Understanding herd immunity for influenza using archived sera from the UK and mathematical models

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The 2009 pandemic highlighted some real problems with how scientists and governments monitor influenza. Up-to-now, they have counted numbers of people who go to their doctor or the hospital with flu-like symptoms. But this doesn't tell us about the mild cases.Mild cases are important because of something called 'herd immunity'. During any epidemic, the number of new cases starts to go down when there are not enough susceptible people to keep 'the fire' of the epidemic going. People with mild or no symptoms are just as good at stopping the epidemic as people with symptoms. So, to be able to understand how big an influenza epidemic might be, we need to measure mild cases as well.We can do this if we take blood and test for antibodies. In this project, we want to test anonymous blood samples for influenza that were discarded in the late 1990s and early 2000s. By doing 10s of 1000s of tests on these old samples, we can see exactly how useful the data on mild cases might be for future epidemics and we can answer interesting questions about different influenza strains. There were three different types of influenza circulating in the late 1990s and early 2000s. Two types of influenza A and one was influenza B. Two of those three are still circulating now (A/H3N2 and B). One of them was replaced by the pandemic strain (A/H1N1). We want to compare and contract these three types.We will use mathematical models to analyze the data from the blood tests. Models help us to figure out why an epidemic occurred or was as big as it was, rather than just describing it. We can look at important basic questions like: is flu B less serious infection that influenza A; and if you get a mild infection of flu you can get infected again more quickly than if you get a serious infection?