Simulation-based inference for epidemiological dynamics

Abstract

The course explored deterministic and stochastic formulations of epidemiological dynamics and developed inference methods appropriate for a range of models. Special emphasis was on exact and approximate likelihood as the key elements in parameter estimation, hypothesis testing, and model selection. Specifically, the course covered sequential Monte Carlo and synthetic likelihood techniques.

Kidus Hiwot, PhD (née Kidus Asfaw)

Data scientist

I am an applied statistician with experience in many areas of the field. My research interests include statistical inference for nonlinear, non-Gaussian stochastic processes and epidemiological applications thereof.