A new approach to characterising infectious disease transmission dynamics from sentinel surveillance: application to the Italian 2009-2010 A/H1N1 influenza pandemic.

TitleA new approach to characterising infectious disease transmission dynamics from sentinel surveillance: application to the Italian 2009-2010 A/H1N1 influenza pandemic.
Publication TypeJournal Article
Year of Publication2012
AuthorsDorigatti I, Cauchemez S, Pugliese A, Ferguson NMorris
JournalEpidemics
Volume4
Issue1
Pagination9-21
Date Published2012 Mar
ISSN1878-0067
KeywordsAdolescent, Child, Child, Preschool, Disease Susceptibility, Female, Humans, Influenza A Virus, H1N1 Subtype, Influenza, Human, Italy, Male, Models, Statistical, Pandemics, Sentinel Surveillance
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.

DOI10.1016/j.epidem.2011.11.001
Alternate JournalEpidemics
PubMed ID22325010
PubMed Central IDPMC4088935
Grant ListU54 GM088491 / GM / NIGMS NIH HHS / United States
/ / Medical Research Council / United Kingdom
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