Multi-step prediction for influenza outbreak by an adjusted long short-term memory
By J. Zhang, Kazumitsu Nawata. 2018
48 citations recorded by the source corpus.
Abstract Influenza results in approximately 3–5 million annual cases of severe illness and 250 000–500 000 deaths. We urgently need an accurate multi-step-ahead time-series forecasting model to help hospitals to perform dynamical assignments of beds to influenza patients for the annually varied influenza season, and aid pharmaceutical companies to formulate a flexible plan of manufacturing vaccine for the yearly different influenza vaccine. In this study, we utilised four different multi-step prediction algorithms in the long short-term memory (LSTM). The result showed that implementing multiple single-output prediction in a six-layer LSTM structure achieved the best accuracy. The mean absolute percentage errors from two- to 13-step-ahead prediction for the US influenza-like illness rates were all <15%, averagely 12.930%. To the best of our knowledge, it is the first time that LSTM has been applied and refined to perform multi-step-ahead prediction for influenza outbreaks. Hopefully, this modelling methodology can be applied in other countries and therefore help prevent and control influenza worldwide.
Subfields: Artificial Intelligence, Epidemiology, Molecular Biology
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Citations in the corpus
- Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting (2019, background): “in retail [5, 10], medicine [26, 42] and economics [6], improvements in existing methods bear much significance for practitioners in many domains.”
- TSMixer: An all-MLP Architecture for Time Series Forecasting (2023, background): “Time series forecasting is a prevalent problem in numerous real-world use cases, such as for forecasting of demand of products (Böse et al., 2017; Courty & Li, 1999), pandemic spread (Zhang & Nawata, 2018), and inflation rates (Capistrán et al., 2010).”