Multi-step influenza outbreak forecasting using deep LSTM network and genetic algorithm

Influenza epidemic is a serious public health problem that has attracted worldwide attention due to cause cases of severe illness, an enormous economic burden, and even deaths worldwide each year. Forecasting influenza outbreak in advance has great significance on influenza-like illness (ILI) prevention and healthcare management. Existing research approaches based on traditional statistical and machine learning have failed to select superior features that detect sophisticated and non-linear characteristics of influenza epidemic sequential data. In this paper, it is introduced a hybrid method that combines long short-term memory (LSTM) neural network and genetic algorithm (GA) for multi-step influenza outbreak forecasting problems. LSTM model is employed to overcome the complexity and nonlinearity issues in an influenza prediction. In order to enhance the efficiency and performance of the neural network, the genetic algorithm is used to obtain the epoch size of the network, the number of LSTM layers, the size of units in each LSTM layer, and the time window size simultaneously. For comparison purposes, it is chosen the weekly data of influenza-like illness (ILI), also known as influenza or other similar illness showing flu-like symptoms, in the USA collected by the Centers for Disease Control and Prevention (CDC). The experimental results demonstrated that the presented hybrid model outperforms other highly developed machine learning approaches, a statistical model, and a fully-connected neural network considering different performance metrics during peak periods.

Automated summary

This paper proposes a hybrid model combining LSTM neural network and genetic algorithm for multi-step influenza outbreak forecasting problems. Experimental results demonstrate that the presented hybrid model outperforms other highly developed machine learning approaches during peak periods in terms of different performance metrics.