Limits to estimating the severity of emerging epidemics due to inherent noise
- Series
- Mathematical Biology Seminar
- Time
- Wednesday, July 6, 2016 - 11:00 for 1 hour (actually 50 minutes)
- Location
- Skiles 005
- Speaker
- Bradford Taylor – School of Biology, Georgia Tech
When a disease outbreak occurs, mathematical models are used to<br />
estimate the potential severity of the epidemic. The average number of<br />
secondary infections resulting from the initial infection or reproduction<br />
number, R_0, quantifies this severity. R_0 is estimated from the models by<br />
leveraging observed case data and understanding of disease epidemiology.<br />
However, the leveraged data is not perfect. How confident should we be<br />
about measurements of R_0 given noisy data? I begin my talk by introducing<br />
techniques used to model epidemics. I show how to adapt standard models to<br />
specific diseases by using the 2014-2015 Ebola outbreak in West Africa as<br />
an example throughout the talk. Nest, I introduce the inverse problem:<br />
given real data tracking the infected population how does one estimate the<br />
severity of the outbreak. Through a novel method I show how to account for<br />
both inherent noise arising from discrete interactions between individuals<br />
(demographic stochasticity) and from uncertainty in epidemiological<br />
parameters. By applying this, I argue that the first estimates of R_0<br />
during the Ebola outbreak were overconfident because demographic<br />
stochasticity was ignored.<br />
This talk will be accessible to undergraduates.