Influenza Forecasting Framework based on Gaussian Processes: Difference between revisions
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== Related Work == | == Related Work == | ||
Given the value of epidemic forecasts, the CDC regularly publishes ILI data and has funded a seasonal ILI forecasting challenge. This challenge has lead to four state of the art models in the field; MSS, a physical susceptible-infected-recovered model with assumed linear noise; SARIMA, a framework based on seasonal auto-regressive moving average models; and LinEns, an ensemble of three linear regression models. | |||
== Motivation == | == Motivation == | ||
Revision as of 00:13, 16 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.
Related Work
Given the value of epidemic forecasts, the CDC regularly publishes ILI data and has funded a seasonal ILI forecasting challenge. This challenge has lead to four state of the art models in the field; MSS, a physical susceptible-infected-recovered model with assumed linear noise; SARIMA, a framework based on seasonal auto-regressive moving average models; and LinEns, an ensemble of three linear regression models.