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Learning from past flu epidemics to model outbreaks as they happen

Date:
October 9, 2012
Source:
BioMed Central Limited
Summary:
A new model of influenza transmission, 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.

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.


Story Source:

The above story is based on materials provided by BioMed Central Limited. Note: Materials may be edited for content and length.


Journal Reference:

  1. Thomas House, Nadia Inglis, Joshua V Ross, Fay Wilson, Shakeel Suleman, Obaghe Edeghere, Gillian Smith, Babatunde Olowokure, Matt J Keeling. Estimation of outbreak severity and transmissibility: Influenza A(H1N1)pdm09 in households. BMC Medicine, 2012; 10 (1): 117 DOI: 10.1186/1741-7015-10-117

Cite This Page:

BioMed Central Limited. "Learning from past flu epidemics to model outbreaks as they happen." ScienceDaily. ScienceDaily, 9 October 2012. <www.sciencedaily.com/releases/2012/10/121009092816.htm>.
BioMed Central Limited. (2012, October 9). Learning from past flu epidemics to model outbreaks as they happen. ScienceDaily. Retrieved April 17, 2014 from www.sciencedaily.com/releases/2012/10/121009092816.htm
BioMed Central Limited. "Learning from past flu epidemics to model outbreaks as they happen." ScienceDaily. www.sciencedaily.com/releases/2012/10/121009092816.htm (accessed April 17, 2014).

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