Epidemiological modeling with Laplacian-P-Splines

What is it about?

EpiLPS uses Laplace approximations and Bayesian P-splines to smooth the epidemic curve and estimate the time-varying reproduction number via a renewal equation model. Two alternative approaches for inference are presented: (1) an approach based on a maximum a posteriori argument that delivers "sampling-free" estimates of the reproduction number in only a few seconds; and (2) an approach based on a Markov chain Monte Carlo (MCMC) scheme with underlying Langevin dynamics for efficient sampling of the posterior target distribution.

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Photo by Martin Sanchez on Unsplash

Photo by Martin Sanchez on Unsplash

Why is it important?

The instantaneous reproduction number is a key statistic that provides important insights into an epidemic outbreak as it informs about the average number of secondary infections engendered by an infectious agent. During epidemic outbreaks, the reproduction number provides a snapshot (often on a daily basis) that quantifies the extent to which a given infectious disease transmits in a population and is therefore an important tool that assists governmental organizations in the management of a public health crisis.

Perspectives