ROBUST ESTIMATION FOR EPIDEMIC MODELS

Abstract

Summary A robust approach to the analysis of epidemic data is suggested. This method is based on a natural extension of M-estimation for i.i.d. observations where the distribution may be asymmetric. It is discussed initially in the context of a general discrete time stochastic process before being applied to previously studied epidemic models. In particular we consider a class of chain binomial models and models based on time dependent branching processes. Robustness and efficiency properties are studied through simulation and some previously analysed data sets are considered.