ArticlePloS one2013
Pandemic influenza A/H1N1pdm in Italy: age, risk and population susceptibility.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 21 citations in OpenAlex.
- A systematic comparison of methodologies for the estimation of the serial interval.Infectious Disease Modelling · 2026Article
- Implications for distancing measures on in-person school and work attendance from Italian post-pandemic social contact data.Communications medicine · 2026Article
- Review
- The New Quadrivalent Adjuvanted Influenza Vaccine for the Italian Elderly: A Health Technology Assessment.International journal of environmental research and public health · 2022Article
- Characterizing the transmission patterns of seasonal influenza in Italy: lessons from the last decade.BMC public health · 2022Article
- [Health Technology Assessment (HTA) of the introduction of influenza vaccination for Italian children with Fluenz TetraJournal of preventive medicine and hygiene · 2021Article
- Inferring high-resolution human mixing patterns for disease modeling.Nature communications · 2021Article
- Reactive school closure weakens the network of social interactions and reduces the spread of influenza.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Measurability of the epidemic reproduction number in data-driven contact networks.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- The Silver Lining in Gain-of-Function Experiments with Pathogens of Pandemic Potential.Methods in molecular biology (Clifton, N.J.) · 2018Review
- Detecting a Surprisingly Low Transmission Distance in the Early Phase of the 2009 Influenza Pandemic.Scientific reports · 2017Article
- School closure policies at municipality level for mitigating influenza spread: a model-based evaluation.BMC infectious diseases · 2016Article
- Estimating transmission probability in schools for the 2009 H1N1 influenza pandemic in Italy.Theoretical biology & medical modelling · 2016Article
- Multiple Lattice Model for Influenza Spreading.PloS one · 2015Article
- The role of different social contexts in shaping influenza transmission during the 2009 pandemic.Scientific reports · 2014Article
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA common pattern emerging from several studies evaluating the impact of the 2009 A/H1N1 pandemic influenza (A/H1N1pdm) conducted in countries worldwide is the low attack rate observed in elderly compared to that observed in children and young adults. The biological or social mechanisms responsible for the observed age-specific risk of infection are still to be deeply investigated.
methodsThe level of immunity against the A/H1N1pdm in pre and post pandemic sera was determined using left over sera taken for diagnostic purposes or routine ascertainment obtained from clinical laboratories. The antibody titres were measured by the haemagglutination inhibition (HI) assay. To investigate whether certain age groups had higher risk of infection the presence of protective antibody (≥1∶40), was calculated using exact binomial 95% CI on both pre- and post- pandemic serological data in the age groups considered. To estimate age-specific susceptibility to infection we used an age-structured SEIR model.
resultsBy comparing pre- and post-pandemic serological data in Italy we found age- specific attack rates similar to those observed in other countries. Cumulative attack rate at the end of the first A/H1N1pdm season in Italy was estimated to be 16.3% (95% CI 9.4%-23.1%). Modeling results allow ruling out the hypothesis that only age-specific characteristics of the contact network and levels of pre-pandemic immunity are responsible for the observed age-specific risk of infection. This means that age-specific susceptibility to infection, suspected to play an important role in the pandemic, was not only determined by pre-pandemic levels of H1N1pdm antibody measured by HI.
conclusionsOur results claim for new studies to better identify the biological mechanisms, which might have determined the observed pattern of susceptibility with age. Moreover, our results highlight the need to obtain early estimates of differential susceptibility with age in any future pandemics to obtain more reliable real time estimates of critical epidemiological parameters.
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