A0186
Title: Consistent and fast inference in compartmental models of epidemics using Poisson approximate likelihoods
Authors: Michael Whitehouse - University of Bristol (United Kingdom)
Lorenzo Rimella - University of Turin (Italy) [presenting]
Nick Whiteley - University of Bristol (United Kingdom)
Abstract: Addressing the challenge of scaling up epidemiological inference to complex and heterogeneous models, Poisson approximate likelihood (PAL) methods are introduced. In contrast to the popular ordinary differential equation (ODE) approach to compartmental modelling, in which a large population limit is used to motivate a deterministic model, PALs are derived from approximate filtering equations for finite-population, stochastic compartmental models, and the large population limit drives consistency of maximum PAL estimators. Theoretical results appear to be the first likelihood-based parameter estimation consistency results which apply to a broad class of partially observed stochastic compartmental models and address the large population limit. PALs are simple to implement, involving only elementary arithmetic operations and no tuning parameters, and fast to evaluate, requiring no simulation from the model and having computational cost independent of population size. Through examples, it is demonstrated how PALs can be used to: Fit an age-structured model of influenza, taking advantage of automatic differentiation in Stan; compare over-dispersion mechanisms in a model of rotavirus by embedding PALs within sequential Monte Carlo; and evaluate the role of unit-specific parameters in a meta-population model of measles.
