description: "General direction/shape of spread (e.g. radiating outward, corridor-following) and candidate origin zone/day" artifact S_origin_clustering : specification origin: given representation form: "parameter settings" description: "Initial spatial radius, temporal window, and minimum point count for density-based clustering of the earliest reports" loop L_origin_localisation: purpose: "Isolate a compact, finer-than-zone spatio-temporal cluster among the earliest reports that plausibly marks the outbreak's point of origin" until: "A stable, spatially compact, temporally coherent origin cluster has been identified and is plausible given nearby landmarks" body: transform T_select_early_reports : intent: define-unit manner: "filter to the earliest days indicated by P_spread_pattern" input: D_illness_reports, P_spread_pattern output: D_early_reports actor: human description: "Restrict analysis to reports from the earliest phase of the outbreak" artifact D_early_reports : entities internal structure: elementary embedment: {set, space, time} description: "Illness reports from the earliest outbreak days" transform T_cluster_origin : intent: define-unit manner: "density-based spatio-temporal clustering" input: D_early_reports, S_origin_clustering output: D_origin_clusters, F_origin_labels actor: machine description: "Group early reports concentrated in space and time into candidate origin clusters" artifact D_origin_clusters : entities internal structure: group/cluster embedment: {space, time} features: - id: f_cluster_size value structure: atomic value type: numeric description: "Number of reports in the cluster" description: "Candidate compact origin clusters among early reports" artifact F_origin_labels : feature(D_early_reports) value structure: atomic value type: categorical description: "Origin-cluster membership or noise designation" transform T_visualise_origin : intent: visualise manner: "detailed local map with landmark and road overlay" input: D_origin_clusters, F_origin_labels, D_landmarks, D_road_network output: V_origin_map actor: machine description: "Show candidate origin clusters against landmarks, hospitals, and roads for plausibility checking" artifact V_origin_map : visualisation(D_origin_clusters, F_origin_labels, D_landmarks) layout: "detailed local map" form: "coloured cluster points; landmark/hospital icons; road lines" encoding: "point colour from cluster membership; icon shape from landmark category; line style from road type" description: "Local map supporting assessment of cluster coherence and plausibility" transform T_assess_origin : intent: assess manner: "evaluate cluster compactness, coherence, and plausibility" input: V_origin_map, D_origin_clusters output: K_origin_quality actor: human description: "Judge whether a candidate cluster is a satisfactory origin, or whether clustering parameters need adjustment" artifact K_origin_quality : knowledge(D_origin_clusters) representation form: "quality judgment" description: "Assessment of origin-cluster compactness, coherence, and plausibility" if K_origin_quality indicates satisfactory origin cluster: then: exit loop L_origin_localisation else: transform T_adjust_origin_params : intent: generate-knowledge manner: "adjust spatial radius, temporal window, or minimum point count" input: K_origin_quality, S_origin_clustering output: S_origin_clustering_r actor: human description: "Revise clustering parameters based on assessment" artifact S_origin_clustering_r : specification representation form: "parameter settings" description: "Updated clustering parameters" assign: S_origin_clustering := S_origin_clustering_r end loop L_origin_localisation transform T_finalise_origin : intent: generate-knowledge manner: "formulate-statements" input: D_origin_clusters, K_origin_quality, D_landmarks output: K_origin actor: human description: "State the confirmed outbreak origin location and time, noting any co-located landmark or facility" artifact K_origin : knowledge(D_origin_clusters) representation form: "statements" description: "Confirmed origin location, onset time, and any co-located landmark relevant to the point-source hypothesis"

artifact S_candidate_hypotheses : specification origin: given representation form: "ordered list of hypothesis types" description: "The four candidate transmission modes to examine: person-to-person, airborne, waterborne, point-source/other" artifact K_evidence_log : knowledge(D_illness_reports) representation form: "accumulated hypothesis assessments" description: "Initially empty; accumulates the evidence for/against each candidate transmission-mode hypothesis across iterations" loop L_transmission_analysis: purpose: "Iteratively test alternative transmission-mode hypotheses against the observed spread pattern, weather, hydrography, road network, and text evidence until the evidence for the most plausible mode(s) is judged sufficiently strong" until: "All relevant hypotheses have been evaluated and the evidence log supports one or more transmission modes with sufficient confidence" body: transform T_formulate_hypothesis : intent: generate-knowledge manner: "select the next untested or inconclusive candidate transmission mode and specify the contextual variables and evidence computation needed to test it" input: S_candidate_hypotheses, K_evidence_log, K_origin output: S_hypothesis actor: human description: "Choose which transmission mode to test next and how" artifact S_hypothesis : specification representation form: "hypothesis type and evidence-computation specification" description: "Current transmission-mode hypothesis and the contextual data/method needed to test it" transform T_compute_hypothesis_evidence : intent: characterise manner: "compute hypothesis-specific evidence: spatial contiguity and road-network proximity for person-to-person; lagged correlation between wind direction/speed and directional case spread for airborne; distance-to-water-body and downstream clustering for waterborne; shared-venue/landmark text mining among early cases for point-source" input: D_illness_reports, A_reports_zone, D_zones, D_weather, D_water_bodies, D_road_network, D_landmarks, S_hypothesis output: F_hypothesis_evidence actor: machine description: "Compute the evidence variables relevant to the current hypothesis" artifact F_hypothesis_evidence : feature(D_illness_reports) value structure: vector value type: numeric description: "Per-report or per-zone evidence values specific to the current hypothesis (e.g. contiguity metric, lagged wind correlation, water-distance, venue co-mention count)" transform T_arrange_hypothesis_view : intent: contextualise manner: "overlay hypothesis-relevant contextual layer with the spread map and timeline" input: F_hypothesis_evidence, A_spread, D_water_bodies, D_road_network, D_weather, D_landmarks output: A_hypothesis actor: machine description: "Combine the case-spread arrangement with the contextual layer (wind vectors, water bodies, roads, or venues) relevant to the current hypothesis" artifact A_hypothesis : arrangement(D_illness_reports) context: S_hypothesis principle: "spread map and timeline overlaid with the hypothesis-relevant contextual layer (wind field, hydrography, road network, or venue markers)" description: "Hypothesis-specific spatio-temporal arrangement for visual comparison" transform T_visualise_hypothesis : intent: visualise manner: "coordinated map overlay and correlation/time-series plot" input: A_hypothesis, F_hypothesis_evidence output: V_hypothesis actor: machine description: "Display the spread pattern against the contextual layer and any derived correlation/time-series evidence" artifact V_hypothesis : visualisation(A_hypothesis, F_hypothesis_evidence) layout: "map panel with contextual overlay, linked to a correlation or time-series panel" form: "map overlay (vectors/polygons/lines/markers as relevant); scatter or line chart for correlation/lag evidence" encoding: "overlay symbol/colour from contextual layer type; chart axes from hypothesis evidence values" description: "Coordinated view supporting visual judgment of alignment between spread pattern and candidate factor" transform T_interpret_hypothesis : intent: abstract manner: "perception-and-interpretation of spatial alignment and correlation strength" input: V_hypothesis output: P_hypothesis_pattern actor: human description: "Interpret whether the spread pattern visually aligns with, or correlates with, the candidate factor" artifact P_hypothesis_pattern : pattern(D_illness_reports, F_hypothesis_evidence) representation form: "described alignment/correlation evidence" description: "Observed degree and direction of alignment between the spread pattern and the candidate transmission factor" transform T_assess_hypothesis : intent: assess manner: "judge whether evidence supports, contradicts, or is inconclusive for the hypothesis" input: P_hypothesis_pattern, V_hypothesis output: K_hypothesis_assessment actor: human description: "Evaluative judgment on the current hypothesis" artifact K_hypothesis_assessment : knowledge(P_hypothesis_pattern) representation form: "quality judgment" description: "Support/contradiction/inconclusive verdict for the current transmission-mode hypothesis, with rationale" transform T_log_evidence : intent: generate-knowledge manner: "append the current assessment to the accumulated evidence log" input: K_hypothesis_assessment, K_evidence_log output: K_evidence_log_r actor: human description: "Update the running record of tested hypotheses and their evidentiary support" artifact K_evidence_log_r : knowledge(D_illness_reports) representation form: "accumulated hypothesis assessments" description: "Evidence log extended with the current hypothesis's assessment" assign: K_evidence_log := K_evidence_log_r if K_evidence_log indicates sufficient evidence has been gathered for one or more transmission modes and no untested plausible hypotheses remain: then: exit loop L_transmission_analysis else: transform T_select_next_hypothesis : intent: generate-knowledge manner: "choose the next candidate hypothesis to test, or refine the current one, based on inconclusive or contradicted evidence" input: K_evidence_log_r, S_candidate_hypotheses output: S_candidate_hypotheses_r actor: human description: "Update the remaining hypothesis agenda" artifact S_candidate_hypotheses_r : specification representation form: "revised ordered list of remaining hypotheses" description: "Updated hypothesis agenda for the next iteration" assign: S_candidate_hypotheses := S_candidate_hypotheses_r end loop L_transmission_analysis

transform T_synthesise : intent: generate-knowledge manner: "formulate-statements" input: K_origin, P_spread_pattern, K_evidence_log output: K_findings actor: human description: "Integrate the localised origin, characterised spread pattern, and accumulated transmission-mode evidence into an actionable outbreak characterisation" artifact K_findings : knowledge(K_origin, K_evidence_log) representation form: "statements and recommendations" description: "Final characterisation of the outbreak: confirmed origin location and time; most plausible transmission mode(s) with supporting evidence and confidence; and recommended emergency resource deployment (e.g. contact tracing/quarantine, air-quality intervention, water-supply investigation, or facility closure)"