Influenza Forecasting Framework based on Gaussian Processes

Abstract

This paper presents a novel framework for seasonal epidemic forecasting using Gaussian process regression. Resulting retrospective forecasts, trained on a subset of the publicly available CDC influenza-like-illness (ILI) data-set, outperformed four state-of-the-art models when compared using the official CDC scoring rule (log-score).

Background

Each year, the seasonal influenza epidemic affects public health at a massive scale, resulting in 38 million cases, 400 000 hospitalizations, and 22 000 deaths in the United States in 2019/20 alone (cite CDC). Given this, reliable forecasts of future influenza development are invaluable, because they allow for improved public health policies and informed resource development and allocation.