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• W3206969312 endingPage "107994" @default.
• W3206969312 startingPage "107994" @default.
• W3206969312 abstract "Epidemics of influenza are major public health concerns. Since influenza prediction always relies on the weekly clinical or laboratory surveillance data, typically the weekly Influenza-like illness (ILI) rate series, accurate multi-step-ahead influenza predictions using ILI series is of great importance, especially, to the potential coming influenza outbreaks. This study proposes Comprehensive Learning Particle Swarm Optimization based Machine Learning (CLPSO-ML) framework incorporating support vector regression (SVR) and multilayer perceptron (MLP) for multi-step-ahead influenza prediction. A comprehensive examination and comparison of the performance and potential of three commonly used multi-step-ahead prediction modeling strategies, including iterated strategy, direct strategy and multiple-input multiple-output (MIMO) strategy, was conducted using the weekly ILI rate series from both the Southern and Northern China. The results show that: (1) The MIMO strategy achieves the best multi-step-ahead prediction, and is potentially more adaptive for longer horizon; (2) The iterated strategy demonstrates special potentials for deriving the least time difference between the occurrence of the predicted peak value and the true peak value of an influenza outbreak; (3) For ILI in the Northern China, SVR model implemented with MIMO strategy performs best, and SVR with iterated strategy also shows remarkable performance especially during outbreak periods; while for ILI in the Southern China, both SVR and MLP models with MIMO strategy have competitive prediction performance" @default.
• W3206969312 created "2021-10-25" @default.
• W3206969312 creator A5027578915 @default.
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• W3206969312 date "2021-12-01" @default.
• W3206969312 modified "2023-10-16" @default.
• W3206969312 title "Comprehensive learning particle swarm optimization enabled modeling framework for multi-step-ahead influenza prediction" @default.
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• W3206969312 doi "https://doi.org/10.1016/j.asoc.2021.107994" @default.
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