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Activity Number: 319 - Innovative Approaches to the Study of an Epidemic
Type: Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #313052
Title: A Linear Noise Approximation for Stochastic Epidemic Models Fit to Partially Observed Incidence Counts
Author(s): Jonathan Fintzi* and Jon Wakefield and Vladimir Minin
Companies: Biostatistics Research Branch, NIAID and Departments of Biostatistics and Statistics, University of WAshington and Department of Statistics, University of California, Irvine
Keywords: Bayesian data augmentation; Elliptical slice sampler; Non-centered parameterization; Surveillance count data; Disease transmission

Stochastic epidemic models (SEMs) fit to incidence data help to elucidate outbreak transmission dynamics and shape response strategies. SEMs represent counts of individuals in discrete infection states using Markov jump processes (MJP), but are hard to fit as imperfect surveillance and the temporal coarseness of the data obscure the epidemic. Analytically integrating over the epidemic process is often impossible, and integration via MCMC is cumbersome due to the dimensionality and discreteness of the latent state space. Simulation-based computational approaches can address the intractability of the MJP likelihood, but are fragile and prohibitively expensive for complex models. A linear noise approximation (LNA) that replaces the MJP transition density with a Gaussian density has been explored for analyzing prevalence data in large-population settings. Existing LNA frameworks are inappropriate for incidence data and depend on simulation-based methods or further model approximations. We show how the LNA can be used to analyze incidence data within a data augmentation MCMC framework that outperforms deterministic methods, statistically, and simulation-based methods, computationally.

Authors who are presenting talks have a * after their name.

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