{"id":"https://openalex.org/W3210003094","doi":"https://doi.org/10.1145/3459637.3482356","title":"Into the Unobservables","display_name":"Into the Unobservables","publication_year":2021,"publication_date":"2021-10-26","ids":{"openalex":"https://openalex.org/W3210003094","doi":"https://doi.org/10.1145/3459637.3482356","mag":"3210003094"},"language":"en","primary_location":{"id":"doi:10.1145/3459637.3482356","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482356","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information &amp; Knowledge Management","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/A5055065511","display_name":"Yue Cui","orcid":"https://orcid.org/0000-0002-1656-5407"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yue Cui","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009658768","display_name":"Chen Zhu","orcid":"https://orcid.org/0000-0002-1002-8387"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Zhu","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106667829","display_name":"Guanyu Ye","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guanyu Ye","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100389365","display_name":"Ziwei Wang","orcid":"https://orcid.org/0000-0001-7813-4488"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziwei Wang","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100603979","display_name":"Kai Zheng","orcid":"https://orcid.org/0000-0002-0217-3998"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Zheng","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5055065511"],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":1.1948,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.79470098,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"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/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9984999895095825,"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":0.9984999895095825,"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/T10410","display_name":"COVID-19 epidemiological studies","score":0.9976000189781189,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9975000023841858,"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":0.7879018783569336},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.603543758392334},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.5784711837768555},{"id":"https://openalex.org/keywords/metropolitan-area","display_name":"Metropolitan area","score":0.4945771396160126},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4059014916419983},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.356838583946228},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.32736876606941223},{"id":"https://openalex.org/keywords/disease","display_name":"Disease","score":0.16529184579849243},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09581702947616577},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.09558916091918945}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7879018783569336},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.603543758392334},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.5784711837768555},{"id":"https://openalex.org/C158739034","wikidata":"https://www.wikidata.org/wiki/Q1907114","display_name":"Metropolitan area","level":2,"score":0.4945771396160126},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4059014916419983},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.356838583946228},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.32736876606941223},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.16529184579849243},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09581702947616577},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.09558916091918945},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3459637.3482356","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482356","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8199999928474426,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1973943669","https://openalex.org/W2016674662","https://openalex.org/W2626778328","https://openalex.org/W2798058877","https://openalex.org/W2903871660","https://openalex.org/W2962790412","https://openalex.org/W3009876049","https://openalex.org/W3010233963","https://openalex.org/W3015379812","https://openalex.org/W3015623674","https://openalex.org/W3022306516","https://openalex.org/W3028192203","https://openalex.org/W3034159733","https://openalex.org/W3039629996","https://openalex.org/W3041064065","https://openalex.org/W3045835659","https://openalex.org/W3080678184","https://openalex.org/W3081224453","https://openalex.org/W3087257704","https://openalex.org/W3088493855","https://openalex.org/W3099011804","https://openalex.org/W3111507638","https://openalex.org/W3197737372","https://openalex.org/W4206482253"],"related_works":["https://openalex.org/W4212929323","https://openalex.org/W2045046253","https://openalex.org/W2000995042","https://openalex.org/W2494740635","https://openalex.org/W1632599465","https://openalex.org/W3177269507","https://openalex.org/W1563545158","https://openalex.org/W2091545482","https://openalex.org/W2379499532","https://openalex.org/W2115206115"],"abstract_inverted_index":{"The":[0,170],"ongoing":[1],"COVID-19":[2,15,33,179],"pandemic":[3],"has":[4],"dramatically":[5],"changed":[6],"people's":[7],"daily":[8,186],"lives.":[9],"A":[10],"robust":[11],"forecasting":[12],"model":[13,145,205],"for":[14,19,32],"infections":[16],"is":[17],"essential":[18],"governments":[20],"and":[21,26,52,100,107,141,156,185,202,220],"institutions":[22],"to":[23,64,133,194],"plan":[24],"timely":[25],"perform":[27],"accurate":[28],"interventions.":[29],"Mainstream":[30],"solutions":[31],"prediction":[34,184,187],"fit":[35],"reported":[36,127],"data":[37,95,114,214],"only":[38,207],"by":[39],"considering":[40],"observed":[41,140],"cases.":[42],"However,":[43],"the":[44,56,76,80,89,118,122,136,146,163,168,196,204,210],"neglected":[45],"facts":[46,54],"that":[47],"positive":[48],"samples":[49],"are":[50,59,160],"incomplete":[51],"many":[53],"of":[55,79,138,148,154,162,198,212],"novel":[57],"disease":[58,149],"unknown":[60],"may":[61],"be":[62],"prone":[63],"cause":[65],"severe":[66],"error":[67],"accumulation,":[68],"especially":[69],"in":[70,88,117,150,167],"long-term":[71],"predictions.":[72],"To":[73],"fully":[74],"understand":[75],"spreading":[77,147],"patterns":[78],"virus,":[81],"we":[82,91,120],"propose":[83],"an":[84],"encoder-decoder":[85],"framework:":[86],"(i)":[87],"encoder":[90],"embed":[92],"historical":[93],"case":[94],"into":[96],"multiple":[97],"expose-infection":[98],"ranges":[99,109],"learn":[101],"message":[102],"passing":[103],"between":[104],"time":[105],"slices":[106],"across":[108],"with":[110,209,217],"coarse-grained":[111],"human":[112],"mobility":[113,213],"incorporated;":[115],"(ii)":[116],"decoder,":[119],"decode":[121],"embedded":[123],"features":[124],"based":[125],"on":[126,177,181],"cases":[128,219],"as":[129,131],"well":[130],"deaths":[132],"jointly":[134],"consider":[135],"effect":[137],"both":[139,182],"hidden":[142],"data.":[143],"We":[144,189],"over":[151],"60":[152],"counties":[153],"California":[155],"New":[157],"York,":[158],"which":[159,222],"two":[161],"most":[164],"metropolitan":[165],"areas":[166],"US.":[169],"proposed":[171],"framework":[172],"significantly":[173],"outperforms":[174],"state-of-the-art":[175],"baselines":[176],"JHU":[178],"dataset":[180],"weekly":[183],"tasks.":[188],"design":[190],"detailed":[191],"ablation":[192],"studies":[193],"verify":[195],"effectiveness":[197],"each":[199],"key":[200],"module":[201],"find":[203],"not":[206],"works":[208],"assistance":[211],"but":[215],"also":[216],"purely":[218],"deaths,":[221],"implies":[223],"its":[224],"broad":[225],"application":[226],"scenarios.":[227]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
