Bradford Taylor, School of Biological Sciences

When a disease outbreak occurs, mathematical models are used to estimate the potential severity of the epidemic. The average number of secondary infections resulting from the initial infection or reproduction number, R_0, quantifies this severity. R_0 is estimated from the models by leveraging observed case data and understanding of disease epidemiology. However, the leveraged data is not perfect. How confident should we be about measurements of R_0 given noisy data? I begin my talk by introducing techniques used to model epidemics. I show how to adapt standard models to specific diseases by using the 2014-2015 Ebola outbreak in West Africa as an example throughout the talk. Next, I introduce the inverse problem: given real data tracking
the infected population, how does one estimate the severity of the outbreak. Through a novel method I show how to account for both inherent noise arising from discrete interactions between individuals (demographic stochasticity) and from uncertainty in epidemiological parameters. By applying this, I argue that the first estimates of R_0 during the Ebola outbreak were over confident because demographic stochasticity was ignored. This talk will be accessible to undergraduates.