Abstract
Seasonal influenza is an infectious disease of multi-causal etiology and a major cause of mortality worldwide that has been associated with environmental factors. In the attempt to model and predict future outbreaks of seasonal influenza with multiple environmental factors, we face the challenge of increased dimensionality that makes the models more complex and unstable. In this paper, we propose a nowcasting and forecasting framework that compares the theoretical approaches of Single Environmental Factor and Multiple Environmental Factors. We introduce seven solutions to minimize the weaknesses associated with the increased dimensionality when predicting seasonal influenza activity level using multiple environmental factors as external proxies. Our work provides evidence that using dimensionality reduction techniques as a strategy to combine multiple datasets improves seasonal influenza forecasting without the penalization of increased dimensionality.
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Acknowledgements
The work of IM and PP has been supported in part by the Digital Futures EXTREMUM project titled “Explainable and Ethical Machine Learning for Knowledge Discovery from Medical Data Sources”.
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Appendix
Appendix
In Table 2 we comparatively present the performance of all models in each solution using the following performance indicators: MAPE, RMSE, and Pearson correlation.
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Guarnizo, S., Miliou, I., Papapetrou, P. (2022). Impact of Dimensionality on Nowcasting Seasonal Influenza with Environmental Factors. In: Bouadi, T., Fromont, E., Hüllermeier, E. (eds) Advances in Intelligent Data Analysis XX. IDA 2022. Lecture Notes in Computer Science, vol 13205. Springer, Cham. https://doi.org/10.1007/978-3-031-01333-1_11
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DOI: https://doi.org/10.1007/978-3-031-01333-1_11
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