Data Assimilation Predictive GAN (DA-PredGAN) Applied to a Spatio-Temporal Compartmental Model in Epidemiology.

We propose a novel use of generative adversarial networks (GANs) (i) to make predictions in time (PredGAN) and (ii) to assimilate measurements (DA-PredGAN). In the latter case, we take advantage of the natural adjoint-like properties of generative models and the ability to simulate forwards and backwards in time. GANs have received much attention recently, after achieving excellent results for their generation of realistic-looking images. We wish to explore how this property translates to new applications in computational modelling and to exploit the adjoint-like properties for efficient data assimilation. We apply these methods to a compartmental model in epidemiology that is able to model space and time variations, and that mimics the spread of COVID-19 in an idealised town. To do this, the GAN is set within a reduced-order model, which uses a low-dimensional space for the spatial distribution of the simulation states. Then the GAN learns the evolution of the low-dimensional states over time. The results show that the proposed methods can accurately predict the evolution of the high-fidelity numerical simulation, and can efficiently assimilate observed data and determine the corresponding model parameters.

ST-COVID: a Deep Multi-View Spatio-temporal Model for COVID-19 Forecasting

The outbreak of COVID-19 has caused a dramatic loss of human life worldwide. Reliable prediction results are crucial on pandemic prevention and control in the early stage. However, it is a very challenging task due to insufficient data and dynamic virus spread pattern. Unlike most existing works only considering local data for a given region, we propose a spatio-temporal prediction model (ST-COVID) for COVID-19 forecasting to borrow experience from historical observations of other regions. Specifically, our proposed model consists of two views: spatial view (modeling global spatial connectivity with neighbor regions in geography and semantic space via GCNs), temporal view (extracting local and global latent temporal trend via CNNs and GRU). Extensive experiments on two real-world datasets at state and county level in US indicate that the proposed model outperforms over nine baselines in both short-term and long-term prediction. © 2022 IEEE.

A hierarchical spatio-temporal model to analyze relative risk variations of COVID-19: a focus on Spain, Italy and Germany.

The novel coronavirus disease (COVID-19) has spread rapidly across the world in a short period of time and with a heterogeneous pattern. Understanding the underlying temporal and spatial dynamics in the spread of COVID-19 can result in informed and timely public health policies. In this paper, we use a spatio-temporal stochastic model to explain the temporal and spatial variations in the daily number of new confirmed cases in Spain, Italy and Germany from late February 2020 to mid January 2021. Using a hierarchical Bayesian framework, we found that the temporal trends of the epidemic in the three countries rapidly reached their peaks and slowly started to decline at the beginning of April and then increased and reached their second maximum in the middle of November. However decline and increase of the temporal trend seems to show different patterns in Spain, Italy and Germany.