VAST · Claude Sonnet 5 · C2

workflow vastopolis-outbreak-origin-and-transmission template: characterise → define-unit → loop(contextualise → visualise → assess → define-unit) → characterise → characterise → contextualise → visualise → abstract → loop(characterise → visualise → assess → generate-knowledge) → generate-knowledge description: "Human-in-the-loop workflow to identify the spatial-temporal origin of a flu-like outbreak in Vastopolis and to determine the most plausible transmission mechanism (person-to-person, airborne, waterborne, or other), combining social-media evidence with map, population, and weather context to support resource deployment decisions"

artifact D_microblogs : entities origin: given internal structure: elementary embedment: {set, space, time} features: - id: text_content value structure: atomic value type: text description: "Free text content of the message" - id: location value structure: atomic value type: spatial description: "GPS coordinates of posting" - id: timestamp value structure: atomic value type: temporal description: "Posting time" description: "Geolocated microblog messages posted from mobile devices over the outbreak period" 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" - id: zone_name value structure: atomic value type: categorical description: "Zone identifier/name" description: "13 city zones with population statistics, used as spatial units for incidence normalisation" 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 description: "Daily wind direction" description: "Daily weather observations over the outbreak period" artifact D_map_context : entities origin: given internal structure: elementary embedment: space features: - id: layer_type value structure: atomic value type: categorical description: "Map layer category: highway, hospital, landmark, water_body" - id: geometry value structure: atomic value type: spatial description: "Geometry of the map feature" description: "Static geographic layers of the metropolitan area (road network, hospitals, landmarks, water bodies), used as spatial reference context for mapping and mechanism analysis" artifact S_keyword_spec : specification origin: given representation form: "keyword lexicon" description: "Initial lexicon of flu-like symptom, severity, and death-related terms used to identify illness-related messages" artifact K_domain_transmission_knowledge : knowledge(D_weather, D_map_context) origin: given representation form: "domain rules" description: "Epidemiological domain knowledge describing expected spatial-temporal signatures of airborne, waterborne, and person-to-person transmission, used to interpret observed spread patterns"

transform T_extract_symptom_signal : intent: characterise manner: "keyword/text feature extraction" input: D_microblogs, S_keyword_spec output: F_symptom_signal actor: machine description: "Match message text against the keyword lexicon to derive indicators of symptom mention, severity, and death-related content" artifact F_symptom_signal : feature(D_microblogs) value structure: vector value type: categorical representation form: "boolean indicators for symptom mention, severity keywords, and death-related keywords" description: "Text-derived illness-indicator features per message" transform T_filter_illness_reports : intent: define-unit manner: "filter" input: D_microblogs, F_symptom_signal output: D_illness_reports actor: machine description: "Select messages whose content indicates flu-like illness" artifact D_illness_reports : entities internal structure: elementary embedment: {set, space, time} description: "Geolocated, timestamped messages indicative of flu-like illness"

loop L_filter_refinement: purpose: "Refine the keyword lexicon and filtering criteria so the illness-report set is a reliable proxy for actual illness incidence" until: "Visual inspection confirms filtered reports are plausibly illness-related and their spatial-temporal density looks consistent with a genuine outbreak signal rather than noise" body: transform T_contextualise_reports_map : intent: contextualise manner: "map-based" input: D_illness_reports, D_map_context output: A_reports_map actor: machine description: "Position illness reports on the metropolitan map" artifact A_reports_map : arrangement(D_illness_reports) context: D_map_context principle: "geographic coordinates" description: "Spatial placement of illness reports relative to roads, hospitals, landmarks, and water bodies" transform T_visualise_reports : intent: visualise manner: "coloured points on map with time slider" input: A_reports_map, D_illness_reports output: V_reports_check actor: machine description: "Display raw spatial-temporal distribution of filtered reports for inspection of content and density" artifact V_reports_check : visualisation(A_reports_map, D_illness_reports) layout: "map with time slider" form: "coloured points" encoding: "position from A_reports_map; colour/opacity from timestamp recency" description: "Interactive view for judging plausibility of the filtered illness signal" transform T_assess_filter : intent: assess manner: "judge relevance and noise level of filtered reports" input: V_reports_check, F_symptom_signal output: K_filter_quality actor: human description: "Assess whether filtered reports plausibly reflect genuine illness incidence or contain excessive noise/false positives" artifact K_filter_quality : knowledge(D_illness_reports, F_symptom_signal) representation form: "quality judgment" description: "Judgment of filtering adequacy and need for lexicon refinement" if K_filter_quality indicates refinement needed: then: transform T_refine_keywords : intent: generate-knowledge manner: "formulate methodological decision" input: K_filter_quality, F_symptom_signal output: S_keyword_spec_r actor: human description: "Revise keyword lexicon/filtering rules based on observed false positives/negatives" artifact S_keyword_spec_r : specification representation form: "keyword lexicon" description: "Revised keyword lexicon for illness-indicative content" transform T_extract_symptom_signal_r : intent: characterise manner: "keyword/text feature extraction" input: D_microblogs, S_keyword_spec_r output: F_symptom_signal_r actor: machine description: "Re-extract illness-indicator features using the revised lexicon" artifact F_symptom_signal_r : feature(D_microblogs) value structure: vector value type: categorical description: "Updated illness-indicator features per message" transform T_filter_illness_reports_r : intent: define-unit manner: "filter" input: D_microblogs, F_symptom_signal_r output: D_illness_reports_r actor: machine description: "Re-select illness-related messages using updated features" artifact D_illness_reports_r : entities internal structure: elementary embedment: {set, space, time} description: "Updated set of illness-related messages" assign: S_keyword_spec := S_keyword_spec_r F_symptom_signal := F_symptom_signal_r D_illness_reports := D_illness_reports_r else: exit loop L_filter_refinement end loop L_filter_refinement

transform T_aggregate_zone_counts : intent: characterise manner: "aggregate" input: D_illness_reports, D_zones output: F_case_counts actor: machine description: "Spatially join illness reports to zones and aggregate daily case counts per zone" artifact F_case_counts : feature(D_zones) value structure: vector value type: numeric representation form: "daily case-count time series per zone" description: "Daily number of illness reports per zone" transform T_normalise_incidence : intent: characterise manner: "compute-statistics (per-capita normalisation)" input: F_case_counts, D_zones output: F_incidence_rate actor: machine description: "Normalise daily case counts by zone population to obtain comparable per-capita incidence rates" artifact F_incidence_rate : feature(D_zones) value structure: vector value type: numeric representation form: "daily per-capita incidence rate time series per zone" description: "Population-normalised daily illness incidence per zone" transform T_visualise_incidence_spread : intent: visualise manner: "animated choropleth map with time slider" input: D_zones, F_incidence_rate, D_map_context output: V_incidence_map actor: machine description: "Show spatial-temporal progression of normalised illness incidence across zones and days" artifact V_incidence_map : visualisation(D_zones, F_incidence_rate, D_map_context) layout: "map with time slider" form: "choropleth coloured zones" encoding: "zone geometry from D_map_context; colour intensity from F_incidence_rate per day" description: "Reveals onset location and diffusion pattern of the outbreak" transform T_abstract_origin_pattern : intent: abstract manner: "perception-and-interpretation" input: V_incidence_map, V_reports_check output: P_outbreak_origin actor: human description: "Identify the zone(s) and approximate time where the outbreak appears to have started, and characterise the overall spatial diffusion pattern" artifact P_outbreak_origin : pattern(D_zones, D_illness_reports) representation form: "spatial-temporal description of earliest cluster and diffusion direction" description: "Candidate outbreak origin (zone/time) and qualitative description of how the outbreak has spread so far"

artifact S_mechanism_spec : specification origin: given representation form: "analysis scope settings" description: "Initial scope for transmission-mechanism analysis: full time period, all zones, primary map layers (water bodies, road network)" loop L_transmission_analysis: purpose: "Iteratively test and refine mechanism-specific evidence until the spread pattern is clearly best explained by one (or a ranked set of) transmission mechanism(s)" until: "Assessment yields a clear, defensible conclusion about the dominant transmission pathway" body: transform T_characterise_wind_alignment : intent: characterise manner: "define-relations (directional correlation)" input: P_outbreak_origin, D_illness_reports, D_weather, S_mechanism_spec output: F_wind_alignment actor: machine description: "Compute daily alignment between the direction of case spread from the origin and prevailing wind direction/speed (airborne-transmission signature)" artifact F_wind_alignment : feature(D_zones) value structure: vector value type: numeric representation form: "daily wind-spread alignment score per zone" description: "Evidence for airborne transmission" transform T_characterise_water_proximity : intent: characterise manner: "compute-statistics (spatial distance)" input: D_illness_reports, D_map_context, S_mechanism_spec output: F_water_proximity actor: machine description: "Compute distance of illness-report locations to nearest water body/water infrastructure (waterborne-transmission signature)" artifact F_water_proximity : feature(D_illness_reports) value structure: atomic value type: numeric description: "Evidence for waterborne transmission" transform T_characterise_diffusion_shape : intent: characterise manner: "define-relations (spatial diffusion pattern)" input: D_illness_reports, D_zones, D_map_context, S_mechanism_spec output: F_diffusion_shape actor: machine description: "Characterise zone-to-zone spread over time relative to population density and road connectivity, distinguishing gradual contact-like diffusion (person-to-person) from near-simultaneous onset in disconnected zones (point-source)" artifact F_diffusion_shape : feature(D_zones) value structure: relational configuration representation form: "time-stamped zone-adjacency graph weighted by case-onset lag" description: "Evidence distinguishing person-to-person from point-source spread" transform T_visualise_mechanism_overlays : intent: visualise manner: "coordinated overlay views" input: P_outbreak_origin, F_wind_alignment, F_water_proximity, F_diffusion_shape, D_map_context output: V_transmission_overlays actor: machine description: "Overlay wind vectors, water-proximity gradient, and zone-connectivity graph on the map for visual comparison against candidate transmission signatures" artifact V_transmission_overlays : visualisation(P_outbreak_origin, F_wind_alignment, F_water_proximity, F_diffusion_shape, D_map_context) layout: "map with wind-vector, water-layer, and zone-adjacency overlays, small multiples per day" form: "vector arrows, coloured zones, network edges" encoding: "wind arrows from F_wind_alignment; water-proximity colour gradient from F_water_proximity; connectivity edges from F_diffusion_shape" description: "Visual comparison of observed spread against airborne, waterborne, and person-to-person signatures" transform T_assess_transmission_hypotheses : intent: assess manner: "compare alternative results produced under different specifications" input: V_transmission_overlays, F_wind_alignment, F_water_proximity, F_diffusion_shape, K_domain_transmission_knowledge output: K_transmission_evidence actor: human description: "Judge which candidate transmission mechanism(s) best explain the observed spread, informed by domain knowledge of typical mechanism signatures; determine if evidence is conclusive" artifact K_transmission_evidence : knowledge(F_wind_alignment, F_water_proximity, F_diffusion_shape) representation form: "comparative quality judgment" description: "Evaluation of which transmission mechanism(s) are best supported by current evidence, and whether further analysis is needed" if K_transmission_evidence indicates refinement needed: then: transform T_adjust_analysis_scope : intent: generate-knowledge manner: "formulate methodological decision" input: K_transmission_evidence output: S_mechanism_spec_r actor: human description: "Decide how to refine the mechanism analysis, e.g. narrow time window to early outbreak days, focus on specific zones, or incorporate additional map layers" artifact S_mechanism_spec_r : specification representation form: "analysis scope settings" description: "Revised scope for transmission-mechanism analysis" assign: S_mechanism_spec := S_mechanism_spec_r else: exit loop L_transmission_analysis end loop L_transmission_analysis

transform T_synthesise_findings : intent: generate-knowledge manner: "formulate-statements" input: P_outbreak_origin, K_transmission_evidence, K_filter_quality output: K_outbreak_findings actor: human description: "Synthesise conclusions about the outbreak's spatial-temporal origin and its most plausible transmission mechanism(s), to inform prioritisation of emergency-management resources" artifact K_outbreak_findings : knowledge(D_illness_reports, D_zones, D_weather, D_map_context) representation form: "statements and recommendations" description: "Conclusions on outbreak origin location/time and dominant transmission mechanism, with implications for resource deployment"