workflow vastopolis-outbreak-origin-and-transmission template: characterise (extract-symptom-features) -> loop(assess -> characterise (refine-lexicon)) -> define-unit (filter-illness-events) -> define-unit (daily-episodes) -> characterise (daily-profile) -> define-unit (cluster-days) -> contextualise (calendar) -> visualise -> abstract -> generate-knowledge -> define-unit (derive-places) -> characterise (place-timeseries) -> characterise (per-capita-incidence) -> contextualise (map) -> visualise -> abstract -> characterise (zone-incidence) -> define-unit (cluster-zones) -> contextualise (map) -> visualise -> build-model (growth) -> characterise (residuals) -> visualise -> abstract -> characterise (downwind-alignment) -> loop(define-unit (lag-windows) -> characterise (cross-impact) -> visualise -> assess) -> generate-knowledge -> characterise (social-mentions) -> define-unit (social-network) -> visualise -> abstract -> visualise (water-overlay) -> abstract -> generate-knowledge (mechanism-checklist) -> generate-knowledge (final-report) description: "Visual analytics workflow for identifying the spatio-temporal origin of a flu-like outbreak in Vastopolis from geolocated microblogs, and for adjudicating among candidate transmission mechanisms (airborne, person-to-person, waterborne) using map, population, and wind data." # ============================================================================ # INPUT ARTIFACTS # ============================================================================ artifact D_microblogs : entities origin: given internal structure: elementary embedment: {space, time} features: - id: text_content value structure: atomic value type: text description: "Raw message text" description: "Geolocated microblog messages posted from mobile devices over several days, each with timestamp, GPS coordinates, and text content" artifact D_map_features : entities origin: given internal structure: elementary embedment: space features: - id: feature_type value structure: atomic value type: categorical description: "Category of map feature: highway, hospital, landmark, water body" description: "Static geographic features of the Vastopolis metropolitan area, used as map context" artifact D_zones : entities origin: given internal structure: region embedment: space features: - id: population value structure: atomic value type: numeric description: "Resident population of the zone" description: "13 city zones of Vastopolis with population statistics" artifact D_weather : entities origin: given internal structure: elementary embedment: time features: - id: wind_speed value structure: atomic value type: numeric description: "Daily wind speed" - id: wind_direction value structure: atomic value type: {numeric, spatial} description: "Daily wind direction (bearing)" description: "Daily weather observations including wind speed and direction" artifact D_calendar : entities origin: given internal structure: elementary embedment: time features: - id: date value structure: atomic value type: temporal description: "Calendar date" description: "Calendar days spanning the observation period, used as arrangement context" artifact S_symptom_lexicon : specification origin: given representation form: "keyword lexicon" description: "Flu-related symptom terms and severity indicators (fever, cough, vomiting, hospital, death, etc.) used to seed text extraction" # ============================================================================ # STAGE A: EXTRACT AND VALIDATE ILLNESS-REPORT EVENTS FROM TEXT # ============================================================================ transform T_extract_symptoms : intent: characterise manner: "extract (keyword/entity extraction of symptom and severity terms)" input: D_microblogs, S_symptom_lexicon output: F_symptom_features actor: machine description: "Extract symptom mentions, severity indicators (hospitalization, death), and embedded place references from message text" artifact F_symptom_features : feature(D_microblogs) value structure: vector value type: {categorical, ordinal, text} description: "Per-message symptom type(s), severity level, and any embedded place mentions" loop L_extraction_validation: purpose: "Validate and correct automatically extracted symptom/severity indicators against raw message text before trusting the derived event set" until: "Sampled false positive/negative rate in extracted indicators is acceptable to the analyst" body: transform T_assess_extraction : intent: assess manner: "sample and inspect raw text against extracted indicators" input: F_symptom_features, D_microblogs output: K_extraction_quality actor: human description: "Sample messages and check extracted symptom/severity labels against raw text for false positives (e.g., sarcastic 'sick of...') and false negatives" artifact K_extraction_quality : knowledge(F_symptom_features) representation form: "quality judgment" description: "Judgment of whether extraction lexicon/rules yield an acceptable false positive/negative rate" if K_extraction_quality indicates refinement needed: then: transform T_refine_lexicon : intent: characterise manner: "refine extraction lexicon/rules" input: D_microblogs, S_symptom_lexicon, K_extraction_quality output: F_symptom_features_r, S_symptom_lexicon_r actor: human description: "Adjust keyword lexicon or extraction rules based on observed errors and re-extract" artifact F_symptom_features_r : feature(D_microblogs) value structure: vector value type: {categorical, ordinal, text} description: "Updated per-message symptom/severity/place-mention indicators" artifact S_symptom_lexicon_r : specification representation form: "keyword lexicon" description: "Revised symptom lexicon" assign: F_symptom_features := F_symptom_features_r S_symptom_lexicon := S_symptom_lexicon_r else: exit loop L_extraction_validation end loop L_extraction_validation transform T_define_illness_events : intent: define-unit manner: "filter messages with validated symptom/severity indicators" input: D_microblogs, F_symptom_features output: D_illness_events actor: hybrid description: "Retain only messages carrying validated illness-related indicators as geolocated, time-stamped illness-report events" artifact D_illness_events : entities internal structure: elementary embedment: {space, time} features: - id: symptom_type value structure: atomic value type: categorical description: "Symptom type(s) mentioned" - id: severity value structure: atomic value type: ordinal description: "Severity level (mild symptom vs. severe/hospitalization/death mention)" - id: raw_text value structure: atomic value type: text description: "Original message text retained for drill-down" description: "Geolocated, time-stamped illness-report events derived from validated microblog messages" # ============================================================================ # STAGE B: TEMPORAL OVERVIEW - ONSET DETECTION # ============================================================================ transform T_define_days : intent: define-unit manner: "time-partitioning into per-zone daily episodes" input: D_illness_events, D_zones output: D_days actor: machine description: "Aggregate illness events into hourly/6-hour bins and group into daily episodes per zone" artifact D_days : entities internal structure: episode embedment: {set, time} features: - id: hourly_counts value structure: vector value type: numeric description: "Hourly (or 6-hour) event counts within the day, per zone" description: "Per-zone daily episodes of illness-event counts" transform T_characterise_daily_profile : intent: characterise manner: "profile" input: D_days output: F_daily_profile actor: machine description: "Normalise hourly count vectors into comparable daily shape profiles" artifact F_daily_profile : feature(D_days) value structure: vector value type: numeric representation form: "daily count profile" description: "Normalized daily profile shape used for baseline-deviation clustering" transform T_cluster_days : intent: define-unit manner: "cluster-by-similarity of daily profile shape" input: D_days, F_daily_profile output: D_day_clusters, F_day_cluster_label actor: machine description: "Cluster days by profile shape so that days deviating from the pre-outbreak baseline pattern are separated out" artifact D_day_clusters : entities internal structure: group/cluster embedment: set description: "Groups of days with similar profile shape (baseline vs. deviating clusters)" artifact F_day_cluster_label : feature(D_days) value structure: atomic value type: categorical description: "Cluster membership label (baseline vs. deviating) for each day" transform T_arrange_calendar : intent: contextualise manner: "calendar-based" input: D_days, D_calendar output: A_calendar actor: machine description: "Position each per-zone daily episode in its corresponding calendar cell" artifact A_calendar : arrangement(D_days) context: D_calendar principle: "calendar(date, zone)" description: "Calendar-based arrangement of daily episodes per zone" transform T_vis_calendar : intent: visualise manner: "coloured-marks" input: A_calendar, F_day_cluster_label output: V_calendar_clusters actor: machine description: "Calendar view showing cluster membership (baseline vs. deviating) of each day, per zone" artifact V_calendar_clusters : visualisation(A_calendar, F_day_cluster_label) layout: "calendar grid per zone" form: "coloured marks" encoding: "layout: from A_calendar (day -> calendar cell); colour: from F_day_cluster_label" description: "Visual overview for spotting onset day(s) and earliest-deviating zones" transform T_abstract_onset : intent: abstract manner: "perception-and-interpretation" input: V_calendar_clusters output: P_onset_pattern actor: human description: "Identify the earliest day(s) and zone(s) whose profile breaks from the baseline pattern" artifact P_onset_pattern : pattern(D_days) representation form: "textual description" description: "Description of onset day(s) and earliest-deviating zone(s)" transform T_knowledge_onset : intent: generate-knowledge manner: "formulate-statements" input: P_onset_pattern output: K_onset_candidates actor: human description: "Formulate candidate outbreak onset time window and earliest zone(s) as working hypothesis for spatial origin" artifact K_onset_candidates : knowledge(D_days) representation form: "statements" description: "Candidate onset time window and earliest-affected zone(s), to be confirmed against place-level evidence" # ============================================================================ # STAGE C: SPATIAL HOTSPOT / SIGNIFICANT-PLACE EXTRACTION # ============================================================================ transform T_define_places : intent: define-unit manner: "derive-places (spatio-temporal density clustering)" input: D_illness_events output: D_places, F_place_membership actor: machine description: "Cluster illness events jointly by spatial and temporal proximity into candidate significant places, treating isolated events as noise" artifact D_places : entities internal structure: group/cluster embedment: space features: - id: place_extent value structure: atomic value type: spatial description: "Spatial hull/buffer delineating the place" - id: onset_time value structure: atomic value type: temporal description: "Earliest event time within the place cluster" description: "Candidate significant places (spatio-temporal event clusters) derived from illness events" artifact F_place_membership : feature(D_illness_events) value structure: atomic value type: reference description: "Assignment of each illness event to a place cluster (or noise)" transform T_characterise_place_timeseries : intent: characterise manner: "aggregate" input: D_places, D_illness_events, F_place_membership output: F_place_timeseries actor: machine description: "Compute per-place time series of event counts" artifact F_place_timeseries : feature(D_places) value structure: vector value type: numeric representation form: "time series per place" description: "Event-count time series per candidate place" transform T_characterise_place_incidence : intent: characterise manner: "normalise by population" input: D_places, D_zones, F_place_timeseries output: F_place_incidence actor: machine description: "Normalise place-level event counts by zone population to obtain per-capita incidence" artifact F_place_incidence : feature(D_places) value structure: vector value type: numeric description: "Per-capita incidence time series per place" transform T_arrange_map_places : intent: contextualise manner: "map-based" input: D_places, D_map_features output: A_map_places actor: machine description: "Overlay candidate places on the map context (highways, hospitals, landmarks, water bodies)" artifact A_map_places : arrangement(D_places) context: D_map_features principle: "geographic coordinates" description: "Spatial arrangement of candidate places relative to map features" transform T_vis_map_hotspots : intent: visualise manner: "coloured-marks" input: A_map_places, F_place_incidence, D_map_features output: V_map_hotspots actor: machine description: "Map view of candidate places sized/coloured by onset time and per-capita incidence, overlaid on landmarks and water bodies" artifact V_map_hotspots : visualisation(A_map_places, F_place_incidence, D_map_features) layout: "geographic map" form: "coloured marks with size/time encoding" encoding: "position: from A_map_places; colour/size: from F_place_incidence and onset_time; background: D_map_features" description: "Visual identification of the earliest and most severe place cluster(s) and their co-location with fixed landmarks" transform T_abstract_origin_candidate : intent: abstract manner: "perception-and-interpretation" input: V_map_hotspots output: P_origin_candidate actor: human description: "Identify candidate origin place(s) and note whether they coincide with a specific fixed feature (e.g., reservoir, festival ground) versus diffuse residential spread" artifact P_origin_candidate : pattern(D_places) representation form: "textual description" description: "Candidate origin place(s), their co-location with map features, and earliest onset time" # ============================================================================ # STAGE D: SPATIO-TEMPORAL GROUPING AND GROWTH MODELLING ACROSS ZONES # ============================================================================ transform T_characterise_zone_incidence : intent: characterise manner: "aggregate and normalise by population" input: D_zones, D_illness_events output: F_zone_incidence actor: machine description: "Compute per-capita incidence time series for each of the 13 zones" artifact F_zone_incidence : feature(D_zones) value structure: vector value type: numeric representation form: "time series per zone" description: "Daily per-capita incidence time series per zone" transform T_cluster_zones : intent: define-unit manner: "cluster-by-similarity of incidence profile" input: D_zones, F_zone_incidence output: D_zone_groups, F_zone_group_label actor: machine description: "Group the 13 zones by similarity of their temporal incidence profile" artifact D_zone_groups : entities internal structure: group/cluster embedment: space description: "Groups of zones with similar temporal incidence profiles" artifact F_zone_group_label : feature(D_zones) value structure: atomic value type: categorical description: "Temporal-similarity group label for each zone" transform T_arrange_map_zones : intent: contextualise manner: "map-based" input: D_zones, D_map_features output: A_map_zones actor: machine description: "Position zones on the map context, including the highway network" artifact A_map_zones : arrangement(D_zones) context: D_map_features principle: "geographic coordinates" description: "Spatial arrangement of zones relative to highways and other map features" transform T_vis_zone_groups : intent: visualise manner: "coloured-marks" input: A_map_zones, F_zone_group_label, D_map_features output: V_zone_groups_map actor: machine description: "Map view of zones coloured by temporal-similarity group, overlaid on the highway network, to check spatial contiguity versus dispersion" artifact V_zone_groups_map : visualisation(A_map_zones, F_zone_group_label, D_map_features) layout: "geographic map" form: "coloured region marks" encoding: "position: from A_map_zones; colour: from F_zone_group_label; background: highway layer from D_map_features" description: "Visual check of whether temporally similar zones are spatially contiguous/highway-aligned or spatially disconnected" transform T_build_growth_model : intent: build-model manner: "fit-growth-curve" input: F_zone_incidence, D_zone_groups output: M_growth actor: machine description: "Fit an exponential/logistic growth curve to each zone group's representative incidence curve" artifact M_growth : model(F_zone_incidence, D_zone_groups) model type: "growth model" representation form: "exponential/logistic curve parameters per group" description: "Simple growth model of outbreak onset/expansion per zone group" transform T_characterise_residuals : intent: characterise manner: "compute-statistics (residuals)" input: F_zone_incidence, M_growth output: F_growth_residuals actor: machine description: "Compute residuals of observed incidence relative to the fitted group growth curve, to flag zones deviating from the dominant pattern" artifact F_growth_residuals : feature(D_zones) value structure: vector value type: numeric description: "Residual time series per zone relative to fitted growth model" transform T_vis_residuals : intent: visualise manner: "line-graph / coloured-marks on map" input: F_growth_residuals, A_map_zones output: V_residuals_map actor: machine description: "Map and/or timeline view highlighting zones with large residuals (candidates for a distinct secondary source or delayed exposure)" artifact V_residuals_map : visualisation(F_growth_residuals, A_map_zones) layout: "geographic map / aligned timelines" form: "coloured marks / line graph" encoding: "colour or line deviation: from F_growth_residuals; position: from A_map_zones" description: "Highlights zones whose incidence deviates from the dominant growth pattern" transform T_abstract_spread_pattern : intent: abstract manner: "perception-and-interpretation" input: V_zone_groups_map, V_residuals_map, D_map_features output: P_spread_pattern actor: human description: "Interpret whether zone grouping follows the highway/mobility network (favouring person-to-person spread) or is spatially disconnected (favouring a shared, non-contact exposure); flag deviating zones as candidate secondary sources" artifact P_spread_pattern : pattern(D_zone_groups, F_growth_residuals) representation form: "textual description" description: "Description of the dominant spread geometry (contiguous/highway-aligned vs. disconnected) and any deviating zones" # ============================================================================ # STAGE E: WIND-SPREAD CROSS-IMPACT ANALYSIS (AIRBORNE HYPOTHESIS) # ============================================================================ transform T_characterise_downwind_alignment : intent: characterise manner: "compute directional alignment relative to wind" input: D_zones, D_weather, P_origin_candidate output: F_downwind_alignment actor: machine description: "For each zone and day, compute how well the zone's bearing from the candidate origin matches that day's wind direction" artifact F_downwind_alignment : feature(D_zones) value structure: vector value type: numeric description: "Daily downwind alignment score per zone relative to the candidate origin place" artifact S_lag_spec : specification origin: given representation form: "parameter settings" description: "Initial window length, lag range, and tolerance interval for cross-impact analysis between wind and incidence/alignment series" loop L_wind_analysis: purpose: "Compute and calibrate the wind-incidence cross-impact pattern to test the airborne transmission hypothesis" until: "Impact matrix is stable/interpretable enough to support a judgment on the airborne hypothesis" body: transform T_define_lag_windows : intent: define-unit manner: "time-partitioning into sliding windows" input: D_weather, F_zone_incidence, S_lag_spec output: D_windows actor: machine description: "Partition the wind series and each zone's response series into sliding comparison windows over a range of plausible incubation lags" artifact D_windows : entities internal structure: episode embedment: time features: - id: lag value structure: atomic value type: numeric description: "Lag offset applied when comparing base and response series in this window" description: "Sliding time windows used to compare the wind (base) series with each zone's response series at varying lags" transform T_characterise_cross_impact : intent: characterise manner: "compute lagged association (tolerance-based)" input: D_windows, D_weather, F_zone_incidence, F_downwind_alignment, S_lag_spec output: F_impact_matrix actor: machine description: "Compute a tolerance-based association measure (e.g., tolerance-Kendall's-tau) between the wind base series and each zone's response series across the window/lag grid" artifact F_impact_matrix : feature(D_zones) value structure: matrix value type: numeric representation form: "time x lag impact matrix (Ikat-style)" description: "Per-zone matrix of association strength across time windows and lags" transform T_vis_impact : intent: visualise manner: "matrix-plot" input: F_impact_matrix output: V_wind_impact actor: machine description: "Ikat-style visualisation of the time x lag impact matrix for each zone (or aggregated downwind vs. upwind comparison)" artifact V_wind_impact : visualisation(F_impact_matrix) layout: "time x lag grid, per zone" form: "matrix plot" encoding: "cell colour: from F_impact_matrix association strength" description: "Visual display for inspecting whether downwind zones show a consistent positive impact at a plausible lag" transform T_assess_impact : intent: assess manner: "evaluate stability and interpretability of impact pattern" input: V_wind_impact, D_weather, F_zone_incidence output: K_impact_quality actor: human description: "Judge whether the current window/lag/tolerance settings yield a stable, interpretable impact pattern, cross-checking suspicious cells against the underlying wind and incidence series" artifact K_impact_quality : knowledge(F_impact_matrix) representation form: "quality judgment" description: "Judgment of whether the impact matrix is stable/interpretable or requires recalibration of parameters" if K_impact_quality indicates refinement needed: then: transform T_refine_lag_spec : intent: generate-knowledge manner: "adjust parameter settings" input: K_impact_quality, S_lag_spec output: S_lag_spec_r actor: human description: "Adjust window length, lag range, or tolerance based on assessment" artifact S_lag_spec_r : specification representation form: "parameter settings" description: "Revised window/lag/tolerance settings" assign: S_lag_spec := S_lag_spec_r else: exit loop L_wind_analysis end loop L_wind_analysis transform T_knowledge_airborne : intent: generate-knowledge manner: "formulate-statements" input: V_wind_impact, F_impact_matrix output: K_airborne_evidence actor: human description: "Formulate a statement on whether downwind zones show a consistent lagged impact from wind at the candidate origin, supporting or refuting airborne transmission" artifact K_airborne_evidence : knowledge(F_impact_matrix) representation form: "statements" description: "Evidence for or against airborne transmission based on stable directional wind-lag association" # ============================================================================ # STAGE F: PERSON-TO-PERSON AND WATERBORNE HYPOTHESIS CHECKS # ============================================================================ transform T_characterise_social_mentions : intent: characterise manner: "extract (co-location/social mentions from text)" input: D_illness_events output: F_social_mentions actor: hybrid description: "Extract mentions of shared social context (coworkers, family, shared venues, shared transit lines) from illness-event text" artifact F_social_mentions : feature(D_illness_events) value structure: list value type: {text, categorical} description: "Extracted social/co-location references per illness event" transform T_define_social_network : intent: define-unit manner: "derive relational structure from co-occurrence mentions" input: D_illness_events, F_social_mentions output: D_social_network actor: hybrid description: "Build a light co-occurrence network of people/places mentioned together across illness events" artifact D_social_network : entities internal structure: formation features: - id: edge_weight value structure: atomic value type: numeric description: "Strength of co-occurrence link" description: "Co-occurrence network of persons/places mentioned together in illness reports (single relational structure)" transform T_vis_social_network : intent: visualise manner: "node-link-diagram" input: D_social_network output: V_social_network actor: machine description: "Node-link visualisation of the social/co-location co-occurrence network" artifact V_social_network : visualisation(D_social_network) layout: "force-directed network layout" form: "node-link diagram" encoding: "edges: from D_social_network edge_weight" description: "Visualisation for inspecting chains of social/mobility contact among cases" transform T_abstract_p2p_pattern : intent: abstract manner: "perception-and-interpretation" input: V_social_network, V_zone_groups_map output: P_p2p_pattern actor: human description: "Assess whether cases chain along social/mobility links and whether spatial growth tracks the highway/transit network rather than a single fixed landmark" artifact P_p2p_pattern : pattern(D_social_network, D_zone_groups) representation form: "textual description" description: "Description of evidence for/against person-to-person transmission via social and mobility chains" transform T_vis_water_overlay : intent: visualise manner: "map-based overlay" input: A_map_places, D_map_features output: V_water_overlay actor: machine description: "Overlay origin and hotspot clusters directly on the water-body layer and any known water-service-area boundaries" artifact V_water_overlay : visualisation(A_map_places, D_map_features) layout: "geographic map" form: "coloured marks over water/service-area layer" encoding: "hotspot marks: from A_map_places; background: water bodies/service areas from D_map_features" description: "Visual check of whether hotspot clusters are coincident with water sources or service-area boundaries" transform T_abstract_waterborne_pattern : intent: abstract manner: "perception-and-interpretation" input: V_water_overlay, P_origin_candidate output: P_waterborne_pattern actor: human description: "Assess whether the origin/hotspot clusters coincide with a water source and whether case distribution matches water-service areas rather than road proximity or social chains" artifact P_waterborne_pattern : pattern(D_places) representation form: "textual description" description: "Description of evidence for/against waterborne transmission" transform T_knowledge_mechanism_checklist : intent: generate-knowledge manner: "formulate-statements (comparison checklist)" input: K_airborne_evidence, P_p2p_pattern, P_waterborne_pattern, P_spread_pattern output: K_mechanism_assessment actor: human description: "Compile a diagnostic checklist comparing the expected spatial/temporal/contextual signature of each transmission mechanism (airborne, person-to-person, waterborne) against the observed evidence" artifact K_mechanism_assessment : knowledge(K_airborne_evidence, P_p2p_pattern, P_waterborne_pattern, P_spread_pattern) representation form: "comparison checklist / statements" description: "Structured comparison of candidate transmission mechanisms against observed diagnostic signatures" # ============================================================================ # STAGE G: SYNTHESIS AND REPORTING # ============================================================================ transform T_synthesise_report : intent: generate-knowledge manner: "write-summary" input: K_onset_candidates, P_origin_candidate, K_mechanism_assessment output: K_final_report actor: human description: "Synthesise the probable origin place and time window, the dominant spread pattern, and the best-supported transmission mechanism into an evidence-linked report for emergency management" artifact K_final_report : knowledge(D_places, D_zone_groups, F_impact_matrix) representation form: "statements / report" description: "Evidence-linked report on outbreak origin, spread pattern, and most probable transmission mechanism, to guide deployment of emergency management resources"