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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書誌詳細

出版年:	Scientific African
主要な著者:	Adegboyega Adebayo, Olumide O. Obe, Akintoba E. Akinwonmi, Francis Osang, Adeyinka O. Abiodun, Stephen Alaba Mogaji
フォーマット:	論文
言語:	英語
出版事項:	Elsevier 2024-06-01
主題:	Time-series modeling Bifurcation theory Epidemiology Deep learning Bayesian optimization Long short term memory algorithm
オンライン･アクセス:	http://www.sciencedirect.com/science/article/pii/S2468227624001030