MEASLES AS A CASE-STUDY IN NONLINEAR FORECASTING AND CHAOS

This paper uses measles incidence in developed countries as the basis of a case study in nonlinear forecasting and chaos. It uses a combinat ion of epidemiological modelling and nonlinear forecasting to explore a range of issues relating to the predictability of measles before and after the advent of mass vaccination. A comparison of the pre-vaccina tion self-predictability of measles in England and Wales indicates rel atively high predictability of these predominantly biennial epidemic s eries, compared to New York City, which shows mixtures of one-, two- a nd three-year epidemics. This analysis also indicates the importance o f choosing correct embeddings to avoid bias in prediction. Forecasting for English cities indicates significant spatial heterogeneity in pre dictability before vaccination and an overall drop in predictability d uring the vaccination era. The interpretation of predictions of observ ed measles series by epidemiological models is explored and areas for refinement of current models discussed.