{"id":"https://openalex.org/W2101877806","doi":"https://doi.org/10.1145/2491288.2491294","title":"Detecting epidemics using highly noisy data","display_name":"Detecting epidemics using highly noisy data","publication_year":2013,"publication_date":"2013-07-29","ids":{"openalex":"https://openalex.org/W2101877806","doi":"https://doi.org/10.1145/2491288.2491294","mag":"2101877806"},"language":"en","primary_location":{"id":"doi:10.1145/2491288.2491294","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2491288.2491294","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the fourteenth ACM international symposium on Mobile ad hoc networking and computing","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/A5040832655","display_name":"Chris Milling","orcid":null},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chris Milling","raw_affiliation_strings":["UT Austin, Austin, TX, USA"],"affiliations":[{"raw_affiliation_string":"UT Austin, Austin, TX, USA","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053978837","display_name":"Constantine Caramanis","orcid":"https://orcid.org/0000-0001-9939-8378"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Constantine Caramanis","raw_affiliation_strings":["UT Austin, Austin, TX, USA"],"affiliations":[{"raw_affiliation_string":"UT Austin, Austin, TX, USA","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036260775","display_name":"Shie Mannor","orcid":"https://orcid.org/0000-0003-4439-7647"},"institutions":[{"id":"https://openalex.org/I174306211","display_name":"Technion \u2013 Israel Institute of Technology","ror":"https://ror.org/03qryx823","country_code":"IL","type":"education","lineage":["https://openalex.org/I174306211"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Shie Mannor","raw_affiliation_strings":["The Technion, Haifa, Israel"],"affiliations":[{"raw_affiliation_string":"The Technion, Haifa, Israel","institution_ids":["https://openalex.org/I174306211"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028903768","display_name":"Sanjay Shakkottai","orcid":"https://orcid.org/0000-0002-4325-9050"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sanjay Shakkottai","raw_affiliation_strings":["UT Austin, Austin, TX, USA"],"affiliations":[{"raw_affiliation_string":"UT Austin, Austin, TX, USA","institution_ids":["https://openalex.org/I86519309"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5040832655"],"corresponding_institution_ids":["https://openalex.org/I86519309"],"apc_list":null,"apc_paid":null,"fwci":2.4943,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.89786489,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"177","last_page":"186"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9925000071525574,"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9768999814987183,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.8130452632904053},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.655977189540863},{"id":"https://openalex.org/keywords/random-graph","display_name":"Random graph","score":0.4582509398460388},{"id":"https://openalex.org/keywords/epidemic-model","display_name":"Epidemic model","score":0.440395712852478},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4225095212459564},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3302941918373108},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3220244348049164},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.28063100576400757},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.11333814263343811}],"concepts":[{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.8130452632904053},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.655977189540863},{"id":"https://openalex.org/C47458327","wikidata":"https://www.wikidata.org/wiki/Q910404","display_name":"Random graph","level":3,"score":0.4582509398460388},{"id":"https://openalex.org/C1627819","wikidata":"https://www.wikidata.org/wiki/Q2572354","display_name":"Epidemic model","level":3,"score":0.440395712852478},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4225095212459564},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3302941918373108},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3220244348049164},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28063100576400757},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.11333814263343811},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"score":0.0},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2491288.2491294","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2491288.2491294","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the fourteenth ACM international symposium on Mobile ad hoc networking and computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.8799999952316284,"display_name":"Good health and well-being"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W160364443","https://openalex.org/W1526841711","https://openalex.org/W1544729927","https://openalex.org/W1677770578","https://openalex.org/W1914027636","https://openalex.org/W1984430808","https://openalex.org/W1989607057","https://openalex.org/W1994416662","https://openalex.org/W2036777551","https://openalex.org/W2038983716","https://openalex.org/W2053913534","https://openalex.org/W2079848860","https://openalex.org/W2092418988","https://openalex.org/W2093029384","https://openalex.org/W2109603058","https://openalex.org/W2111772797","https://openalex.org/W2115227517","https://openalex.org/W2150105124","https://openalex.org/W2483790945","https://openalex.org/W2555171551","https://openalex.org/W2569283211","https://openalex.org/W2996821993","https://openalex.org/W3005169540","https://openalex.org/W4247176319","https://openalex.org/W4285719527","https://openalex.org/W6612694313","https://openalex.org/W6631426646"],"related_works":["https://openalex.org/W1557094818","https://openalex.org/W2183246718","https://openalex.org/W2099261052","https://openalex.org/W3209204065","https://openalex.org/W2105707930","https://openalex.org/W1755711892","https://openalex.org/W2160907113","https://openalex.org/W2070813941","https://openalex.org/W3046510185","https://openalex.org/W2348583279"],"abstract_inverted_index":{"From":[0],"Cholera,":[1],"AIDS/HIV,":[2],"and":[3,7,39,71,133,166,185],"Malaria,":[4],"to":[5,37,74,90,130,148,158,188],"rumors":[6],"viral":[8],"video,":[9],"understanding":[10],"the":[11,24,30,44,76,140,152,175,192],"causative":[12,77],"network":[13,78],"behind":[14],"an":[15,80,85,116,135,167],"epidemic's":[16],"spread":[17,25],"has":[18],"repeatedly":[19],"proven":[20],"critical":[21],"for":[22,127],"managing":[23],"(controlling":[26],"or":[27],"encouraging,":[28],"as":[29],"case":[31],"may":[32],"be).":[33],"Our":[34],"current":[35],"approaches":[36],"understand":[38],"predict":[40],"epidemics":[41],"rely":[42],"on":[43],"scarce,":[45],"but":[46,61],"exact/reliable,":[47],"expert":[48],"diagnoses.":[49],"This":[50],"paper":[51],"proposes":[52],"a":[53,102,107,171,179],"different":[54],"way":[55],"forward:":[56],"use":[57],"more":[58,63],"readily":[59],"available":[60],"also":[62],"noisy":[64],"data":[65],"with":[66,115],"{\\em":[67],"many":[68],"false":[69,72,121],"negatives":[70],"positives},":[73],"determine":[75],"of":[79,92,111,120,181,183],"epidemic.":[81],"Specifically,":[82],"we":[83,100,155,177,186],"consider":[84],"epidemic":[86,168],"that":[87,138],"spreads":[88],"according":[89],"one":[91],"two":[93,149],"networks.":[94],"At":[95],"some":[96],"point":[97],"in":[98],"time":[99],"see":[101],"small":[103,109],"random":[104,161],"subsample":[105],"(perhaps":[106],"vanishingly":[108],"fraction)":[110],"those":[112],"infected,":[113],"along":[114,170],"order-wise":[117],"similar":[118],"number":[119],"positives.":[122],"We":[123,144],"derive":[124],"sufficient":[125],"conditions":[126],"this":[128,146],"problem":[129],"be":[131],"detectable,":[132],"provide":[134],"efficient":[136],"algorithm":[137],"solves":[139],"hypothesis":[141],"testing":[142],"problem.":[143],"apply":[145],"model":[147],"settings.":[150],"In":[151,174],"first":[153],"setting,":[154],"simply":[156],"want":[157],"distinguish":[159],"between":[160],"illness":[162],"(a":[163],"complete":[164],"graph)":[165],"(spread":[169],"structured":[172],"graph).":[173],"second,":[176],"have":[178],"superposition":[180],"both":[182],"these,":[184],"wish":[187],"detect":[189],"which":[190],"is":[191],"strongest":[193],"component.":[194]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":5},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":5},{"year":2015,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}