{"id":"https://openalex.org/W4315779632","doi":"https://doi.org/10.1145/3579815","title":"Spatio-temporal Graph Learning for Epidemic Prediction","display_name":"Spatio-temporal Graph Learning for Epidemic Prediction","publication_year":2023,"publication_date":"2023-01-12","ids":{"openalex":"https://openalex.org/W4315779632","doi":"https://doi.org/10.1145/3579815"},"language":"en","primary_location":{"id":"doi:10.1145/3579815","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3579815","pdf_url":null,"source":{"id":"https://openalex.org/S2492086750","display_name":"ACM Transactions on Intelligent Systems and Technology","issn_l":"2157-6904","issn":["2157-6904","2157-6912"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Intelligent Systems and Technology","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004781883","display_name":"Shuo Yu","orcid":"https://orcid.org/0000-0003-1124-9509"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shuo Yu","raw_affiliation_strings":["Dalian University of Technology, Dalian, China"],"affiliations":[{"raw_affiliation_string":"Dalian University of Technology, Dalian, China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089615958","display_name":"Feng Xia","orcid":"https://orcid.org/0000-0002-8324-1859"},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Feng Xia","raw_affiliation_strings":["RMIT University, Melbourne, VIC, Australia"],"affiliations":[{"raw_affiliation_string":"RMIT University, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I82951845"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035899298","display_name":"Shihao Li","orcid":"https://orcid.org/0000-0003-1735-9899"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shihao Li","raw_affiliation_strings":["Dalian University of Technology, Dalian, China"],"affiliations":[{"raw_affiliation_string":"Dalian University of Technology, Dalian, China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089105523","display_name":"Mingliang Hou","orcid":"https://orcid.org/0000-0001-5225-2195"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingliang Hou","raw_affiliation_strings":["Dalian University of Technology, Dalian, China"],"affiliations":[{"raw_affiliation_string":"Dalian University of Technology, Dalian, China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080744092","display_name":"Quan Z. Sheng","orcid":"https://orcid.org/0000-0002-3326-4147"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Quan Z. Sheng","raw_affiliation_strings":["Macquarie University, Sydney, NSW, Australia"],"affiliations":[{"raw_affiliation_string":"Macquarie University, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I99043593"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5004781883"],"corresponding_institution_ids":["https://openalex.org/I27357992"],"apc_list":null,"apc_paid":null,"fwci":7.034,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.97778478,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"14","issue":"2","first_page":"1","last_page":"25"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9994999766349792,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9911999702453613,"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/computer-science","display_name":"Computer science","score":0.7692849636077881},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.6419183611869812},{"id":"https://openalex.org/keywords/pandemic","display_name":"Pandemic","score":0.5307878255844116},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5173237323760986},{"id":"https://openalex.org/keywords/government","display_name":"Government (linguistics)","score":0.5103136897087097},{"id":"https://openalex.org/keywords/trace","display_name":"TRACE (psycholinguistics)","score":0.45030179619789124},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.42317190766334534},{"id":"https://openalex.org/keywords/psychological-resilience","display_name":"Psychological resilience","score":0.41988930106163025},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41863471269607544},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4134637415409088},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40055426955223083},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.390023797750473},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.37041908502578735},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.14930641651153564},{"id":"https://openalex.org/keywords/infectious-disease","display_name":"Infectious disease (medical specialty)","score":0.1324906349182129},{"id":"https://openalex.org/keywords/politics","display_name":"Politics","score":0.12121167778968811}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7692849636077881},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.6419183611869812},{"id":"https://openalex.org/C89623803","wikidata":"https://www.wikidata.org/wiki/Q12184","display_name":"Pandemic","level":5,"score":0.5307878255844116},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5173237323760986},{"id":"https://openalex.org/C2778137410","wikidata":"https://www.wikidata.org/wiki/Q2732820","display_name":"Government (linguistics)","level":2,"score":0.5103136897087097},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.45030179619789124},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.42317190766334534},{"id":"https://openalex.org/C137176749","wikidata":"https://www.wikidata.org/wiki/Q4105337","display_name":"Psychological resilience","level":2,"score":0.41988930106163025},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41863471269607544},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4134637415409088},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40055426955223083},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.390023797750473},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.37041908502578735},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.14930641651153564},{"id":"https://openalex.org/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.1324906349182129},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.12121167778968811},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"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/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.0},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3579815","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3579815","pdf_url":null,"source":{"id":"https://openalex.org/S2492086750","display_name":"ACM Transactions on Intelligent Systems and Technology","issn_l":"2157-6904","issn":["2157-6904","2157-6912"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Intelligent Systems and Technology","raw_type":"journal-article"},{"id":"pmh:oai:figshare.com:article/27570993","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:figshare.com:article/27570993","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1739276736","display_name":null,"funder_award_id":"DUT22RC(3)060","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G376530787","display_name":null,"funder_award_id":"62102060","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W1071368427","https://openalex.org/W1689711448","https://openalex.org/W1924770834","https://openalex.org/W1971993044","https://openalex.org/W1991288592","https://openalex.org/W2012808668","https://openalex.org/W2020165868","https://openalex.org/W2514025363","https://openalex.org/W2531563875","https://openalex.org/W2564922739","https://openalex.org/W2602856279","https://openalex.org/W2604803309","https://openalex.org/W2734475807","https://openalex.org/W2776370812","https://openalex.org/W2809035759","https://openalex.org/W2809086059","https://openalex.org/W2809583854","https://openalex.org/W2894692710","https://openalex.org/W2901504064","https://openalex.org/W2911392324","https://openalex.org/W2911752602","https://openalex.org/W2914588721","https://openalex.org/W2945319864","https://openalex.org/W2945827377","https://openalex.org/W2950369002","https://openalex.org/W2952734551","https://openalex.org/W2966293752","https://openalex.org/W2976046728","https://openalex.org/W3004280078","https://openalex.org/W3014578296","https://openalex.org/W3023190093","https://openalex.org/W3028009638","https://openalex.org/W3033094519","https://openalex.org/W3034373693","https://openalex.org/W3038787377","https://openalex.org/W3044468596","https://openalex.org/W3048307099","https://openalex.org/W3080252065","https://openalex.org/W3080466448","https://openalex.org/W3080748023","https://openalex.org/W3093639344","https://openalex.org/W3093695087","https://openalex.org/W3094246578","https://openalex.org/W3095263534","https://openalex.org/W3098208509","https://openalex.org/W3100762626","https://openalex.org/W3101553402","https://openalex.org/W3105463319","https://openalex.org/W3105818684","https://openalex.org/W3109518304","https://openalex.org/W3119880924","https://openalex.org/W3132012119","https://openalex.org/W3144557990","https://openalex.org/W3157395621","https://openalex.org/W3157999218","https://openalex.org/W3165093051","https://openalex.org/W3168186765","https://openalex.org/W3217451197","https://openalex.org/W4206670236","https://openalex.org/W4226116435","https://openalex.org/W4240777659","https://openalex.org/W4287183381","https://openalex.org/W4288335984"],"related_works":["https://openalex.org/W2383111961","https://openalex.org/W2365952365","https://openalex.org/W2352448290","https://openalex.org/W2380820513","https://openalex.org/W2913146933","https://openalex.org/W2372385138","https://openalex.org/W4296359239","https://openalex.org/W2043093291","https://openalex.org/W2101155126","https://openalex.org/W2363545964"],"abstract_inverted_index":{"The":[0],"COVID-19":[1,85],"pandemic":[2,129,198],"has":[3],"posed":[4],"great":[5],"challenges":[6],"to":[7,73,76,89,95,127,161],"public":[8,20],"health":[9,21],"services,":[10],"government":[11],"agencies,":[12],"and":[13,22,80,119,145,183,213],"policymakers,":[14],"raising":[15],"huge":[16],"social":[17,81],"conflicts":[18],"between":[19],"economic":[23],"resilience.":[24],"Policies":[25],"such":[26],"as":[27,113],"reopening":[28],"or":[29],"closure":[30],"of":[31,40,62,132,186],"business":[32],"activities":[33],"are":[34],"formulated":[35],"based":[36],"on":[37],"scientific":[38],"projections":[39],"infection":[41,45,130,199],"risks":[42],"obtained":[43],"from":[44],"dynamics":[46],"models.":[47],"Though":[48],"most":[49],"parameters":[50],"in":[51,158,210],"epidemic":[52],"prediction":[53],"service":[54],"models":[55],"can":[56,194],"be":[57],"set":[58],"with":[59],"domain":[60],"knowledge":[61],"COVID-19,":[63],"a":[64,114,122,133,154,166],"key":[65],"parameter,":[66],"namely,":[67],"human":[68],"mobility,":[69],"is":[70,100],"often":[71],"challenging":[72],"estimate":[74,128],"due":[75],"complex":[77],"spatio-temporal":[78,115],"correlations":[79],"contexts":[82],"under":[83],"escalating":[84],"facilities.":[86],"Moreover,":[87,205],"how":[88],"integrate":[90],"the":[91,111,175,187],"various":[92,137],"implicit":[93,163],"features":[94,164],"accurately":[96],"predict":[97],"infectious":[98],"cases":[99],"still":[101],"an":[102],"open":[103],"issue.":[104],"To":[105],"address":[106],"this":[107],"challenge,":[108],"we":[109],"formulate":[110],"problem":[112,118],"network":[116],"representation":[117,159],"propose":[120],"STEP,":[121],"Spatio-Temporal":[123],"Epidemic":[124],"Prediction":[125],"framework,":[126],"risk":[131,200],"city":[134],"by":[135],"integrating":[136],"real-world":[138,176],"conditions":[139],"(e.g.,":[140],"City":[141],"Risk":[142],"Index,":[143],"climate,":[144],"medical":[146],"conditions)":[147],"into":[148],"graph-structured":[149],"data.":[150],"We":[151],"also":[152],"employ":[153],"multi-head":[155],"attention":[156],"mechanism":[157],"learning":[160],"extract":[162],"for":[165,178],"given":[167],"city.":[168],"Extensive":[169],"experiments":[170],"have":[171],"been":[172],"conducted":[173],"upon":[174],"dataset":[177],"51":[179],"states":[180,182],"(50":[181],"Washington,":[184],"D.C.)":[185],"USA.":[188],"Experimental":[189],"results":[190],"show":[191],"that":[192],"STEP":[193,206],"yield":[195],"more":[196],"accurate":[197],"estimation":[201],"than":[202],"baseline":[203],"methods.":[204],"outperforms":[207],"other":[208],"methods":[209],"both":[211],"short-term":[212],"long-term":[214],"prediction.":[215]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":5}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}