Statistical challenges and opportunities in infectious disease modeling

Date Wednesday January 10, 2018 at 1:00 PM
Location 13-105 Center for the Health Sciences
Speaker Vladimir Minin, Ph.D., Professor of Statistics, University of California, Irvine
Sponsoring Dept UCLA Biomathematics
Abstract 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 present results of fitting stochastic epidemic models to data from outbreaks of influenza and Ebola viruses.
Flyer 20180110_Vlad_Minin_flyer_(2).pdf