{"id":"https://openalex.org/W4225713811","doi":"https://doi.org/10.1080/01969722.2022.2103614","title":"Fine-Grained Population Mobility Data-Based Community-Level COVID-19 Prediction Model","display_name":"Fine-Grained Population Mobility Data-Based Community-Level COVID-19 Prediction Model","publication_year":2022,"publication_date":"2022-07-23","ids":{"openalex":"https://openalex.org/W4225713811","doi":"https://doi.org/10.1080/01969722.2022.2103614"},"language":"en","primary_location":{"id":"doi:10.1080/01969722.2022.2103614","is_oa":false,"landing_page_url":"https://doi.org/10.1080/01969722.2022.2103614","pdf_url":null,"source":{"id":"https://openalex.org/S117436046","display_name":"Cybernetics & Systems","issn_l":"0196-9722","issn":["0196-9722","1087-6553"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cybernetics and Systems","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/A5079877927","display_name":"Pengyue Jia","orcid":null},"institutions":[{"id":"https://openalex.org/I168879160","display_name":"Zhejiang University of Science and Technology","ror":"https://ror.org/05mx0wr29","country_code":"CN","type":"education","lineage":["https://openalex.org/I168879160"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengyue Jia","raw_affiliation_strings":["College of Computer Science and Technology, Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I168879160"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100411139","display_name":"Ling Chen","orcid":"https://orcid.org/0000-0003-1934-5992"},"institutions":[{"id":"https://openalex.org/I168879160","display_name":"Zhejiang University of Science and Technology","ror":"https://ror.org/05mx0wr29","country_code":"CN","type":"education","lineage":["https://openalex.org/I168879160"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ling Chen","raw_affiliation_strings":["College of Computer Science and Technology, Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I168879160"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102447768","display_name":"Dandan Lyu","orcid":null},"institutions":[{"id":"https://openalex.org/I168879160","display_name":"Zhejiang University of Science and Technology","ror":"https://ror.org/05mx0wr29","country_code":"CN","type":"education","lineage":["https://openalex.org/I168879160"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dandan Lyu","raw_affiliation_strings":["College of Computer Science and Technology, Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I168879160"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100411139"],"corresponding_institution_ids":["https://openalex.org/I168879160"],"apc_list":null,"apc_paid":null,"fwci":1.3334,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.80958421,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"55","issue":"1","first_page":"184","last_page":"202"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9988999962806702,"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"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9988999962806702,"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/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9983000159263611,"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":0.9916999936103821,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6674423813819885},{"id":"https://openalex.org/keywords/geographic-information-system","display_name":"Geographic information system","score":0.5719820857048035},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.5476464629173279},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5417935848236084},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.5138011574745178},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.475192666053772},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.4601270854473114},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.42322731018066406},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.4229182004928589},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.34811627864837646},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.33807075023651123},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.3235393464565277},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.266979455947876},{"id":"https://openalex.org/keywords/demography","display_name":"Demography","score":0.12671390175819397},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.09423193335533142},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.0865720808506012}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6674423813819885},{"id":"https://openalex.org/C41856607","wikidata":"https://www.wikidata.org/wiki/Q483130","display_name":"Geographic information system","level":2,"score":0.5719820857048035},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.5476464629173279},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5417935848236084},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.5138011574745178},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.475192666053772},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.4601270854473114},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.42322731018066406},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.4229182004928589},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.34811627864837646},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.33807075023651123},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.3235393464565277},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.266979455947876},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.12671390175819397},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.09423193335533142},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.0865720808506012},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"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},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"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/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/01969722.2022.2103614","is_oa":false,"landing_page_url":"https://doi.org/10.1080/01969722.2022.2103614","pdf_url":null,"source":{"id":"https://openalex.org/S117436046","display_name":"Cybernetics & Systems","issn_l":"0196-9722","issn":["0196-9722","1087-6553"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cybernetics and Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.8299999833106995,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2118898434","https://openalex.org/W2604847698","https://openalex.org/W2756203131","https://openalex.org/W2798329844","https://openalex.org/W3014768759","https://openalex.org/W3014916324","https://openalex.org/W3019351867","https://openalex.org/W3020878652","https://openalex.org/W3026475761","https://openalex.org/W3035619533","https://openalex.org/W3036309913","https://openalex.org/W3036356470","https://openalex.org/W3038787377","https://openalex.org/W3042316884","https://openalex.org/W3043618167","https://openalex.org/W3049737176","https://openalex.org/W3093695087","https://openalex.org/W3103720336","https://openalex.org/W3107101066","https://openalex.org/W3120509284","https://openalex.org/W3131101114","https://openalex.org/W3169236312","https://openalex.org/W3181000914"],"related_works":["https://openalex.org/W2023578311","https://openalex.org/W1833397253","https://openalex.org/W4214841405","https://openalex.org/W1800639126","https://openalex.org/W2347703430","https://openalex.org/W2370273288","https://openalex.org/W3148227991","https://openalex.org/W2368441895","https://openalex.org/W3001521712","https://openalex.org/W4312895206"],"abstract_inverted_index":{"Predicting":[0],"the":[1,6,13,38,71,90,94,121],"number":[2],"of":[3,61,123],"infections":[4],"in":[5,15,20],"anti-epidemic":[7,17],"process":[8],"is":[9,81,117],"extremely":[10],"beneficial":[11],"to":[12,37,88,119,125],"government":[14],"developing":[16],"strategies,":[18],"especially":[19],"fine-grained":[21,53],"geographic":[22,40,63,84,131],"units.":[23],"Previous":[24],"works":[25],"focus":[26],"on":[27,129,138,152],"low":[28],"spatial":[29,113,134],"resolution":[30],"prediction,":[31,111],"e.g.,":[32],"county-level,":[33],"and":[34,92,133],"preprocess":[35],"data":[36,60,74,144],"same":[39],"level,":[41],"which":[42,80],"loses":[43],"some":[44],"useful":[45],"information.":[46],"In":[47],"this":[48],"paper,":[49],"we":[50],"propose":[51],"a":[52,82,112],"population":[54,72],"mobility":[55,73],"data-based":[56],"model":[57,147],"(FGC-COVID)":[58],"utilizing":[59],"two":[62],"levels":[64],"for":[65,110],"community-level":[66,153],"COVID-19":[67,143,154],"prediction.":[68,155],"We":[69],"use":[70],"between":[75,96],"Census":[76],"Block":[77],"Groups":[78],"(CBGs),":[79],"finer-grained":[83,106],"level":[85,127],"than":[86],"community,":[87],"build":[89],"graph":[91,99],"capture":[93],"dependencies":[95],"CBGs":[97,124],"using":[98],"neural":[100],"networks":[101],"(GNNs).":[102],"To":[103],"mine":[104],"as":[105,108],"patterns":[107],"possible":[109],"weighted":[114],"aggregation":[115],"module":[116],"introduced":[118],"aggregate":[120],"embeddings":[122],"community":[126],"based":[128],"their":[130],"affiliation":[132],"autocorrelation.":[135],"Extensive":[136],"experiments":[137],"300":[139],"days":[140],"LA":[141],"city":[142],"indicate":[145],"our":[146],"outperforms":[148],"existing":[149],"forecasting":[150],"models":[151]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
