West African Ebola, Zika in the Americas and Direct Likelihood-Based Inference for Stochastic Compartmental Models

• Activity Number: 143 - Advancing Translational Research Using Novel Statistical Analyses for Complex and Omics Data
• Type: Invited
• Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Marc A. Suchard*
• Companies: University of California, Los Angeles
• Keywords: stochastic processes ; MCMC ; count data ; infectious diseases ; numerical analysis

Abstract

Researchers struggle with likelihood-based inference from count data that arise continuously in time but we only intermittently observe them. A major shortcoming lies in our inability to integrate most underlying stochastic processes generating the data over all possible realizations between observations. One seemingly trivial example is a stochastic compartmental model tracking the count of susceptible, infectious and removed people during the spread of an infectious disease. For over 90 years, many have believed the transition probabilities of this SIR model remain beyond reach. However, applying a novel re-parameterization, integral transforms and other tools from numerical analysis shows that we can compute the transition probabilities in merely quadratic complexity in terms of the observed change in population size. Examples in this talk stem from the dynamics of Zika across the Americas and the 2014-2015 West African ebola outbreak.