{"id":"https://openalex.org/W4318147791","doi":"https://doi.org/10.1109/bigdata55660.2022.10020969","title":"Explainable Prediction of the Severity of COVID-19 Outbreak for US Counties","display_name":"Explainable Prediction of the Severity of COVID-19 Outbreak for US Counties","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318147791","doi":"https://doi.org/10.1109/bigdata55660.2022.10020969"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020969","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020969","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004703367","display_name":"Shailik Sarkar","orcid":"https://orcid.org/0000-0001-6544-2262"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shailik Sarkar","raw_affiliation_strings":["Virginia Tech,Department of Computer Science,Falls Church,VA,USA,22043"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Virginia Tech,Department of Computer Science,Falls Church,VA,USA,22043","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054580543","display_name":"Abdulaziz Alhamadani","orcid":"https://orcid.org/0009-0003-4732-5144"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abdulaziz Alhamadani","raw_affiliation_strings":["Virginia Tech,Department of Computer Science,Falls Church,VA,USA,22043"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Virginia Tech,Department of Computer Science,Falls Church,VA,USA,22043","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038002204","display_name":"Chang\u2010Tien Lu","orcid":"https://orcid.org/0000-0003-3675-0199"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chang-Tien Lu","raw_affiliation_strings":["Virginia Tech,Department of Computer Science,Falls Church,VA,USA,22043"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Virginia Tech,Department of Computer Science,Falls Church,VA,USA,22043","institution_ids":["https://openalex.org/I859038795"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.43495935,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5338","last_page":"5345"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10410","display_name":"COVID-19 epidemiological studies","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10410","display_name":"COVID-19 epidemiological studies","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9929999709129333,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9891999959945679,"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/pandemic","display_name":"Pandemic","score":0.714729368686676},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7136123180389404},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.6053133606910706},{"id":"https://openalex.org/keywords/outbreak","display_name":"Outbreak","score":0.6014028787612915},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5444837212562561},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4954451322555542},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.48852837085723877},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42116910219192505},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4037128686904907},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38710957765579224},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3816959261894226},{"id":"https://openalex.org/keywords/infectious-disease","display_name":"Infectious disease (medical specialty)","score":0.3641069531440735},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3529728651046753},{"id":"https://openalex.org/keywords/disease","display_name":"Disease","score":0.197648823261261},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1580594778060913},{"id":"https://openalex.org/keywords/demography","display_name":"Demography","score":0.14386528730392456},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.10610848665237427},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07784855365753174}],"concepts":[{"id":"https://openalex.org/C89623803","wikidata":"https://www.wikidata.org/wiki/Q12184","display_name":"Pandemic","level":5,"score":0.714729368686676},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7136123180389404},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.6053133606910706},{"id":"https://openalex.org/C116675565","wikidata":"https://www.wikidata.org/wiki/Q3241045","display_name":"Outbreak","level":2,"score":0.6014028787612915},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5444837212562561},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4954451322555542},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.48852837085723877},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42116910219192505},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4037128686904907},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38710957765579224},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3816959261894226},{"id":"https://openalex.org/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.3641069531440735},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3529728651046753},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.197648823261261},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1580594778060913},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.14386528730392456},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.10610848665237427},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07784855365753174},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C159047783","wikidata":"https://www.wikidata.org/wiki/Q7215","display_name":"Virology","level":1,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020969","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020969","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.8399999737739563,"display_name":"Good health and well-being"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1548037568","https://openalex.org/W2029262537","https://openalex.org/W2113867961","https://openalex.org/W2758000438","https://openalex.org/W2953231487","https://openalex.org/W2962711740","https://openalex.org/W2964015378","https://openalex.org/W2972317931","https://openalex.org/W2982110820","https://openalex.org/W3006914768","https://openalex.org/W3024182761","https://openalex.org/W3037898693","https://openalex.org/W3043618167","https://openalex.org/W3046379335","https://openalex.org/W3088179043","https://openalex.org/W3088534488","https://openalex.org/W3094397675","https://openalex.org/W3103717137","https://openalex.org/W3123259284","https://openalex.org/W3173994567","https://openalex.org/W3214380316","https://openalex.org/W4205957872","https://openalex.org/W4297733535","https://openalex.org/W6726873649","https://openalex.org/W6754929296","https://openalex.org/W6767288045","https://openalex.org/W6781588999","https://openalex.org/W6784106106","https://openalex.org/W6786048916"],"related_works":["https://openalex.org/W2373635223","https://openalex.org/W2412355096","https://openalex.org/W1990012352","https://openalex.org/W2431766951","https://openalex.org/W4385969441","https://openalex.org/W127458931","https://openalex.org/W2362266265","https://openalex.org/W3028429280","https://openalex.org/W2378327072","https://openalex.org/W326517241"],"abstract_inverted_index":{"Ever":[0],"since":[1],"the":[2,20,28,46,60,72,101,104,148,151,165,188,196,200,208],"COVID-19":[3],"outbreak,":[4,215],"various":[5],"works":[6],"have":[7],"focused":[8],"on":[9,56,158],"using":[10],"multitude":[11],"of":[12,22,30,64,103,114,123,136,150,167,191,199],"different":[13],"static":[14,115,175],"and":[15,202],"dynamic":[16],"features":[17,122,146,176],"to":[18,40,52,75,118,129,163,186,206],"aid":[19],"prediction":[21,99],"disease":[23,214],"forecasting":[24],"models.":[25],"However,":[26],"in":[27,193],"absence":[29],"historical":[31,218],"pandemic":[32,161,201],"data":[33,162,219],"these":[34],"models":[35],"will":[36],"not":[37,221],"be":[38,53,184],"able":[39],"give":[41],"any":[42,77,212],"meaningful":[43],"insight":[44],"about":[45],"areas":[47,210],"which":[48],"are":[49],"most":[50],"likely":[51],"affected":[54],"based":[55],"preexisting":[57],"conditions.":[58],"Furthermore,":[59],"black":[61],"box":[62],"nature":[63],"neural":[65],"networks":[66],"often":[67],"becomes":[68],"an":[69],"impediment":[70],"for":[71,100,143,211],"concerned":[73],"authorities":[74],"derive":[76],"meaning":[78],"from.":[79],"In":[80],"this":[81],"paper,":[82],"we":[83,139,172],"propose":[84,140],"a":[85,111,130,141,204],"novel":[86],"explainable":[87,98],"Graph":[88,124],"Neural":[89],"Network":[90],"(GNN)":[91],"framework":[92,142,205],"called":[93],"Graph-COVID-19-Explainer":[94],"(GC-Explainer)":[95],"that":[96,174],"gives":[97],"severity":[102,192],"spread":[105],"during":[106,147,195],"initial":[107],"outbreak.":[108],"We":[109,153],"utilize":[110],"comprehensive":[112],"set":[113],"population":[116],"characteristics":[117],"use":[119],"as":[120],"node":[121,127],"where":[125],"each":[126],"corresponds":[128],"geographical":[131],"region.":[132],"Unlike":[133],"post-hoc":[134],"methods":[135],"GNN":[137],"explanations,":[138],"learning":[144],"important":[145],"training":[149],"model.":[152],"further":[154],"apply":[155],"our":[156,168],"model":[157],"real-world":[159],"early":[160,197],"show":[164,173],"validity":[166],"approach.":[169],"Through":[170],"GC-Explainer,":[171],"along":[177],"with":[178],"spatial":[179],"dependency":[180],"among":[181],"regions":[182],"can":[183],"used":[185],"explain":[187],"varied":[189],"degree":[190],"outbreak":[194],"part":[198],"provide":[203],"identify":[207],"at-risk":[209],"infectious":[213],"especially":[216],"when":[217],"is":[220],"available.":[222]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}