Predictive healthcare modeling for early pandemic assessment leveraging deep auto regressor neural prophet.
Sci Rep
; 14(1): 5287, 2024 03 04.
Article
em En
| MEDLINE
| ID: mdl-38438528
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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Pandemias
/
COVID-19
Limite:
Humans
País/Região como assunto:
Asia
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article