Influenza Forecasting Framework based on Gaussian Processes: Difference between revisions
Jump to navigation
Jump to search
| Line 6: | Line 6: | ||
== Background == | == 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 | 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. | ||
== Related Work == | == Related Work == | ||
Revision as of 23:13, 15 November 2020
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.