Parametrically Smoothened Likelihood Inference (PARSLI)
Channel: BioInference
Date: 2023-09-22

Talk by Andrew Glover at BioInference 2023.

https://bioinference.github.io/

Abstract: Stochasticity in epidemic systems can pose significant challenges for parameter inference, particularly for models with fine-scale spatial structure. Here, an example of this is initially demonstrated through a temperature-dependent metapopulation model for the 2015-16 Zika outbreak in Colombia. Using a grid approximation approach for inference of the seeding date and an R0 thermal response scaling parameter, markedly different posterior distributions for different random seeds were initially yielded due to stochasticity in the model. To address this, a novel methodology of Parametrically Smoothened Likelihood Inference (PARSLI) is presented, which aims to maximise information from a grid approximation of the posterior distribution for stochastic systems that are highly sensitive to different random seeds. Prior to applying this method, ensembles of realisations are conducted at each point in parameter space, from which empirical log-likelihood distributions are calculated given an observed case data timeseries. Following an approximate normalization procedure, parametric distributions are then fitted to the empirical log-likelihood distributions at each point in parameter space. By exploiting the definition of the MGF, expectations of the likelihoods are generated at each location in the parameter space. This ultimately enables likelihood and posterior distributions to be generated over the parameter space that are approximately invariant to random seeds for a highly stochastic system. This methodology provides an alternative approach to MCMC methods where poor chain convergence or mixing may prove problematic during parameter inference for highly stochastic systems.