workflow vastopolis-outbreak-origin-and-transmission template: define-unit -> loop(build-model -> characterise -> visualise -> abstract -> assess -> build-model) -> define-unit -> characterise -> contextualise -> visualise -> assess -> define-unit -> characterise -> contextualise -> visualise -> abstract -> generate-knowledge -> generate-knowledge -> characterise -> build-model -> loop(characterise -> contextualise -> visualise -> abstract -> assess -> build-model) -> visualise -> generate-knowledge -> characterise -> visualise -> generate-knowledge description: "Visual analytics workflow for identifying likely outbreak-origin areas, comparing possible transmission mechanisms, and prioritising public health deployment using geolocated microblog reports, geographic context, population statistics, and weather observations." # ============================================================================ # INPUT DATA AND INITIAL SPECIFICATIONS # ============================================================================ artifact D_posts : entities origin: given internal structure: elementary embedment: {space, time} features: - id: timestamp value structure: atomic value type: temporal description: "Posting time" - id: gps_location value structure: atomic value type: spatial description: "GPS coordinate recorded by the mobile device" - id: text value structure: atomic value type: text description: "Microblog message text" - id: account_id value structure: atomic value type: reference description: "Pseudonymous account identifier, used only for duplicate and probable-bot detection" description: "Geolocated microblog messages collected during several days of the suspected outbreak" artifact D_city_features : entities origin: given internal structure: elementary embedment: space features: - id: 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: "Geographic geometry" - id: name value structure: atomic value type: text description: "Map feature name, where available" description: "Metropolitan geographic context including highways, hospitals, landmarks, and water bodies" 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 city zone" - id: zone_name value structure: atomic value type: text description: "Zone identifier or name" description: "Thirteen city zones 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: "Observed wind speed" - id: wind_direction value structure: atomic value type: numeric description: "Observed wind direction" description: "Daily weather observations for Vastopolis" artifact K_epidemiology : knowledge(D_posts, D_city_features, D_weather) origin: given representation form: "domain assumptions and interpretation guidance" description: "Public-health knowledge concerning flu-like symptoms, plausible reporting biases, plausible transmission mechanisms, expected temporal lags, and limitations of social-media data as a proxy for cases" artifact S_cleaning : specification origin: given representation form: "filtering rules and uncertainty criteria" description: "Rules for duplicate removal, bot detection, timestamp and coordinate validation, and retention of uncertainty flags" artifact S_content_initial : specification origin: given representation form: "initial text-classification configuration" description: "Initial symptom vocabulary, topic-model settings, content categories, confidence thresholds, and rules for identifying probable self-reported illness" artifact S_spatiotemporal_units : specification origin: given representation form: "spatiotemporal aggregation settings" description: "Zone, fine-grid, and adaptive-cell resolutions; one-, three-, and six-hour temporal bins; population normalization; and low-volume uncertainty rules" artifact S_origin_detection : specification origin: given representation form: "cluster and origin-ranking criteria" description: "Space-time clustering settings, persistence requirement, independent-account requirement where possible, onset definition, and sensitivity-analysis settings" artifact S_model_initial : specification origin: given representation form: "candidate-model configuration" description: "Initial lag ranges, candidate source locations, covariates, comparison criteria, and transparent count/rate regression forms for contact-propagation, airborne, water-exposure, point-source, and mixed hypotheses" assign: S_content_current := S_content_initial S_model_current := S_model_initial # ============================================================================ # STEP 1: CLEAN POSTS AND ITERATIVELY REFINE CONTENT CLASSIFICATION # ============================================================================ transform T_clean_posts : intent: define-unit manner: "filter and quality-control" input: D_posts, S_cleaning output: D_clean_posts actor: machine description: "Remove duplicate posts, obvious automated activity, and posts with invalid timestamps or coordinates while retaining quality and uncertainty indicators" artifact D_clean_posts : entities internal structure: elementary embedment: {space, time} features: - id: text value structure: atomic value type: text description: "Post text" - id: location value structure: atomic value type: spatial description: "Validated GPS coordinate" - id: timestamp value structure: atomic value type: temporal description: "Validated posting time" - id: data_quality_flag value structure: atomic value type: categorical description: "Reliability and uncertainty flag" description: "Quality-controlled microblog posts" transform T_train_initial_content_model : intent: build-model manner: "train text classifier and topic model" input: D_clean_posts, S_content_current, K_epidemiology output: M_content_model_initial actor: hybrid description: "Construct an initial content model using rules, topic modelling, and public-health terminology to distinguish symptom reports from news, concern, misinformation, and unrelated posts" artifact M_content_model_initial : model(D_clean_posts, K_epidemiology) model type: "text classifier and topic model" representation form: "keyword rules plus topic-model parameters" description: "Initial model for classifying illness-related microblog posts" assign: M_content_model := M_content_model_initial loop L_content_refinement: purpose: "Refine illness-report classification until categories and confidence judgements are adequate for outbreak analysis" until: "Public-health analysts judge that sampled classifications, topic interpretations, and uncertainty flags are sufficiently credible" body: transform T_classify_posts : intent: characterise manner: "classify text content" input: D_clean_posts, M_content_model, S_content_current output: F_content_class actor: machine description: "Assign content categories and confidence values, including self-reported symptoms, reports about others, severe illness or death, healthcare seeking, news or concern, and unrelated content" artifact F_content_class : feature(D_clean_posts) value structure: vector value type: {categorical, numeric} representation form: "content category and confidence per post" description: "Probabilistic content labels for cleaned posts" transform T_visualise_content_review : intent: visualise manner: "linked text-map-time review" input: D_clean_posts, F_content_class output: V_content_review actor: machine description: "Display sampled original posts, topic/category summaries, message locations, and posting times for analyst inspection" artifact V_content_review : visualisation(D_clean_posts, F_content_class) layout: "linked document list, topic summary, map, and timeline" form: "text excerpts, coloured points, bars, and time series" encoding: "colour represents content category; map position represents GPS location; timeline position represents posting time; text panel exposes original messages" description: "Evidence view for reviewing text-classification results" transform T_interpret_content : intent: abstract manner: "perception-and-interpretation" input: V_content_review, F_content_class, K_epidemiology output: P_content_interpretation actor: human description: "Identify misleading terms, sarcasm, news reposting, non-flu conditions, category ambiguities, and meaningful illness- related themes" artifact P_content_interpretation : pattern(D_clean_posts, F_content_class) representation form: "annotated topic and classification issues" description: "Observed semantic patterns and classification problems in microblog content" transform T_assess_content : intent: assess manner: "assess text-classification adequacy" input: V_content_review, P_content_interpretation, F_content_class output: K_content_quality, S_content_refined actor: human description: "Judge whether content categories and confidence thresholds are adequate for use as illness indicators; formulate revised settings when they are not" artifact K_content_quality : knowledge(F_content_class, V_content_review) representation form: "quality judgment" description: "Analyst judgment about credibility, ambiguity, and suitability of classified posts for outbreak analysis" artifact S_content_refined : specification representation form: "revised text-classification configuration" description: "Updated symptom terms, topic interpretation, category definitions, thresholds, and exclusion rules" if K_content_quality indicates classification refinement needed: then: transform T_refine_content_model : intent: build-model manner: "retrain with analyst-guided vocabulary and topic changes" input: D_clean_posts, M_content_model, S_content_refined, P_content_interpretation output: M_content_model_refined actor: hybrid description: "Retrain or adjust the content model using analyst- identified semantic issues and revised classification settings" artifact M_content_model_refined : model(D_clean_posts, P_content_interpretation) model type: "refined text classifier and topic model" representation form: "updated keyword rules and topic-model parameters" description: "Revised model for identifying credible illness reports" assign: M_content_model := M_content_model_refined S_content_current := S_content_refined else: exit loop L_content_refinement end loop L_content_refinement transform T_extract_place_mentions : intent: characterise manner: "named-entity and place-reference extraction" input: D_clean_posts, F_content_class, D_city_features output: F_place_mentions actor: hybrid description: "Extract and contextually validate references to landmarks, hospitals, events, workplaces, water locations, and travel locations" artifact F_place_mentions : feature(D_clean_posts) value structure: list value type: reference representation form: "linked map-feature references and unresolved place names" description: "Place and facility references found in microblog text" # ============================================================================ # STEP 2: CONSTRUCT AND VALIDATE SPATIOTEMPORAL OUTBREAK SIGNALS # ============================================================================ transform T_select_health_reports : intent: define-unit manner: "filter by classified health-report category" input: D_clean_posts, F_content_class, K_content_quality output: D_symptom_reports, D_severe_reports actor: machine description: "Select high-confidence symptom reports and separately retain severe-illness or death-associated reports" artifact D_symptom_reports : entities internal structure: elementary embedment: {space, time} features: - id: symptom_confidence value structure: atomic value type: numeric description: "Confidence that the post reports flu-like illness" - id: report_type value structure: atomic value type: categorical description: "Self-report or report about another person" description: "High-confidence flu-like symptom-report posts" artifact D_severe_reports : entities internal structure: elementary embedment: {space, time} features: - id: severity_type value structure: atomic value type: categorical description: "Severe illness, death, or hospital-related report category" description: "Posts associated with severe illness, deaths, or healthcare seeking" transform T_construct_signal_units : intent: define-unit manner: "spatiotemporal aggregation" input: D_symptom_reports, D_severe_reports, D_zones, S_spatiotemporal_units output: D_analysis_areas, D_signal_units actor: machine description: "Create city-zone, fine-grid, or adaptive spatial units and time-binned units for comparing outbreak signals across resolutions" artifact D_analysis_areas : entities internal structure: region embedment: space features: - id: area_type value structure: atomic value type: categorical description: "Zone, grid cell, or adaptive area" - id: population value structure: atomic value type: numeric description: "Population estimate for the analysis area" description: "Spatial analysis areas at multiple resolutions" artifact D_signal_units : entities internal structure: group/cluster embedment: {space, time} features: - id: temporal_bin value structure: atomic value type: temporal description: "Time interval represented by the unit" - id: spatial_area value structure: atomic value type: reference description: "Associated analysis area" description: "Aggregated symptom and severe-report observations by area and time" transform T_compute_outbreak_indicators : intent: characterise manner: "aggregate and normalise" input: D_signal_units, D_clean_posts, D_analysis_areas output: F_outbreak_indicators actor: machine description: "Compute symptom counts, population-normalised rates, symptom share of local posting volume, severe-report counts, growth measures, and uncertainty indicators" artifact F_outbreak_indicators : feature(D_signal_units) value structure: vector value type: numeric representation form: "time-binned outbreak indicator vector" description: "Counts, rates, posting-volume-adjusted measures, severe-report signals, growth estimates, and uncertainty values" transform T_arrange_signals_on_map : intent: contextualise manner: "map-based" input: D_signal_units, D_analysis_areas output: A_signal_map actor: machine description: "Arrange aggregated signal units in their corresponding geographic analysis areas" artifact A_signal_map : arrangement(D_signal_units) context: D_analysis_areas principle: "spatial-area membership" description: "Map arrangement of outbreak signal units" transform T_visualise_outbreak_signals : intent: visualise manner: "coordinated map and temporal views" input: A_signal_map, F_outbreak_indicators, D_city_features, D_severe_reports output: V_signal_overview actor: machine description: "Show animated maps, area time series, temporal heatmaps, severe-report signals, and uncertainty indicators" artifact V_signal_overview : visualisation(A_signal_map, F_outbreak_indicators, D_city_features, D_severe_reports) layout: "linked map, space-time heatmap, and zone time-series views" form: "coloured areas, points, lines, and uncertainty bands" encoding: "map position from arrangement; colour and height from symptom rate; point overlays from severe reports; bands represent uncertainty" description: "Visual overview for checking whether apparent peaks are credible illness signals rather than posting or media artifacts" transform T_assess_signal_validity : intent: assess manner: "judge social-media signal credibility" input: V_signal_overview, D_symptom_reports, D_severe_reports, K_content_quality, K_epidemiology output: K_signal_validity actor: human description: "Inspect supporting posts and assess whether observed peaks plausibly reflect illness reporting rather than news amplification, hospital-reporting effects, or a viral-post cascade" artifact K_signal_validity : knowledge(V_signal_overview, D_symptom_reports) representation form: "quality judgment and caveats" description: "Analyst assessment of credible, uncertain, and likely artifact-driven outbreak signals" # ============================================================================ # STEP 3: DETECT PERSISTENT EARLY CLUSTERS AND RANK ORIGIN CANDIDATES # ============================================================================ transform T_cluster_outbreak_events : intent: define-unit manner: "space-time density clustering" input: D_symptom_reports, F_outbreak_indicators, S_origin_detection output: D_outbreak_clusters, F_cluster_membership actor: machine description: "Identify persistent space-time concentrations of independent symptom reports and distinguish them from isolated bursts" artifact D_outbreak_clusters : entities internal structure: group/cluster embedment: {space, time} features: - id: cluster_extent value structure: vector value type: {spatial, temporal} description: "Spatial and temporal extent of the cluster" description: "Persistent candidate outbreak clusters" artifact F_cluster_membership : feature(D_symptom_reports) value structure: atomic value type: reference description: "Outbreak-cluster identifier for each symptom report" transform T_characterise_clusters : intent: characterise manner: "compute onset, persistence, and robustness descriptors" input: D_outbreak_clusters, D_signal_units, F_outbreak_indicators, D_analysis_areas, S_origin_detection output: F_cluster_descriptors actor: machine description: "Compute sustained onset time, intensity, persistence, population-normalised severity, subsequent expansion, and sensitivity to classification thresholds and spatial-temporal resolutions" artifact F_cluster_descriptors : feature(D_outbreak_clusters) value structure: vector value type: {numeric, temporal} representation form: "origin-evidence descriptor vector" description: "Features used to compare candidate origin areas" transform T_arrange_clusters_on_map : intent: contextualise manner: "map-based" input: D_outbreak_clusters, D_analysis_areas output: A_cluster_map actor: machine description: "Place outbreak clusters on the geographic analysis map" artifact A_cluster_map : arrangement(D_outbreak_clusters) context: D_analysis_areas principle: "cluster centroid and spatial extent" description: "Geographic arrangement of outbreak clusters" transform T_visualise_onset_and_expansion : intent: visualise manner: "animated onset and expansion map" input: A_cluster_map, F_cluster_descriptors, D_city_features, F_outbreak_indicators output: V_origin_evidence actor: machine description: "Show onset times, expansion contours, candidate clusters, nearby hospitals, landmarks, highways, and water bodies" artifact V_origin_evidence : visualisation(A_cluster_map, F_cluster_descriptors, D_city_features, F_outbreak_indicators) layout: "time-enabled geographic map with linked cluster ranking" form: "coloured cluster regions, contours, points, and ranked bars" encoding: "colour represents onset time; size represents intensity; contours represent later expansion; map overlays show city features" description: "Evidence view for identifying early sustained outbreak areas" transform T_interpret_origin_patterns : intent: abstract manner: "rank persistent early clusters" input: V_origin_evidence, F_cluster_descriptors, K_signal_validity output: P_origin_candidates actor: hybrid description: "Rank candidate origin areas by early sustained onset, persistence, intensity, downstream temporal ordering, and robustness, while retaining the distinction between reporting origin and infection origin" artifact P_origin_candidates : pattern(D_outbreak_clusters, F_cluster_descriptors) representation form: "ranked candidate-origin patterns" description: "Persistent early outbreak clusters and their observed expansion relationships" transform T_formulate_origin_assessment : intent: generate-knowledge manner: "formulate-statements" input: P_origin_candidates, V_origin_evidence, K_signal_validity, K_epidemiology output: K_origin_assessment actor: human description: "Formulate a ranked, uncertainty-aware assessment of candidate source areas, including supporting evidence, contradictory evidence, and reporting-bias caveats" artifact K_origin_assessment : knowledge(P_origin_candidates, V_origin_evidence) representation form: "ranked evidence statements" description: "Candidate outbreak-origin areas with confidence, supporting observations, contradictions, and caveats" # ============================================================================ # STEP 4: FORMULATE COMPETING TRANSMISSION HYPOTHESES AND PREDICTORS # ============================================================================ transform T_formulate_hypotheses : intent: generate-knowledge manner: "formulate-statements and modelling strategy" input: K_origin_assessment, K_epidemiology, D_city_features, D_weather output: K_transmission_hypotheses, S_hypothesis_models actor: human description: "Define testable person-to-person, airborne, waterborne, common-point-source, reporting-artifact, and mixed-mechanism hypotheses, with expected observable patterns and comparison requirements" artifact K_transmission_hypotheses : knowledge(K_origin_assessment, D_city_features, D_weather) representation form: "competing explanatory hypotheses" description: "Explicit statements of expected spatial and temporal patterns for alternative transmission mechanisms" artifact S_hypothesis_models : specification representation form: "model structure and comparison constraints" description: "Hypothesis-specific predictors, lags, candidate-source handling, validation periods, matched-area comparisons, and complexity constraints" transform T_compute_exposure_predictors : intent: characterise manner: "derive spatiotemporal explanatory features" input: D_signal_units, F_outbreak_indicators, D_analysis_areas, D_city_features, D_weather, K_origin_assessment, S_hypothesis_models output: F_exposure_predictors actor: machine description: "Compute prior local rates, adjacent-zone and highway-connected rates, source distance, downwind alignment, wind speed, water proximity, reporting volume, hospital proximity, and population-related predictors" artifact F_exposure_predictors : feature(D_signal_units) value structure: vector value type: numeric representation form: "lagged spatial-exposure predictor vector" description: "Predictors for contrasting transmission mechanisms" transform T_build_initial_models : intent: build-model manner: "fit alternative transparent spatiotemporal regressions" input: D_signal_units, F_outbreak_indicators, F_exposure_predictors, K_transmission_hypotheses, S_model_current output: M_contact_initial, M_airborne_initial, M_water_initial, M_point_source_initial, M_mixed_initial actor: machine description: "Fit initial explanatory models for contact propagation, airborne spread, water exposure, common point source, and mixed mechanisms" artifact M_contact_initial : model(F_outbreak_indicators, F_exposure_predictors) model type: "spatiotemporal count or rate regression model" representation form: "lagged local, adjacent-zone, and highway-connectivity terms" description: "Model of person-to-person or mobility-mediated propagation" artifact M_airborne_initial : model(F_outbreak_indicators, F_exposure_predictors) model type: "spatiotemporal count or rate regression model" representation form: "source-distance, downwind-alignment, wind-speed, and lag terms" description: "Model of airborne or aerosol-mediated spread" artifact M_water_initial : model(F_outbreak_indicators, F_exposure_predictors) model type: "spatiotemporal count or rate regression model" representation form: "water-proximity or service-area exposure terms" description: "Model of water-related exposure" artifact M_point_source_initial : model(F_outbreak_indicators, F_exposure_predictors) model type: "spatiotemporal count or rate regression model" representation form: "candidate-event or candidate-location exposure terms" description: "Model of common-event or local point-source exposure" artifact M_mixed_initial : model(F_outbreak_indicators, F_exposure_predictors) model type: "spatiotemporal count or rate regression model" representation form: "penalised combination of mechanism-specific predictors" description: "Model allowing more than one transmission mechanism" assign: M_contact := M_contact_initial M_airborne := M_airborne_initial M_water := M_water_initial M_point_source := M_point_source_initial M_mixed := M_mixed_initial # ============================================================================ # STEP 5: RESIDUAL-DRIVEN HYPOTHESIS COMPARISON AND MODEL REFINEMENT # ============================================================================ loop L_hypothesis_refinement: purpose: "Compare competing transmission explanations and refine models only when visual residual evidence identifies a meaningful missing factor" until: "Model performance, residual patterns, validation results, and epidemiological interpretability are adequate for operational comparison" body: transform T_apply_hypothesis_models : intent: characterise manner: "predict and compute residuals" input: D_signal_units, F_outbreak_indicators, F_exposure_predictors, M_contact, M_airborne, M_water, M_point_source, M_mixed output: F_model_outputs actor: machine description: "Generate model-specific predicted rates, residuals, uncertainty estimates, fit statistics, and held-out location/time validation measures" artifact F_model_outputs : feature(D_signal_units) value structure: vector value type: numeric representation form: "observed, predicted, residual, and validation values per model" description: "Comparative outputs of the candidate transmission models" transform T_arrange_model_outputs : intent: contextualise manner: "map-based" input: D_signal_units, D_analysis_areas output: A_model_map actor: machine description: "Arrange model predictions and residuals by analysis area" artifact A_model_map : arrangement(D_signal_units) context: D_analysis_areas principle: "spatial-area membership" description: "Geographic arrangement for model diagnostics" transform T_visualise_model_diagnostics : intent: visualise manner: "comparative residual and validation display" input: A_model_map, F_model_outputs, D_weather, D_city_features, K_transmission_hypotheses output: V_model_diagnostics actor: machine description: "Display observed-versus-predicted rates, residual maps, uncertainty intervals, model comparisons, lag plots, wind vectors, and downwind/upwind contrasts" artifact V_model_diagnostics : visualisation(A_model_map, F_model_outputs, D_weather, D_city_features) layout: "linked maps, time series, observed-predicted plots, residual panels, and model-comparison charts" form: "coloured areas, lines, arrows, scatterplots, and uncertainty bands" encoding: "map colour represents residual or predicted rate; wind arrows represent direction and speed; line plots show lagged changes; panels compare alternative models" description: "Visual diagnostic environment for evaluating competing transmission explanations" transform T_interpret_residual_patterns : intent: abstract manner: "perception-and-interpretation" input: V_model_diagnostics, F_model_outputs, K_transmission_hypotheses output: P_model_residual_patterns actor: human description: "Identify coherent unexplained residual hotspots, stable lagged wind effects, spatial propagation waves, water-aligned concentrations, or model-specific failure patterns" artifact P_model_residual_patterns : pattern(F_model_outputs, V_model_diagnostics) representation form: "annotated residual and cross-impact patterns" description: "Observed explanatory successes, failures, and possible missing factors in the transmission models" transform T_assess_hypothesis_models : intent: assess manner: "assess model adequacy and comparative support" input: V_model_diagnostics, P_model_residual_patterns, K_transmission_hypotheses, S_model_current output: K_model_assessment, S_model_refined actor: human description: "Assess predictive performance, residual structure, robustness across wind changes and resolutions, plausibility, and whether model refinement is justified" artifact K_model_assessment : knowledge(F_model_outputs, P_model_residual_patterns) representation form: "comparative quality judgment" description: "Assessment of each transmission model's adequacy, limitations, residual anomalies, and relative operational support" artifact S_model_refined : specification representation form: "revised model configuration" description: "Revised predictors, lag choices, interaction terms, thresholds, validation settings, or feature exclusions justified by residual analysis" if K_model_assessment indicates refinement needed: then: transform T_refine_hypothesis_models : intent: build-model manner: "residual-driven regression refinement" input: D_signal_units, F_outbreak_indicators, F_exposure_predictors, P_model_residual_patterns, S_model_refined, M_contact, M_airborne, M_water, M_point_source, M_mixed output: M_contact_refined, M_airborne_refined, M_water_refined, M_point_source_refined, M_mixed_refined actor: hybrid description: "Refit candidate models after adding, removing, or reconfiguring factors only where diagnostic evidence supports it" artifact M_contact_refined : model(F_outbreak_indicators, F_exposure_predictors) model type: "refined spatiotemporal regression model" description: "Refined contact-propagation explanation" artifact M_airborne_refined : model(F_outbreak_indicators, F_exposure_predictors) model type: "refined spatiotemporal regression model" description: "Refined airborne-spread explanation" artifact M_water_refined : model(F_outbreak_indicators, F_exposure_predictors) model type: "refined spatiotemporal regression model" description: "Refined water-exposure explanation" artifact M_point_source_refined : model(F_outbreak_indicators, F_exposure_predictors) model type: "refined spatiotemporal regression model" description: "Refined common-point-source explanation" artifact M_mixed_refined : model(F_outbreak_indicators, F_exposure_predictors) model type: "refined spatiotemporal regression model" description: "Refined mixed-mechanism explanation" assign: M_contact := M_contact_refined M_airborne := M_airborne_refined M_water := M_water_refined M_point_source := M_point_source_refined M_mixed := M_mixed_refined S_model_current := S_model_refined else: exit loop L_hypothesis_refinement end loop L_hypothesis_refinement # ============================================================================ # STEP 6: INVESTIGATE CANDIDATE SOURCES AND SYNTHESISE EVIDENCE # ============================================================================ transform T_visualise_source_evidence : intent: visualise manner: "linked evidence investigation" input: D_symptom_reports, D_severe_reports, F_place_mentions, P_origin_candidates, F_model_outputs, D_city_features, V_model_diagnostics output: V_source_investigation actor: machine description: "Support inspection of candidate-source messages in temporal order, linked landmarks and facilities, backward tracing from later hotspots, matched comparison areas, and conflicting evidence" artifact V_source_investigation : visualisation(D_symptom_reports, D_severe_reports, F_place_mentions, P_origin_candidates, F_model_outputs, D_city_features) layout: "linked map, message timeline, candidate-source panel, and comparison-area views" form: "points, trajectories, text excerpts, ranked lists, and maps" encoding: "message positions derive from GPS coordinates; temporal order derives from timestamps; overlays show candidate origins, facilities, water bodies, highways, and model-supported exposure relationships" description: "Detailed evidence view for field investigation of possible source locations and exposure mechanisms" transform T_investigate_candidate_sources : intent: generate-knowledge manner: "formulate evidence statements" input: V_source_investigation, K_origin_assessment, K_model_assessment, K_transmission_hypotheses, K_epidemiology output: K_source_investigation actor: human description: "Record source-specific supporting and contradictory evidence, compare matched non-outbreak areas, distinguish infection-source evidence from care-seeking/reporting effects, and identify field-investigation needs" artifact K_source_investigation : knowledge(V_source_investigation, K_origin_assessment, K_model_assessment) representation form: "candidate-source evidence log" description: "Documented interpretation of likely sources, possible mechanisms, uncertainties, contradictions, and recommended validation actions" transform T_synthesise_transmission_assessment : intent: generate-knowledge manner: "formulate-statements" input: K_origin_assessment, K_model_assessment, K_source_investigation, K_transmission_hypotheses, K_signal_validity output: K_transmission_assessment actor: human description: "Synthesize the relative support for person-to-person, airborne, waterborne, point-source, reporting-artifact, and mixed explanations without claiming definitive causation from microblog data" artifact K_transmission_assessment : knowledge(K_origin_assessment, K_model_assessment, K_source_investigation) representation form: "uncertainty-aware explanatory assessment" description: "Ranked transmission hypotheses, supporting and contradictory evidence, uncertainty, and requirements for clinical or environmental confirmation" # ============================================================================ # STEP 7: PRIORITISE DEPLOYMENT AND COMMUNICATE RESULTS # ============================================================================ transform T_compute_resource_priority : intent: characterise manner: "risk and deployment prioritisation" input: D_signal_units, F_outbreak_indicators, F_model_outputs, D_analysis_areas, K_transmission_assessment output: F_resource_priority actor: machine description: "Compute area priority from current intensity, growth rate, severe-report signal, population exposure, hospital context, model support, and uncertainty" artifact F_resource_priority : feature(D_analysis_areas) value structure: vector value type: {numeric, ordinal} representation form: "priority score with explanatory components" description: "Emergency-management priority and contributing risk factors for each analysis area" transform T_visualise_operational_dashboard : intent: visualise manner: "linked emergency-management dashboard" input: A_signal_map, F_outbreak_indicators, P_origin_candidates, K_transmission_assessment, F_resource_priority, D_city_features output: V_operational_dashboard actor: machine description: "Provide current risk, origin candidates, transmission evidence, uncertainty, and resource-priority views in one linked dashboard" artifact V_operational_dashboard : visualisation(A_signal_map, F_outbreak_indicators, P_origin_candidates, K_transmission_assessment, F_resource_priority, D_city_features) layout: "linked current-risk map, origin-ranking panel, transmission-evidence panel, and resource-priority map" form: "coloured areas, ranked lists, time series, symbols, and uncertainty bands" encoding: "area colour and size encode current risk and priority; panels show ranked origin candidates and relative hypothesis support; symbols identify hospitals, highways, landmarks, and water bodies" description: "Operational dashboard for public-health and emergency managers" transform T_formulate_deployment_recommendations : intent: generate-knowledge manner: "derive-rules and write-summary" input: V_operational_dashboard, K_transmission_assessment, K_source_investigation, K_epidemiology output: K_deployment_recommendations, K_update_protocol actor: human description: "Recommend immediate resource deployment, targeted field investigations, clinical and environmental validation, public communication, and documented re-analysis as new posts and weather observations arrive" artifact K_deployment_recommendations : knowledge(V_operational_dashboard, K_transmission_assessment) representation form: "prioritised recommendations" description: "Recommended deployment of testing, clinical outreach, communications, environmental inspection, and emergency resources, with uncertainty-aware rationale" artifact K_update_protocol : knowledge(V_operational_dashboard, K_deployment_recommendations) representation form: "operational update protocol" description: "Instructions to preserve the analysis log and rerun the workflow when new microblogs, weather data, clinical surveillance, or environmental evidence become available"