{"id":"https://openalex.org/W7169813683","doi":"https://doi.org/10.48550/arxiv.2607.15544","title":"EpiNarrate: Agentic Generation of Grounded Narratives from Epidemiological Scenario Projections","display_name":"EpiNarrate: Agentic Generation of Grounded Narratives from Epidemiological Scenario Projections","publication_year":2026,"publication_date":"2026-07-17","ids":{"openalex":"https://openalex.org/W7169813683","doi":"https://doi.org/10.48550/arxiv.2607.15544"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.15544","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.15544","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.15544","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084806883","display_name":"Rituparna Datta","orcid":"https://orcid.org/0000-0003-3816-2438"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Datta, Rituparna","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108316950","display_name":"Srini Venkatramanan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Venkatramanan, Srini","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137277501","display_name":"Bryan L. Lewis","orcid":"https://orcid.org/0000-0003-0793-6082"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lewis, Bryan L.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027153226","display_name":"Y W Su","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Su, Yiqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135539234","display_name":"Harry Hochheiser","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hochheiser, Harry","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082249376","display_name":"Lucie Contamin","orcid":"https://orcid.org/0000-0001-5797-1279"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Contamin, Lucie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064307334","display_name":"Parantapa Bhattacharya","orcid":"https://orcid.org/0000-0002-3626-9939"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bhattacharya, Parantapa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141185059","display_name":"Naren Ramakrishnan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ramakrishnan, Naren","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5141189690","display_name":"Anil Vullikanti","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vullikanti, Anil","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.23149999976158142,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.23149999976158142,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.14069999754428864,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.1404000073671341,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/narrative","display_name":"Narrative","score":0.7297999858856201},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.6801000237464905},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5038999915122986},{"id":"https://openalex.org/keywords/agency","display_name":"Agency (philosophy)","score":0.4059999883174896},{"id":"https://openalex.org/keywords/grammar","display_name":"Grammar","score":0.3952000141143799},{"id":"https://openalex.org/keywords/lexicon","display_name":"Lexicon","score":0.36820000410079956},{"id":"https://openalex.org/keywords/tree-traversal","display_name":"Tree traversal","score":0.3481000065803528},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.33869999647140503}],"concepts":[{"id":"https://openalex.org/C199033989","wikidata":"https://www.wikidata.org/wiki/Q1318295","display_name":"Narrative","level":2,"score":0.7297999858856201},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.6801000237464905},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.576200008392334},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5038999915122986},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4334000051021576},{"id":"https://openalex.org/C108170787","wikidata":"https://www.wikidata.org/wiki/Q3951828","display_name":"Agency (philosophy)","level":2,"score":0.4059999883174896},{"id":"https://openalex.org/C26022165","wikidata":"https://www.wikidata.org/wiki/Q8091","display_name":"Grammar","level":2,"score":0.3952000141143799},{"id":"https://openalex.org/C2778121359","wikidata":"https://www.wikidata.org/wiki/Q8096","display_name":"Lexicon","level":2,"score":0.36820000410079956},{"id":"https://openalex.org/C140745168","wikidata":"https://www.wikidata.org/wiki/Q1210082","display_name":"Tree traversal","level":2,"score":0.3481000065803528},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.33869999647140503},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.337799996137619},{"id":"https://openalex.org/C132829578","wikidata":"https://www.wikidata.org/wiki/Q581151","display_name":"Situated","level":2,"score":0.3377000093460083},{"id":"https://openalex.org/C156325361","wikidata":"https://www.wikidata.org/wiki/Q1152864","display_name":"Grounded theory","level":3,"score":0.33180001378059387},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3111000061035156},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.29170000553131104},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.28850001096725464},{"id":"https://openalex.org/C167651023","wikidata":"https://www.wikidata.org/wiki/Q1474611","display_name":"Plot (graphics)","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.27959999442100525},{"id":"https://openalex.org/C2780665704","wikidata":"https://www.wikidata.org/wiki/Q959298","display_name":"Intervention (counseling)","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.2782000005245209},{"id":"https://openalex.org/C197947376","wikidata":"https://www.wikidata.org/wiki/Q5155608","display_name":"Comparability","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C117893075","wikidata":"https://www.wikidata.org/wiki/Q6966213","display_name":"Narrative inquiry","level":3,"score":0.26429998874664307},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.26249998807907104},{"id":"https://openalex.org/C2777877512","wikidata":"https://www.wikidata.org/wiki/Q1116097","display_name":"Common ground","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.25450000166893005}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.15544","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.15544","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.15544","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.15544","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.6267125010490417,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Generation":[0],"of":[1,120,181,189],"clear":[2],"and":[3,17,35,55,61,73,82,110,131,143,149,178],"accessible":[4],"public":[5,20,92],"health":[6,93],"narratives":[7,24,173],"is":[8],"critical":[9],"for":[10,91],"communicating":[11],"complex":[12],"epidemiological":[13,183],"projections":[14,31,42],"to":[15,71,79],"policymakers":[16],"the":[18,121,163,187],"general":[19],"at":[21],"large.":[22],"Such":[23],"require":[25],"more":[26],"than":[27],"simply":[28],"reporting":[29],"numbers:":[30],"must":[32],"be":[33],"contextualized":[34],"quantitatively":[36],"grounded":[37],"across":[38],"multiple":[39],"dimensions.":[40],"Further,":[41],"are":[43],"often":[44,77],"derived":[45],"from":[46,101],"large":[47,67],"ensemble":[48],"datasets":[49],"which":[50],"combine":[51],"intervention":[52],"assumptions,":[53],"geographic":[54],"demographic":[56],"strata,":[57],"outcomes,":[58],"time":[59],"horizons,":[60],"uncertainty":[62],"quantiles.":[63],"However,":[64],"directly":[65],"using":[66],"language":[68],"models":[69],"(LLMs)":[70],"summarize":[72],"contextualize":[74],"such":[75],"data":[76],"leads":[78],"inconsistencies,":[80],"omissions,":[81],"fragile":[83],"behavior.":[84],"We":[85],"introduce":[86,152],"an":[87,128,153],"agentic":[88],"framework":[89,105],"(EpiNarrate)":[90],"report":[94],"generation":[95],"that":[96,140,169],"separates":[97],"structured":[98],"numerical":[99],"reasoning":[100],"natural-language":[102],"generation.":[103],"The":[104],"first":[106],"extracts":[107],"scenario":[108],"axes":[109],"organizes":[111],"them":[112],"into":[113],"a":[114,137],"partial-order":[115],"schema,":[116],"enabling":[117],"systematic":[118],"traversal":[119],"underlying":[122],"multidimensional":[123],"space.":[124],"It":[125],"then":[126],"constructs":[127],"augmented":[129],"dataset":[130],"derives":[132],"valid":[133],"quantitative":[134],"statements":[135],"through":[136],"comparison":[138],"grammar":[139],"enforces":[141],"semantic":[142],"arithmetic":[144],"consistency.":[145],"To":[146],"balance":[147],"coverage":[148,180],"non-redundancy,":[150],"we":[151],"interestingness-driven":[154],"selection":[155],"mechanism":[156],"based":[157],"on":[158,162],"maximum-entropy":[159],"principles.":[160],"Experiments":[161],"COVID-19":[164],"Scenario":[165],"Modeling":[166],"Hub":[167],"demonstrate":[168],"our":[170],"model":[171],"produces":[172],"with":[174],"improved":[175],"factual":[176],"grounding":[177],"broader":[179],"salient":[182],"patterns,":[184],"while":[185],"preserving":[186],"style":[188],"expert-written":[190],"reports.":[191]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-21T00:00:00"}
