Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic.

Autor: Quilodrán-Casas C; Data Science Institute, Department of Computing, Imperial College London, UK.; Department of Earth Science & Engineering, Imperial College London, UK., Silva VLS; Department of Earth Science & Engineering, Imperial College London, UK., Arcucci R; Data Science Institute, Department of Computing, Imperial College London, UK.; Department of Earth Science & Engineering, Imperial College London, UK., Heaney CE; Department of Earth Science & Engineering, Imperial College London, UK., Guo Y; Data Science Institute, Department of Computing, Imperial College London, UK., Pain CC; Data Science Institute, Department of Computing, Imperial College London, UK.; Department of Earth Science & Engineering, Imperial College London, UK.
Jazyk: angličtina
Zdroj: Neurocomputing [Neurocomputing (Amst)] 2022 Jan 22; Vol. 470, pp. 11-28. Date of Electronic Publication: 2021 Oct 22.
DOI: 10.1016/j.neucom.2021.10.043
Abstrakt: The outbreak of the coronavirus disease 2019 (COVID-19) has now spread throughout the globe infecting over 150 million people and causing the death of over 3.2 million people. Thus, there is an urgent need to study the dynamics of epidemiological models to gain a better understanding of how such diseases spread. While epidemiological models can be computationally expensive, recent advances in machine learning techniques have given rise to neural networks with the ability to learn and predict complex dynamics at reduced computational costs. Here we introduce two digital twins of a SEIRS model applied to an idealised town. The SEIRS model has been modified to take account of spatial variation and, where possible, the model parameters are based on official virus spreading data from the UK. We compare predictions from one digital twin based on a data-corrected Bidirectional Long Short-Term Memory network with predictions from another digital twin based on a predictive Generative Adversarial Network. The predictions given by these two frameworks are accurate when compared to the original SEIRS model data. Additionally, these frameworks are data-agnostic and could be applied to towns, idealised or real, in the UK or in other countries. Also, more compartments could be included in the SEIRS model, in order to study more realistic epidemiological behaviour.
Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2021 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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