Comprehensive learning particle swarm optimization enabled modeling framework for multi-step-ahead influenza prediction

TL;DR

This paper introduces a novel comprehensive learning particle swarm optimization framework combined with machine learning models to improve multi-step-ahead influenza prediction accuracy using weekly ILI data from China.

Contribution

It compares three multi-step prediction strategies and demonstrates the superior performance of the MIMO strategy, especially with SVR, for influenza outbreak forecasting.

Findings

MIMO strategy yields the best multi-step-ahead predictions.

SVR with MIMO performs best in Northern China.

Iterated strategy is effective for early peak detection.

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