Deep learning for disease outbreak prediction: a parallel LSTM-CNN model.

Abstract

Early warning signals (EWSs) are vital for implementing preventive measures before a disease turns into a pandemic. While new diseases exhibit unique behaviours, they often share fundamental characteristics from a dynamical systems perspective. Moreover, measurements during disease outbreaks are often corrupted by different noise sources, posing challenges for time-series classification (TSC) tasks. In this study, we address the problem of having a robust EWS for disease outbreak prediction using a parallel long short-term memory-convolutional neural network deep learning model in the domain of TSC. We employed two simulated datasets to train the model: one representing generated dynamical systems with randomly selected polynomial terms to model new disease behaviours, and another simulating noise-induced disease dynamics to account for noisy measurements. The model's performance was analysed using both simulated data from different disease models and real-world data, including influenza, COVID-19 and monkeypox. Results demonstrate that the proposed model outperforms previous models and statistical indicators in most of the datasets, effectively providing EWSs of impending outbreaks across various scenarios. This study bridges advancements in deep learning with the ability to provide improved EWSs in noisy environments, making it highly applicable to real-world crises involving emerging disease outbreaks.