Tracking dynamics change parameters of chaotic infectious disease outbreak with bifurcated time-series long short-term memory model

Tracking dynamics change parameters of chaotic infectious disease outbreak with bifurcated time-series long short-term memory model

In the dire need to develop a robust epidemiological surveillance model, this study aimed to determine if the dynamics change parameters of past infectious disease outbreaks would enable early accurate detection of future outbreaks. An optimized regular LSTM could not accurately learn the non-linear...

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Bibliographic Details
Main Authors: Adegboyega Adebayo, Olumide O. Obe, Akintoba E. Akinwonmi, Francis Osang, Adeyinka O. Abiodun, Stephen Alaba Mogaji
Format: Article
Language:English
Published: Elsevier 2024-06-01
Series:Scientific African
Subjects:
Time-series modeling
Bifurcation theory
Epidemiology
Deep learning
Bayesian optimization
Long short term memory algorithm
Online Access:http://www.sciencedirect.com/science/article/pii/S2468227624001030

Internet

http://www.sciencedirect.com/science/article/pii/S2468227624001030

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