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Scientists say each person with swine flu will infect two others

Published on July 27, 2009 at 1:19 AM · No Comments

Scientists in New Zealand who have been examining the spread of the new H1N1 flu (swine flu), say the virus appears more infectious than previously thought and a person who becomes ill with the new strain will on average infect almost two others.

Their research which is the first estimate for New Zealand suggests that almost 80% of the population could catch the virus.

The researchers from the University of Otago in Wellington say that the transmission potential of a virus in a pandemic is typically summarised by the reproduction number which indicates the average number of secondary cases generated by a single primary case.

Associate Professor Michael Baker and Dr. Nick Wilson from the Department of Public Health at the University of Otago, Wellington worked in collaboration with Dr. Hiroshi Nishiura, a mathematical modeller based at the University of Utrecht in Holland and their results offer the first published estimate for the reproduction number of the pandemic in the southern hemisphere.

Associate Professor Baker says their best estimate of the reproduction number for the Influenza A virus in New Zealand is 1.96, which is higher than the number previously used in modelling estimates, where a lower estimate of 1.5 was used early in the pandemic based on data from Mexico.

The researchers say a reproduction number of 1.96 means that up to 79% of an affected population could ultimately catch H1N1 influenza during the epidemic, though only two-thirds of those infected may be expected to show symptoms - but this would require a high level of mixing between groups and they say effective public health interventions could lower that proportion.

Published estimates of the reproduction number in the current influenza pandemic have ranged from 1.4-1.6 in Mexico to 2.0-2.6 in Japan and it is thought that New Zealand has a higher reproduction number than Mexico, because of the winter season and large clusters of cases in certain settings.

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