Literature DB >> 20546633

Model predictions and evaluation of possible control strategies for the 2009 A/H1N1v influenza pandemic in Italy.

M Ajelli1, S Merler, A Pugliese, C Rizzo.   

Abstract

We describe the real-time modelling analysis conducted in Italy during the early phases of the 2009 A/H1N1v influenza pandemic in order to estimate the impact of the pandemic and of the related mitigation measures implemented. Results are presented along with a comparison with epidemiological surveillance data which subsequently became available. Simulated epidemics were fitted to the estimated number of influenza-like syndromes collected within the Italian sentinel surveillance systems and showed good agreement with the timing of the observed epidemic. On the basis of the model predictions, we estimated the underreporting factor of the influenza surveillance system to be in the range 3·3-3·7 depending on the scenario considered. Model prediction suggested that the epidemic would peak in early November. These predictions have proved to be a valuable support for public health policy-makers in planning interventions for mitigating the spread of the pandemic.

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Year:  2010        PMID: 20546633     DOI: 10.1017/S0950268810001317

Source DB:  PubMed          Journal:  Epidemiol Infect        ISSN: 0950-2688            Impact factor:   2.451


  26 in total

1.  Connecting within and between-hosts dynamics in the influenza infection-staged epidemiological models with behavior change.

Authors:  Kasia A Pawelek; Cristian Salmeron; Sara Del Valle
Journal:  J Coupled Syst Multiscale Dyn       Date:  2015-09

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

Authors:  Ilaria Dorigatti; Simon Cauchemez; Andrea Pugliese; Neil Morris Ferguson
Journal:  Epidemics       Date:  2011-11-28       Impact factor: 4.396

3.  Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm.

Authors:  Michele Tizzoni; Paolo Bajardi; Chiara Poletto; José J Ramasco; Duygu Balcan; Bruno Gonçalves; Nicola Perra; Vittoria Colizza; Alessandro Vespignani
Journal:  BMC Med       Date:  2012-12-13       Impact factor: 8.775

4.  Inferring the structure of social contacts from demographic data in the analysis of infectious diseases spread.

Authors:  Laura Fumanelli; Marco Ajelli; Piero Manfredi; Alessandro Vespignani; Stefano Merler
Journal:  PLoS Comput Biol       Date:  2012-09-13       Impact factor: 4.475

5.  The effect of risk perception on the 2009 H1N1 pandemic influenza dynamics.

Authors:  Piero Poletti; Marco Ajelli; Stefano Merler
Journal:  PLoS One       Date:  2011-02-07       Impact factor: 3.240

6.  Comparing large-scale computational approaches to epidemic modeling: agent-based versus structured metapopulation models.

Authors:  Marco Ajelli; Bruno Gonçalves; Duygu Balcan; Vittoria Colizza; Hao Hu; José J Ramasco; Stefano Merler; Alessandro Vespignani
Journal:  BMC Infect Dis       Date:  2010-06-29       Impact factor: 3.090

7.  Determinants of the spatiotemporal dynamics of the 2009 H1N1 pandemic in Europe: implications for real-time modelling.

Authors:  Stefano Merler; Marco Ajelli; Andrea Pugliese; Neil M Ferguson
Journal:  PLoS Comput Biol       Date:  2011-09-29       Impact factor: 4.475

8.  Variability in transmissibility of the 2009 H1N1 pandemic in Canadian communities.

Authors:  Luiz C Mostaço-Guidolin; Amy Greer; Beate Sander; Jianhong Wu; Seyed M Moghadas
Journal:  BMC Res Notes       Date:  2011-12-13

9.  Modeling for Estimating Influenza Patients from ILI Surveillance Data in Korea.

Authors:  Joo-Sun Lee; Sun-Hee Park; Jin-Woong Moon; Jacob Lee; Yong Gyu Park; Yong Kyun Roh
Journal:  Osong Public Health Res Perspect       Date:  2011-08-04

10.  Transmission potential and design of adequate control measures for Marburg hemorrhagic fever.

Authors:  Marco Ajelli; Stefano Merler
Journal:  PLoS One       Date:  2012-12-10       Impact factor: 3.240

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