In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@article{Quilodrán-Casas:2021,
author = {Quilodrán-Casas, C and Silva, VS and Arcucci, R and Heaney, CE and Guo, Y and Pain, CC},
title = {Digital twins based on bidirectional LSTM and GAN for modelling COVID-19},
url = {http://arxiv.org/abs/2102.02664v1},
year = {2021}
}
TY - JOUR
AB - The outbreak of the coronavirus disease 2019 (COVID-19) has now spreadthroughout the globe infecting over 100 million people and causing the death ofover 2.2 million people. Thus, there is an urgent need to study the dynamics ofepidemiological models to gain a better understanding of how such diseasesspread. While epidemiological models can be computationally expensive, recentadvances in machine learning techniques have given rise to neural networks withthe ability to learn and predict complex dynamics at reduced computationalcosts. Here we introduce two digital twins of a SEIRS model applied to anidealised town. The SEIRS model has been modified to take account of spatialvariation and, where possible, the model parameters are based on official virusspreading data from the UK. We compare predictions from a data-correctedBidirectional Long Short-Term Memory network and a predictive GenerativeAdversarial Network. The predictions given by these two frameworks are accuratewhen compared to the original SEIRS model data. Additionally, these frameworksare data-agnostic and could be applied to towns, idealised or real, in the UKor in other countries. Also, more compartments could be included in the SEIRSmodel, in order to study more realistic epidemiological behaviour.
AU - Quilodrán-Casas,C
AU - Silva,VS
AU - Arcucci,R
AU - Heaney,CE
AU - Guo,Y
AU - Pain,CC
PY - 2021///
TI - Digital twins based on bidirectional LSTM and GAN for modelling COVID-19
UR - http://arxiv.org/abs/2102.02664v1
UR - http://hdl.handle.net/10044/1/86102
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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