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Article: A nonparametric estimation of the infection curve

TitleA nonparametric estimation of the infection curve
Authors
KeywordsBack-Projection
Epidemic
Infection Curve
Nonparametric Method
Issue Date2011
PublisherScience China Press, co-published with Springer. The Journal's web site is located at http://math.scichina.com/english/
Citation
Science China Mathematics, 2011, v. 54 n. 9, p. 1815-1828 How to Cite?
AbstractPredicting the future course of an epidemic depends on being able to estimate the current numbers of infected individuals. However, while back-projection techniques allow reliable estimation of the numbers of infected individuals in the more distant past, they are less reliable in the recent past. We propose two new nonparametric methods to estimate the unobserved numbers of infected individuals in the recent past in an epidemic. The proposed methods are noniterative, easily computed and asymptotically normal with simple variance formulas. Simulations show that the proposed methods are much more robust and accurate than the existing back projection method, especially for the recent past, which is our primary interest. We apply the proposed methods to the 2003 Severe Acute Respiratory Syndorme (SARS) epidemic in Hong Kong. © 2011 Science China Press and Springer-Verlag Berlin Heidelberg.
Persistent Identifierhttp://hdl.handle.net/10722/172263
ISSN
2015 Impact Factor: 0.761
2015 SCImago Journal Rankings: 0.894
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLin, HZen_US
dc.contributor.authorYip, PSFen_US
dc.contributor.authorHuggins, RMen_US
dc.date.accessioned2012-10-30T06:21:02Z-
dc.date.available2012-10-30T06:21:02Z-
dc.date.issued2011en_US
dc.identifier.citationScience China Mathematics, 2011, v. 54 n. 9, p. 1815-1828en_US
dc.identifier.issn1674-7283en_US
dc.identifier.urihttp://hdl.handle.net/10722/172263-
dc.description.abstractPredicting the future course of an epidemic depends on being able to estimate the current numbers of infected individuals. However, while back-projection techniques allow reliable estimation of the numbers of infected individuals in the more distant past, they are less reliable in the recent past. We propose two new nonparametric methods to estimate the unobserved numbers of infected individuals in the recent past in an epidemic. The proposed methods are noniterative, easily computed and asymptotically normal with simple variance formulas. Simulations show that the proposed methods are much more robust and accurate than the existing back projection method, especially for the recent past, which is our primary interest. We apply the proposed methods to the 2003 Severe Acute Respiratory Syndorme (SARS) epidemic in Hong Kong. © 2011 Science China Press and Springer-Verlag Berlin Heidelberg.en_US
dc.languageengen_US
dc.publisherScience China Press, co-published with Springer. The Journal's web site is located at http://math.scichina.com/english/en_US
dc.relation.ispartofScience China Mathematicsen_US
dc.subjectBack-Projectionen_US
dc.subjectEpidemicen_US
dc.subjectInfection Curveen_US
dc.subjectNonparametric Methoden_US
dc.titleA nonparametric estimation of the infection curveen_US
dc.typeArticleen_US
dc.identifier.emailYip, PSF: sfpyip@hku.hken_US
dc.identifier.authorityYip, PSF=rp00596en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1007/s11425-011-4224-7en_US
dc.identifier.scopuseid_2-s2.0-80052021898en_US
dc.identifier.hkuros221428-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-80052021898&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume54en_US
dc.identifier.issue9en_US
dc.identifier.spage1815en_US
dc.identifier.epage1828en_US
dc.identifier.isiWOS:000294223000001-
dc.publisher.placeChinaen_US
dc.identifier.scopusauthoridLin, HZ=13907979600en_US
dc.identifier.scopusauthoridYip, PSF=7102503720en_US
dc.identifier.scopusauthoridHuggins, RM=7102879186en_US

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