The outbreak of the coronavirus disease 2019 (COVID-19) has now spread
throughout the globe infecting over 100 million people and causing the death of
over 2.2 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 a data-corrected
Bidirectional Long Short-Term Memory network and 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.