Journal
EPIDEMICS
Volume 4, Issue 1, Pages 9-21Publisher
ELSEVIER
DOI: 10.1016/j.epidem.2011.11.001
Keywords
SEIR model; Epidemic modelling; Markov Chain Monte Carlo methods; Bayesian inference; Reporting process
Categories
Funding
- Sanofi Pasteur MSD
- EU
- UK Medical Research Council
- Research Councils UK
- MRC [G0600719] Funding Source: UKRI
- Medical Research Council [G0600719B, G0600719] Funding Source: researchfish
- National Institute for Health Research [NF-SI-0508-10252] Funding Source: researchfish
Ask authors/readers for more resources
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. (C) 2011 Elsevier B. V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available