(Press-News.org) A new model of influenza transmission, published in BioMed Central's open access journal BMC Medicine, using more detailed information about patterns and severity of infection than previous models, finds that cases and transmission rates of H1N1 during the 2009-2010 flu pandemic have been underestimated. This model can provide a more robust and accurate real-time estimate of infection during a pandemic, which will help health services prepare and respond to future outbreaks.
During an epidemic one of the most important pieces of information health services need in order to respond efficiently is how 'transmissible' a disease is. In other words how many people are going to be infected and how easily the infection spreads. Although people exposed to flu can experience a range of symptoms from no infection through to serious illness, most models of disease transmission simplify this to infectious or not. Such a simplification makes the data easier to handle but also potentially disguises important aspects of how an epidemic develops.
A new model, developed by a team led by Dr Thomas House from the University of Warwick, includes within household transmission, as well as size of household, disease severity, and other key factors during the first seven weeks of the 2009 H1N1 epidemic in Birmingham. This information was collected by the BADGER flu clinic and Health Protection Agency centred on laboratory confirmed cases and their household contacts.
By combining transmission possibilities from people with a positive test for flu, people tested for flu and people who had flu symptoms but were not tested, this model gave a much more accurate picture of how the pandemic progressed.
Dr House explained, "By using stratified data we are able to estimate within house infection rates directly. We found that infection rates were higher than previously thought from models relying solely on laboratory confirmed cases and that a large number of people who were likely to have been real cases even if they did not have a positive swab (for example if they had recovered before the swab was taken). We also found that transmission probabilities between two people decrease with increasing household size."
This model will be able to provide real-time information about how an epidemic or pandemic is evolving. It also shows how excluding people without confirmed diagnosis can skew the results and make outbreak seem much less serious than it really is, leaving health services ill-prepared.
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Notes
1. Estimation of outbreak severity and transmissibility: Influenza A(H1N1)pdm09 in households
Thomas House, Nadia Inglis, Joshua V Ross, Fay Wilson, Shakeel Suleman, Obaghe Edeghere, Gillian Smith, Babatunde Olowokure and Matt J Keeling
BMC Medicine (in press)
Please name the journal in any story you write. If you are writing for the web, please link to the article. All articles are available free of charge, according to BioMed Central's open access policy.
Article citation and URL available on request on the day of publication.
2. BMC Medicine is the flagship medical journal of the BMC series, publishing original research, commentaries and reviews that are either of significant interest to all areas of medicine and clinical practice, or provide key translational or clinical advances in a specific field.
3. BioMed Central is an STM (Science, Technology and Medicine) publisher which has pioneered the open access publishing model. All peer-reviewed research articles published by BioMed Central are made immediately and freely accessible online, and are licensed to allow redistribution and reuse. BioMed Central is part of Springer Science+Business Media, a leading global publisher in the STM sector. @BioMedCentral
Learning from past flu epidemics to model outbreaks as they happen
2012-10-09
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