An interpretable hybrid predictive model of COVID-19 cases using autoregressive model and LSTM.

ABSTRACT: The Coronavirus Disease 2019 (COVID-19) has had a profound impact on global health and economy, making it crucial to build accurate and interpretable data-driven predictive models for COVID-19 cases to improve public policy making. The extremely large scale of the pandemic and the intrinsically changing transmission characteristics pose a great challenge for effectively predicting COVID-19 cases. To address this challenge, we propose a novel hybrid model in which the interpretability of the Autoregressive model (AR) and the predictive power of the long short-term memory neural networks (LSTM) join forces. The proposed hybrid model is formalized as a neural network with an architecture that connects two composing model blocks, of which the relative contribution is decided data-adaptively in

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC10126574 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature