Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Bayesian Nowcasting during the STEC O104:H4 Outbreak in Germany, 2011
Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen. Robert Koch Institute, Germany.ORCID-id: 0000-0002-0423-6702
2014 (engelsk)Inngår i: Biometrics, ISSN 0006-341X, E-ISSN 1541-0420, Vol. 70, nr 4, s. 993-1002Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

A Bayesian approach to the prediction of occurred-but-not-yet-reported events is developed for application in real-time public health surveillance. The motivation was the prediction of the daily number of hospitalizations for the hemolytic-uremic syndrome during the large May-July 2011 outbreak of Shiga toxin-producing Escherichia coli (STEC) O104:H4 in Germany. Our novel Bayesian approach addresses the count data nature of the problem using negative binomial sampling and shows that right-truncation of the reporting delay distribution under an assumption of time-homogeneity can be handled in a conjugate prior-posterior framework using the generalized Dirichlet distribution. Since, in retrospect, the true number of hospitalizations is available, proper scoring rules for count data are used to evaluate and compare the predictive quality of the procedures during the outbreak. The results show that it is important to take the count nature of the time series into account and that changes in the delay distribution occurred due to intervention measures. As a consequence, we extend the Bayesian analysis to a hierarchical model, which combines a discrete time survival regression model for the delay distribution with a penalized spline for the dynamics of the epidemic curve. Altogether, we conclude that in emerging and time-critical outbreaks, nowcasting approaches are a valuable tool to gain information about current trends.

sted, utgiver, år, opplag, sider
2014. Vol. 70, nr 4, s. 993-1002
Emneord [en]
Infectious disease epidemiology, Real-time surveillance, Reporting delay, Truncation
HSV kategori
Identifikatorer
URN: urn:nbn:se:su:diva-113230DOI: 10.1111/biom.12194ISI: 000346827500023OAI: oai:DiVA.org:su-113230DiVA, id: diva2:791356
Merknad

AuthorCount:2;

Tilgjengelig fra: 2015-02-27 Laget: 2015-01-26 Sist oppdatert: 2019-12-04bibliografisk kontrollert

Open Access i DiVA

fulltext(348 kB)120 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 348 kBChecksum SHA-512
d5d5c2ce262e0082b2bb955a38850d976bd69999df6b92fb5a7633fd0b3c63112f81345e7bdd81d3c890f2eaa71c41ab85933bcf00142aef2c310d227b43a5ab
Type fulltextMimetype application/pdf

Andre lenker

Forlagets fulltekst

Søk i DiVA

Av forfatter/redaktør
Höhle, Michael
Av organisasjonen
I samme tidsskrift
Biometrics

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 120 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 53 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf