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Article: Estimating incidence curves of several infections using symptom surveillance data

TitleEstimating incidence curves of several infections using symptom surveillance data
Authors
Goldstein, ECowling, BJAiello, AETakahashi, SKing, GLu, YLipsitch, M
Issue Date2011
PublisherPublic Library of Science. The Journal's web site is located at http://www.plosone.org/home.action
Citation
Plos One, 2011, v. 6 n. 8 How to Cite?
DOI: http://dx.doi.org/10.1371/journal.pone.0023380
AbstractWe introduce a method for estimating incidence curves of several co-circulating infectious pathogens, where each infection has its own probabilities of particular symptom profiles. Our deconvolution method utilizes weekly surveillance data on symptoms from a defined population as well as additional data on symptoms from a sample of virologically confirmed infectious episodes. We illustrate this method by numerical simulations and by using data from a survey conducted on the University of Michigan campus. Last, we describe the data needs to make such estimates accurate. © 2011 Goldstein et al.
Persistent Identifierhttp://hdl.handle.net/10722/143807
ISSN
1932-6203
2023 Impact Factor: 2.9
2023 SCImago Journal Rankings: 0.839
PubMed Central ID
PMC3160845
ISI Accession Number ID
WOS:000295832000016
Funding AgencyGrant Number
United States National Institutes of Health Models of Infectious Disease1 U54 GM088558
Centers for Disease Control and PreventionU01C1000441
Hong Kong University Grants CommitteeAoE/M-12/06
Harvard College
RocheRpar
Pfizer/Wyeth
AIR Worldwide
Novartis Vaccines and Diagnostics
MedImmune Inc.
Avian/Pandemic Flu Registry
Funding Information:

This work was supported in part by the United States National Institutes of Health Models of Infectious Disease Agent Study program through cooperative agreement 1 U54 GM088558 (ML, EG, BC); by the Centers for Disease Control and Prevention grant U01C1000441 (AA); by the Area of Excellence Scheme of the Hong Kong University Grants Committee, grant no. AoE/M-12/06 (BC); and by the Harvard College Research Program (ST). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References
References in Scopus
Grants
Control of Pandemic and Inter-pandemic Influenza

 

DC FieldValueLanguage
dc.contributor.authorGoldstein, Een_HK
dc.contributor.authorCowling, BJen_HK
dc.contributor.authorAiello, AEen_HK
dc.contributor.authorTakahashi, Sen_HK
dc.contributor.authorKing, Gen_HK
dc.contributor.authorLu, Yen_HK
dc.contributor.authorLipsitch, Men_HK
dc.date.accessioned2011-12-21T08:56:34Z-
dc.date.available2011-12-21T08:56:34Z-
dc.date.issued2011en_HK
dc.identifier.citationPlos One, 2011, v. 6 n. 8en_HK
dc.identifier.issn1932-6203en_HK
dc.identifier.urihttp://hdl.handle.net/10722/143807-
dc.description.abstractWe introduce a method for estimating incidence curves of several co-circulating infectious pathogens, where each infection has its own probabilities of particular symptom profiles. Our deconvolution method utilizes weekly surveillance data on symptoms from a defined population as well as additional data on symptoms from a sample of virologically confirmed infectious episodes. We illustrate this method by numerical simulations and by using data from a survey conducted on the University of Michigan campus. Last, we describe the data needs to make such estimates accurate. © 2011 Goldstein et al.en_HK
dc.languageengen_US
dc.publisherPublic Library of Science. The Journal's web site is located at http://www.plosone.org/home.actionen_HK
dc.relation.ispartofPLoS ONEen_HK
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subject.meshCommunicable Diseases - complications - epidemiology-
dc.subject.meshDisease Outbreaks - statistics and numerical data-
dc.subject.meshHealth Surveys-
dc.subject.meshInfluenza, Human - complications - epidemiology-
dc.subject.meshPopulation Surveillance-
dc.titleEstimating incidence curves of several infections using symptom surveillance dataen_HK
dc.typeArticleen_HK
dc.identifier.emailCowling, BJ:bcowling@hku.hken_HK
dc.identifier.authorityCowling, BJ=rp01326en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1371/journal.pone.0023380en_HK
dc.identifier.pmid21887246-
dc.identifier.pmcidPMC3160845-
dc.identifier.scopuseid_2-s2.0-80051962615en_HK
dc.identifier.hkuros198003en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-80051962615&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume6en_HK
dc.identifier.issue8en_HK
dc.identifier.spagee23380en_US
dc.identifier.epagee23380en_US
dc.identifier.isiWOS:000295832000016-
dc.publisher.placeUnited Statesen_HK
dc.relation.projectControl of Pandemic and Inter-pandemic Influenza-
dc.identifier.scopusauthoridGoldstein, E=35344973300en_HK
dc.identifier.scopusauthoridCowling, BJ=8644765500en_HK
dc.identifier.scopusauthoridAiello, AE=7004914475en_HK
dc.identifier.scopusauthoridTakahashi, S=48261394600en_HK
dc.identifier.scopusauthoridKing, G=35515028500en_HK
dc.identifier.scopusauthoridLu, Y=27168409800en_HK
dc.identifier.scopusauthoridLipsitch, M=7006236353en_HK
dc.identifier.issnl1932-6203-

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