{"id":"https://openalex.org/W4290927928","doi":"https://doi.org/10.1145/3534678.3539195","title":"Precise Mobility Intervention for Epidemic Control Using Unobservable Information via Deep Reinforcement Learning","display_name":"Precise Mobility Intervention for Epidemic Control Using Unobservable Information via Deep Reinforcement Learning","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4290927928","doi":"https://doi.org/10.1145/3534678.3539195"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539195","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3534678.3539195","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1145/3534678.3539195","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101472243","display_name":"Tao Feng","orcid":"https://orcid.org/0000-0002-7341-0225"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Feng","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040747504","display_name":"Xia Tong","orcid":"https://orcid.org/0000-0002-6994-6318"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Tong Xia","raw_affiliation_strings":["University of Cambridge Cambridge, Beijing, England UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Cambridge Cambridge, Beijing, England UK","institution_ids":["https://openalex.org/I241749"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103169022","display_name":"Xiaochen Fan","orcid":"https://orcid.org/0000-0001-8945-3046"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaochen Fan","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034129532","display_name":"Huandong Wang","orcid":"https://orcid.org/0000-0002-6382-0861"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huandong Wang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020483229","display_name":"Zefang Zong","orcid":"https://orcid.org/0000-0003-3236-6640"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zefang Zong","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100355277","display_name":"Yong Li","orcid":"https://orcid.org/0000-0001-5617-1659"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Li","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2882","last_page":"2892"},"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/T10168","display_name":"COVID-19 and Mental Health","score":0.9807000160217285,"subfield":{"id":"https://openalex.org/subfields/3203","display_name":"Clinical Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11368","display_name":"Long-Term Effects of COVID-19","score":0.9746999740600586,"subfield":{"id":"https://openalex.org/subfields/2728","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/unobservable","display_name":"Unobservable","score":0.9572389125823975},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7847734689712524},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.601099967956543},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4815704822540283},{"id":"https://openalex.org/keywords/intervention","display_name":"Intervention (counseling)","score":0.45995983481407166},{"id":"https://openalex.org/keywords/asymptomatic","display_name":"Asymptomatic","score":0.4505595862865448},{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.4107278883457184},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.39719220995903015},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.1727140247821808},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.145408034324646},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.10730218887329102},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.08130457997322083}],"concepts":[{"id":"https://openalex.org/C2780695315","wikidata":"https://www.wikidata.org/wiki/Q3799040","display_name":"Unobservable","level":2,"score":0.9572389125823975},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7847734689712524},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.601099967956543},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4815704822540283},{"id":"https://openalex.org/C2780665704","wikidata":"https://www.wikidata.org/wiki/Q959298","display_name":"Intervention (counseling)","level":2,"score":0.45995983481407166},{"id":"https://openalex.org/C2777910003","wikidata":"https://www.wikidata.org/wiki/Q1707292","display_name":"Asymptomatic","level":2,"score":0.4505595862865448},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.4107278883457184},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.39719220995903015},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.1727140247821808},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.145408034324646},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.10730218887329102},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.08130457997322083},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3534678.3539195","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3534678.3539195","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3534678.3539195","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3534678.3539195","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.8600000143051147,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1662842982","https://openalex.org/W2181144303","https://openalex.org/W3009876049","https://openalex.org/W3011892127","https://openalex.org/W3035804808","https://openalex.org/W3040264860","https://openalex.org/W3080501557","https://openalex.org/W3098435366","https://openalex.org/W3113541327","https://openalex.org/W4205338728"],"related_works":["https://openalex.org/W2614563012","https://openalex.org/W4293337373","https://openalex.org/W1968533609","https://openalex.org/W2486803649","https://openalex.org/W1984270607","https://openalex.org/W2151842462","https://openalex.org/W2349333549","https://openalex.org/W2116445905","https://openalex.org/W3140685204","https://openalex.org/W2765138172"],"abstract_inverted_index":{"To":[0,142],"control":[1,135,253],"the":[2,22,34,59,66,77,83,89,144,169,179,189,195,198,204,209,214,241,254,257,261],"outbreak":[3],"of":[4,16,36,85,91,107,156,181,191,201,216,256],"COVID-19,":[5,108],"efficient":[6],"individual":[7,183,193],"mobility":[8,35,120,138,268],"intervention":[9],"for":[10,43,72,80,118,133],"EPidemic":[11],"Control":[12],"(EPC)":[13],"strategies":[14],"are":[15,48,93],"great":[17],"importance,":[18],"which":[19,235],"cut":[20],"off":[21],"contact":[23],"among":[24],"people":[25],"at":[26],"epidemic":[27,217],"risks":[28],"and":[29,75,103,163,188,273],"reduce":[30],"infections":[31,64,98],"by":[32,220],"intervening":[33],"individuals.":[37],"Reinforcement":[38,227],"Learning":[39,131,228],"(RL)":[40],"is":[41,110,230],"powerful":[42],"decision":[44],"making,":[45],"however,":[46],"there":[47],"two":[49],"major":[50],"challenges":[51],"in":[52,65,95],"developing":[53],"an":[54,115,150,182,185],"RL-based":[55],"EPC":[56,92],"strategy:":[57],"(1)":[58],"unobservable":[60,100,170,221],"information":[61,151],"about":[62],"asymptomatic":[63,101,186,222,274],"incubation":[67],"period":[68],"makes":[69],"it":[70,109],"difficult":[71],"RL's":[73],"decision-making,":[74],"(2)":[76],"delayed":[78,242],"rewards":[79],"RL":[81,86,116],"causes":[82],"deficiency":[84],"learning.":[87],"Since":[88],"results":[90,247],"reflected":[94],"both":[96,271],"daily":[97],"(including":[99],"infections)":[102],"long-term":[104],"cumulative":[105],"cases":[106],"quite":[111],"daunting":[112],"to":[113,167,212,232,239],"design":[114],"model":[117,213],"precise":[119],"intervention.":[121],"In":[122],"this":[123,192],"paper,":[124],"we":[125],"propose":[126],"a":[127,157,164,225],"Variational":[128,205],"hiErarcHICal":[129],"reinforcement":[130],"method":[132],"Epidemic":[134],"via":[136],"individual-level":[137],"intervention,":[139],"namely":[140],"Vehicle.":[141],"tackle":[143],"above":[145],"challenges,":[146],"Vehicle":[147,250,259],"first":[148],"exploits":[149],"rebuilding":[152],"module":[153],"that":[154,249],"consists":[155],"contact-risk":[158,173],"bipartite":[159,174],"graph":[160,175],"neural":[161,176],"network":[162,177],"variational":[165],"LSTM":[166,206],"restore":[168],"information.":[171],"The":[172],"estimates":[178],"possibility":[180],"being":[184],"infection":[187],"risk":[190],"spreading":[194,218],"epidemic,":[196],"as":[197],"current":[199],"state":[200,210],"RL.":[202],"Then,":[203],"further":[207],"encodes":[208],"sequence":[211],"latency":[215],"caused":[219],"infections.":[223,275],"Finally,":[224],"Hierarchical":[226],"framework":[229],"employed":[231],"train":[233],"Vehicle,":[234],"contains":[236],"dual-level":[237],"agents":[238],"solve":[240],"reward":[243],"problem.":[244],"Extensive":[245],"experimental":[246],"demonstrate":[248],"can":[251],"effectively":[252],"spread":[255],"epidemic.":[258],"outperforms":[260],"state-of-the-art":[262],"baseline":[263],"methods":[264],"with":[265],"remarkably":[266],"high-precision":[267],"interventions":[269],"on":[270],"symptomatic":[272]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}