Dynamic Poisson Autoregression for Influenza-Like-Illness Case Count Prediction

Zheng Wang, Prithwish Chakraborty, Sumiko R. Mekaru, John S. Brownstein, Jieping Ye, Naren Ramakrishnan

Abstract

Influenza-like-illness (ILI) is among of the most common diseases worldwide, and reliable forecasting of the same can have significant public health benefits. Recently, new forms of disease surveillance based upon digital data sources have been proposed and are continuing to attract attention over traditional surveillance methods. In this paper, we focus on short-term ILI case count prediction and develop a dynamic Poisson autoregressive model with exogenous inputs variables (DPARX) for flu forecasting. In this model, we allow the autoregressive model to change over time. In order to control the variation in the model, we construct a model similarity graph to specify the relationship between pairs of models at two time points and embed prior knowledge in terms of the structure of the graph. We formulate ILI case count forecasting as a convex optimization problem, whose objective balances the autoregressive loss and the model similarity regularization induced by the structure of the similarity graph. We then propose an efficient algorithm to solve this problem by block coordinate descent. We apply our model and the corresponding learning method on historical ILI records for 15 countries around the world using a variety of syndromic surveillance data sources. Our approach provides consistently better forecasting results than state-of-the-art models available for short-term ILI case count forecasting.