Predicting influenza with dynamical methods

Abstract

Background: Prediction of influenza weeks in advance can be a useful tool in the management of cases and in the early recognition of pandemic influenza seasons. Methods: This study explores the prediction of influenza-like-illness incidence using both epidemiological and climate data. It uses Lorenz's well-known Method of Analogues, but with two novel improvements. Firstly, it determines internal parameters using the implicit near-neighbor distances in the data, and secondly, it employs climate data (mean dew point) to screen analogue near-neighbors and capture the hidden dynamics of disease spread. Results: These improvements result in the ability to forecast, four weeks in advance, the total number of cases and the incidence at the peak with increased accuracy. In most locations the total number of cases per year and the incidence at the peak are forecast with less than 15 % root-mean-square (RMS) Error, and in some locations with less than 10 % RMS Error. Conclusions: The use of additional variables that contribute to the dynamics of influenza spread can greatly improve prediction accuracy.