Epidemic Models

In this paper, we propose an epidemic model with the transmission rate between susceptible and infected individuals, β, being time varying and piecewise constant. At any point in time, β is linked to a latent variable that follows a Hidden Semi-Markov Model (HSMM). This HSMM-driven epidemic Model (HSMM-EM) structures the data into multiple regimes, which greatly enhances decision-making capabilities, while the limited number of continuous model parameter guarantees straightforward model interpretation.

My doctoral research evolves around the topic of Sequential Bayesian Learning for State Space Models. More generally, I am working on estimating model parameters in a batch as well as in a times series setting, with a focus on latent variable models. This thesis includes projects with Hidden Semi-Markov Models, Copulae, and Epidemic Models that have latent factors attached.