Influenza Forecasting Framework based on Gaussian Processes

Christoph Zimmer, Reza Yaesoubi
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11671-11679, 2020.

Abstract

The seasonal epidemic of influenza costs thousands of lives each year in the US. While influenza epidemics occur every year, timing and size of the epidemic vary strongly from season to season. This complicates the public health efforts to adequately respond to such epidemics. Forecasting techniques to predict the development of seasonal epidemics such as influenza, are of great help to public health decision making. Therefore, the US Center for Disease Control and Prevention (CDC) has initiated a yearly challenge to forecast influenza-like illness. Here, we propose a new framework based on Gaussian process (GP) for seasonal epidemics forecasting and demonstrate its capability on the CDC reference data on influenza like illness: our framework leads to accurate forecasts with small but reliable uncertainty estimation. We compare our framework to several state of the art benchmarks and show competitive performance. We, therefore, believe that our GP based framework for seasonal epidemics forecasting will play a key role for future influenza forecasting and, lead to further research in the area.

Cite this Paper

BibTeX
@InProceedings{pmlr-v119-zimmer20a, title = {Influenza Forecasting Framework based on {G}aussian Processes}, author = {Zimmer, Christoph and Yaesoubi, Reza}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {11671--11679}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/zimmer20a/zimmer20a.pdf}, url = {https://proceedings.mlr.press/v119/zimmer20a.html}, abstract = {The seasonal epidemic of influenza costs thousands of lives each year in the US. While influenza epidemics occur every year, timing and size of the epidemic vary strongly from season to season. This complicates the public health efforts to adequately respond to such epidemics. Forecasting techniques to predict the development of seasonal epidemics such as influenza, are of great help to public health decision making. Therefore, the US Center for Disease Control and Prevention (CDC) has initiated a yearly challenge to forecast influenza-like illness. Here, we propose a new framework based on Gaussian process (GP) for seasonal epidemics forecasting and demonstrate its capability on the CDC reference data on influenza like illness: our framework leads to accurate forecasts with small but reliable uncertainty estimation. We compare our framework to several state of the art benchmarks and show competitive performance. We, therefore, believe that our GP based framework for seasonal epidemics forecasting will play a key role for future influenza forecasting and, lead to further research in the area.} }
Endnote
%0 Conference Paper %T Influenza Forecasting Framework based on Gaussian Processes %A Christoph Zimmer %A Reza Yaesoubi %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-zimmer20a %I PMLR %P 11671--11679 %U https://proceedings.mlr.press/v119/zimmer20a.html %V 119 %X The seasonal epidemic of influenza costs thousands of lives each year in the US. While influenza epidemics occur every year, timing and size of the epidemic vary strongly from season to season. This complicates the public health efforts to adequately respond to such epidemics. Forecasting techniques to predict the development of seasonal epidemics such as influenza, are of great help to public health decision making. Therefore, the US Center for Disease Control and Prevention (CDC) has initiated a yearly challenge to forecast influenza-like illness. Here, we propose a new framework based on Gaussian process (GP) for seasonal epidemics forecasting and demonstrate its capability on the CDC reference data on influenza like illness: our framework leads to accurate forecasts with small but reliable uncertainty estimation. We compare our framework to several state of the art benchmarks and show competitive performance. We, therefore, believe that our GP based framework for seasonal epidemics forecasting will play a key role for future influenza forecasting and, lead to further research in the area.
APA
Zimmer, C. & Yaesoubi, R.. (2020). Influenza Forecasting Framework based on Gaussian Processes. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:11671-11679 Available from https://proceedings.mlr.press/v119/zimmer20a.html.

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