A Novel Data-Driven Model for Real-Time Influenza Forecasting

Abstract

We propose a novel data-driven machine learning method using long short-term memory (LSTM)-based multi-stage forecasting for influenza forecasting. The novel aspects of the method include the following: 1) the introduction of LSTM method to capture the temporal dynamics of seasonal flu and 2) a technique to capture the influence of external variables that includes the geographical proximity and climatic variables such as humidity, temperature, precipitation, and sun exposure. The proposed model is compared against two state-of-the-art techniques using two publicly available datasets. Our proposed method performs better than the existing well-known influenza forecasting methods. The results offer a promising direction in terms of both using the data-driven forecasting methods and capturing the influence of spatio-temporal and environmental factors to improve influenza forecasting.

Keywords

• Influenza forecasting
• LSTM
• recurrent neural networks
• spatio-temporal data
• time series forecasting