Oswaldo Gressani, Hasselt University
Approximate inference with Bayesian P-splines in epidemic models
Statistical methods play an important role in infectious disease epidemiology. They provide the main set of tools to compute estimates of key epidemiological parameters and to shed light on the transmission dynamics of a pathogen. Markov chain Monte Carlo (MCMC) methods are powerful simulation techniques used to explore the posterior parameter space and carry out inference under the Bayesian paradigm. As MCMC samplers are iterative by design, drawing samples from the target posterior distribution often requires huge computational resources. This computational bottleneck is particularly unwelcome when analysis of epidemic data and estimation of model parameters is required in (near) real-time, as is often the case during epidemic outbreaks where massive datasets are updated on a daily basis. We explore the synergy between the Laplace approximation and Bayesian P-splines in epidemic models to deliver a flexible inference methodology with fast and nimble algorithms that outperform MCMC-based approaches from a computational perspective. The socalled “Laplacian-P-splines” method is illustrated in the context of nowcasting (i.e. the real-time assessment of the current epidemic situation corrected for imperfect data information caused by delays in reporting) and in the recently proposed EpiLPS framework for estimating the time-varying reproduction number with applications on data of SARS-CoV-2.