Medical School Office Building (MSOB)
|DATE:||October 26, 2017|
|TIME:||1:30 - 2:50 pm|
|TITLE:||Statistical challenges and opportunities in infectious disease modeling|
Professor of Statistics
University of California, Irvine
Stochastic epidemic models describe how infectious diseases spread through a population of interest. These models are constructed by first assigning individuals to compartments (e.g., susceptible, infectious, and recovered) and then defining a stochastic process that governs the evolution of sizes of these compartments through time. I will discuss multiple strategies for fitting these models to data, which turns out to be a challenging task. The main difficulty is that even the most vigilant infectious disease surveillance programs offer only noisy snapshots of the number of infected individuals in the population. I will discuss Bayesian data augmentation strategies that make statistical inference with stochastic epidemic models computationally tractable. Some of these strategies can even handle more exotic data types, such as infectious disease agent genetic sequences collected during outbreak monitoring. I will discuss results of fitting stochastic epidemic models to data from outbreaks of influenza and Ebola viruses.
Paul Fearnhead, Vasilieos Giagos, Chris Sherlock. Inference for reaction networks using the linear noise approximation. Biometrics. Vol 70, Issue 2, June 2014, pp 457-466.
AA Koepke, IM Longini, Jr, ME Halloran, J Wakefield, and VN Minin. Predictive modeling of cholera outbreaks in Bangladesh. The Annals of Applied Statistics. Vol 10, Number 2 (2016), 575-595.
J Fintzi, X Cui, J Wakefield and VN Minin. Efficient data augmentation for fitting stochastic epidemic models to prevalence data. J of Computational and Graphical Statistics. Pages 1-12 | Received 01 Jun 2016, Accepted author version posted online: 11 May 2017, Published online: 11 May 2017.