tetano
Editor, Senior Moderator
Epidemics. 2012 Mar;4(1):9-21. Epub 2011 Nov 28.
A new approach to characterising infectious disease transmission dynamics from sentinel surveillance: Application to the Italian 2009-2010 A/H1N1 influenza pandemic.
Dorigatti I, Cauchemez S, Pugliese A, Ferguson NM.
Source
MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, Faculty of Medicine, Norfolk Place, London W2 1PG, UK; Department of Mathematics, University of Trento, via Sommarive 14, 38123 Povo (Trento), Italy.
Abstract
Syndromic and virological data are routinely collected by many countries and are often the only information available in real time. The analysis of surveillance data poses many statistical challenges that have not yet been addressed. For instance, the fraction of cases that seek healthcare and are thus detected is often unknown. Here, we propose a general statistical framework that explicitly takes into account the way the surveillance data are generated. Our approach couples a deterministic mathematical model with a statistical description of the reporting process and is applied to surveillance data collected in Italy during the 2009-2010 A/H1N1 influenza pandemic. We estimate that the reproduction number R was initially into the range 1.2-1.4 and that case detection in children was significantly higher than in adults. According to the best fit models, we estimate that school-age children experienced the highest infection rate overall. In terms of both estimated peak-incidence and overall attack rate, according to the Susceptibility and Immunity models the 5-14 years age-class was about 5 times more infected than the 65+ years old age-group and about twice more than the 15-64 years age-class. The multiplying factors are doubled using the Baseline model. Overall, the estimated attack rate was about 16% according to the Baseline model and 30% according to the Susceptibility and Immunity models.
Copyright ? 2011 Elsevier B.V. All rights reserved.
PMID:
22325010
[PubMed - in process]
http://www.ncbi.nlm.nih.gov/pubmed/22325010
A new approach to characterising infectious disease transmission dynamics from sentinel surveillance: Application to the Italian 2009-2010 A/H1N1 influenza pandemic.
Dorigatti I, Cauchemez S, Pugliese A, Ferguson NM.
Source
MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, Faculty of Medicine, Norfolk Place, London W2 1PG, UK; Department of Mathematics, University of Trento, via Sommarive 14, 38123 Povo (Trento), Italy.
Abstract
Syndromic and virological data are routinely collected by many countries and are often the only information available in real time. The analysis of surveillance data poses many statistical challenges that have not yet been addressed. For instance, the fraction of cases that seek healthcare and are thus detected is often unknown. Here, we propose a general statistical framework that explicitly takes into account the way the surveillance data are generated. Our approach couples a deterministic mathematical model with a statistical description of the reporting process and is applied to surveillance data collected in Italy during the 2009-2010 A/H1N1 influenza pandemic. We estimate that the reproduction number R was initially into the range 1.2-1.4 and that case detection in children was significantly higher than in adults. According to the best fit models, we estimate that school-age children experienced the highest infection rate overall. In terms of both estimated peak-incidence and overall attack rate, according to the Susceptibility and Immunity models the 5-14 years age-class was about 5 times more infected than the 65+ years old age-group and about twice more than the 15-64 years age-class. The multiplying factors are doubled using the Baseline model. Overall, the estimated attack rate was about 16% according to the Baseline model and 30% according to the Susceptibility and Immunity models.
Copyright ? 2011 Elsevier B.V. All rights reserved.
PMID:
22325010
[PubMed - in process]
http://www.ncbi.nlm.nih.gov/pubmed/22325010