  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
