ArticleNeurocomputing2022
Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic.
Article in Neurocomputing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 3 citations in OpenAlex.
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- Computational Architectures for Precision Dairy Nutrition Digital Twins: A Technical Review and Implementation Framework.Sensors (Basel, Switzerland) · 2025Pooled it
- Protein aggregation and neurodegenerative disease: Structural outlook for the novel therapeutics.Proteins · 2025Review
- Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges.Nature communications · 2025Article
- Evolution of digital twins in precision health applications: a scoping review study.Research square · 2024Article
- Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review.PeerJ. Computer science · 2024Article
- Multi-weight susceptible-infected model for predicting COVID-19 in China.Neurocomputing · 2023Article
- Real-time updating of dynamic social networks for COVID-19 vaccination strategies.Journal of ambient intelligence and humanized computing · 2023Article
- Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed cases.Frontiers in artificial intelligence · 2023Article
- Popular deep learning algorithms for disease prediction: a review.Cluster computing · 2023Article
- Data Assimilation Predictive GAN (DA-PredGAN) Applied to a Spatio-Temporal Compartmental Model in Epidemiology.Journal of scientific computing · 2023Article
- Attention based parameter estimation and states forecasting of COVID-19 pandemic using modified SIQRD Model.Chaos, solitons, and fractals · 2022Article
- Digital Twin for Civil Engineering Systems: An Exploratory Review for Distributed Sensing Updating.Sensors (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.