Deep learning for disease outbreak prediction: a parallel LSTM-CNN model | JoVE Visualize

Area of Science:

Epidemiology and Public Health
Computational Biology
Dynamical Systems Theory

Background:

Early warning signals (EWSs) are crucial for pandemic prevention, but emerging diseases present unique dynamics and noisy data challenges.
Traditional time-series classification (TSC) methods struggle with the complexities of real-world outbreak data.
Dynamical systems principles offer a framework for understanding disease spread, even for novel pathogens.

Purpose of the Study:

To develop a robust deep learning model for reliable early warning signals (EWSs) in disease outbreak prediction.
To address the challenges posed by noisy measurements and unique disease dynamics in outbreak surveillance.
To improve the accuracy and applicability of EWSs in real-world public health crises.

Main Methods:

Utilized a parallel long short-term memory-convolutional neural network deep learning architecture for TSC.
Trained the model on two simulated datasets: one modeling novel disease behaviors via polynomial dynamical systems, and another simulating noise-induced dynamics.
Evaluated model performance on diverse simulated data and real-world datasets including influenza, COVID-19, and monkeypox.

Main Results:

The proposed deep learning model demonstrated superior performance compared to existing models and statistical indicators across most datasets.
The model effectively provided early warning signals (EWSs) for impending outbreaks under various simulated and real-world conditions.
The parallel LSTM-CNN model proved robust in handling noisy data inherent in disease outbreak measurements.

Conclusions:

Advancements in deep learning, specifically the parallel LSTM-CNN model, significantly enhance the capability to provide improved EWSs.
The model's effectiveness in noisy environments makes it highly applicable for real-world emerging disease outbreak surveillance and prediction.
This research bridges sophisticated computational methods with practical public health needs for better pandemic preparedness.