{"id":"https://openalex.org/W3095636618","doi":"https://doi.org/10.1145/3431843.3431848","title":"COVID-19 ensemble models using representative clustering","display_name":"COVID-19 ensemble models using representative clustering","publication_year":2020,"publication_date":"2020-10-26","ids":{"openalex":"https://openalex.org/W3095636618","doi":"https://doi.org/10.1145/3431843.3431848","mag":"3095636618"},"language":"en","primary_location":{"id":"doi:10.1145/3431843.3431848","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3431843.3431848","pdf_url":null,"source":{"id":"https://openalex.org/S27924493","display_name":"SIGSPATIAL Special","issn_l":"1946-7729","issn":["1946-7729"],"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":"SIGSPATIAL Special","raw_type":"journal-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/A5101811186","display_name":"Joon-Seok Kim","orcid":"https://orcid.org/0000-0003-1796-7851"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joon-Seok Kim","raw_affiliation_strings":["George Mason University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"George Mason University","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055131878","display_name":"Hamdi Kavak","orcid":"https://orcid.org/0000-0003-4307-2381"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hamdi Kavak","raw_affiliation_strings":["George Mason University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"George Mason University","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017299501","display_name":"Andreas Z\u00fcfle","orcid":"https://orcid.org/0000-0001-7001-4123"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andreas Z\u00fcfle","raw_affiliation_strings":["George Mason University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"George Mason University","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017812942","display_name":"Taylor Anderson","orcid":"https://orcid.org/0000-0003-1145-0608"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Taylor Anderson","raw_affiliation_strings":["George Mason University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"George Mason University","institution_ids":["https://openalex.org/I162714631"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I162714631"],"apc_list":null,"apc_paid":null,"fwci":0.3728,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.68280146,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"12","issue":"2","first_page":"33","last_page":"41"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10410","display_name":"COVID-19 epidemiological studies","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9955999851226807,"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":0.987500011920929,"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/cluster-analysis","display_name":"Cluster analysis","score":0.7495201826095581},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7097310423851013},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.6214103102684021},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5642400979995728},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5120115876197815},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.4677667021751404},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.46611693501472473},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4596744179725647},{"id":"https://openalex.org/keywords/confusion","display_name":"Confusion","score":0.4505709111690521},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.44876596331596375},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3934360146522522},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34703344106674194},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.3193400502204895},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14277157187461853},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07980212569236755}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7495201826095581},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7097310423851013},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.6214103102684021},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5642400979995728},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5120115876197815},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.4677667021751404},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.46611693501472473},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4596744179725647},{"id":"https://openalex.org/C2781140086","wikidata":"https://www.wikidata.org/wiki/Q557945","display_name":"Confusion","level":2,"score":0.4505709111690521},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.44876596331596375},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3934360146522522},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34703344106674194},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.3193400502204895},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14277157187461853},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07980212569236755},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C11171543","wikidata":"https://www.wikidata.org/wiki/Q41630","display_name":"Psychoanalysis","level":1,"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/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3431843.3431848","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3431843.3431848","pdf_url":null,"source":{"id":"https://openalex.org/S27924493","display_name":"SIGSPATIAL Special","issn_l":"1946-7729","issn":["1946-7729"],"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":"SIGSPATIAL Special","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7300000190734863,"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":26,"referenced_works":["https://openalex.org/W28412257","https://openalex.org/W638315772","https://openalex.org/W1987434040","https://openalex.org/W2017841038","https://openalex.org/W2021510303","https://openalex.org/W2065069831","https://openalex.org/W2072381293","https://openalex.org/W2100956194","https://openalex.org/W2102937240","https://openalex.org/W2107324390","https://openalex.org/W2167917621","https://openalex.org/W2531458834","https://openalex.org/W2596118091","https://openalex.org/W2614680545","https://openalex.org/W2747968860","https://openalex.org/W2767745493","https://openalex.org/W2968232154","https://openalex.org/W2988205563","https://openalex.org/W3023441241","https://openalex.org/W3031202235","https://openalex.org/W3035826609","https://openalex.org/W3047979380","https://openalex.org/W4285719527","https://openalex.org/W4300988338","https://openalex.org/W6684805940","https://openalex.org/W6734582538"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549","https://openalex.org/W3123177881"],"abstract_inverted_index":{"In":[0],"response":[1],"to":[2,11,15,71,124,127],"the":[3,18,21,48,53,56,99,103,106,110,129,139,160,170],"COVID-19":[4,78],"pandemic,":[5],"there":[6],"have":[7],"been":[8],"various":[9],"attempts":[10],"develop":[12],"realistic":[13],"models":[14,31],"both":[16],"predict":[17],"spread":[19,79],"of":[20,55,77,89,98,105,112,131,138,146],"disease":[22],"and":[23,37,47,51,81,118,159,169],"evaluate":[24],"policy":[25],"measures":[26],"aimed":[27],"at":[28],"mitigation.":[29],"Different":[30],"that":[32,67,150,157],"operate":[33],"under":[34],"different":[35,41],"parameters":[36],"assumptions":[38],"produce":[39,143],"radically":[40],"predictions,":[42],"creating":[43],"confusion":[44],"among":[45,172],"policy-makers":[46,158],"general":[49,161],"population":[50],"limiting":[52],"usefulness":[54],"models.":[57],"This":[58],"newsletter":[59],"article":[60],"proposes":[61],"a":[62,86,144],"novel":[63],"ensemble":[64,93,107,147],"modeling":[65],"approach":[66,95,141],"uses":[68],"representative":[69,120],"clustering":[70,121],"identify":[72,151],"where":[73,152],"existing":[74],"model":[75,148],"predictions":[76,84,114,149,168],"agree":[80],"unify":[82],"these":[83],"into":[85],"smaller":[87],"set":[88,145],"predictions.":[90,135],"The":[91,136],"proposed":[92,140],"prediction":[94],"is":[96],"composed":[97],"following":[100],"stages:":[101],"(1)":[102],"selection":[104],"components,":[108],"(2)":[109],"imputation":[111],"missing":[113],"for":[115],"each":[116],"component,":[117],"(3)":[119],"in":[122],"application":[123],"time-series":[125],"data":[126],"determine":[128],"degree":[130],"agreement":[132],"between":[133],"simulation":[134,153],"results":[137,154],"will":[142],"converge":[155],"so":[156],"public":[162],"are":[163],"informed":[164],"with":[165],"more":[166],"comprehensive":[167],"uncertainty":[171],"them.":[173]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
