In today’s Academic Minute, Dr. Thomas House of the University at Warwick reveals how mathematical models are increasing our understanding of how epidemics move through a population.
Thomas House is a Career Acceleration Fellow in the Mathematics Institute at the University of Warwick. His research interests include epidemiology, network theory, numerical probability, and public health. He is also involved in the development of open-source software for epidemic modeling.
Dr. Thomas House – Mathematics and Mapping Epidemics
One of the biggest problems when faced with an outbreak of an infectious disease is knowing how bad the consequences of epidemic are going to be. At first, we see the number of known infectious cases growing -But: eventually, enough people have experienced infection that what is known as 'herd immunity' becomes important and the number of cases declines. When we know that some cases are not detected, this makes it particularly difficult to predict the overall severity of the outbreak.
In a city of a million people, if ten thousand have reported illness to their doctor, does that mean that only 1% have experienced infection, or could it be that 10% have experienced infection and nine in ten cases are not ill enough to report anything? The people who report infection are the tip of the iceberg, and we need ways to see under the water.
One way to do this is to perform lab tests on a representative sample of the population, which was done during the swine 'flu pandemic, showing that a very large proportion of cases were missed. This approach obviously involves a certain amount of cost. We realised that the way in which infectious diseases spread, together with data that are routinely collected from households early in any pandemic, could be used to obtain information on cases missed without extra cost and as a complement to lab testing.
If one person brings an infection into a household, there is a good chance that they will be the only case; but if they infect one other household member then further secondary infections are likely. This leads to a distinctive pattern of disease in households, one which allowed us to estimate how many cases were missed - in our case, this turned out to be over half. In this way, mathematics acted like a microscope, letting us exploit the patterns of transmission the swine flu virus left during its journey through the population.