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Predictive healthcare modeling for early pandemic assessment leveraging deep auto regressor neural prophet.
- Source :
-
Scientific Reports . 3/4/2024, Vol. 14 Issue 1, p1-12. 12p. - Publication Year :
- 2024
-
Abstract
- In this paper, NeuralProphet (NP), an explainable hybrid modular framework, enhances the forecasting performance of pandemics by adding two neural network modules; auto-regressor (AR) and lagged-regressor (LR). An advanced deep auto-regressor neural network (Deep-AR-Net) model is employed to implement these two modules. The enhanced NP is optimized via AdamW and Huber loss function to perform multivariate multi-step forecasting contrast to Prophet. The models are validated with COVID-19 time-series datasets. The NP’s efficiency is studied component-wise for a long-term forecast for India and an overall reduction of 60.36% and individually 34.7% by AR-module, 53.4% by LR-module in MASE compared to Prophet. The Deep-AR-Net model reduces the forecasting error of NP for all five countries, on average, by 49.21% and 46.07% for short-and-long-term, respectively. The visualizations confirm that forecasting curves are closer to the actual cases but significantly different from Prophet. Hence, it can develop a real-time decision-making system for highly infectious diseases. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 14
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Scientific Reports
- Publication Type :
- Academic Journal
- Accession number :
- 175861147
- Full Text :
- https://doi.org/10.1038/s41598-024-55973-y