COVID-19 Spatio-Temporal Evolution Using Deep Learning at a European Level

Abstract

COVID-19 evolution imposes significant challenges for the European healthcare system. The heterogeneous spread of the pandemic within EU regions elicited a wide range of policies, such as school closure, transport restrictions, etc. However, the implementation of these interventions is not accompanied by the implementation of quantitative methods, which would indicate their effectiveness. As a result, the efficacy of such policies on reducing the spread of the virus varies significantly. This paper investigates the effectiveness of using deep learning paradigms to accurately model the spread of COVID-19. The deep learning approaches proposed in this paper are able to effectively map the temporal evolution of a COVID-19 outbreak, while simultaneously taking into account policy interventions directly into the modelling process. Thus, our approach facilitates data-driven decision making by utilizing previous knowledge to train models that predict not only the spread of COVID-19, but also the effect of specific policy measures on minimizing this spread. Global models at the EU level are proposed, which can be successfully applied at the national level. These models use various inputs in order to successfully model the spatio-temporal variability of the phenomenon and obtain generalization abilities. The proposed models are compared against the traditional epidemiological and Autoregressive Integrated Moving Average (ARIMA) models.

Evidence-driven spatiotemporal COVID-19 hospitalization prediction with Ising dynamics

“…We input all features into a gated recurrent unit (GRU) model and predict the future number of cases. The GRU model is a variant of RNN, widely applied in multiple pandemic prediction works [24][25][26] . The hidden dimension of GRU is set to 128.…”

“…Statistical epidemic prediction models, including SIR, SEIR, and their variants, have achieved some success in infection, hospitalization, and mortality prediction tasks 25,40,41 . To extract complex temporal patterns, some works have applied the recurrent neural network (RNN) and its variants, such as long shortterm memory network (LSTM) and gated recurrent unit network (GRU), to predict future infected and hospitalization cases [24][25][26] .…”

Spatio-temporal variation of Covid-19 health outcomes in India using deep learning based models

“…Several previous works capture patterns of COVID-19 progression using mathematical, machine learning, and deep learning techniques ( John et al, 2022 ). Kavouras et al (2022) studied the efficacy of employing deep learning techniques to properly model COVID-19 transmission. However, they focuses on low-granular (country-level) data modelling and analysis over EU (European Union) regions.…”

Novel Insights in Spatial Epidemiology Utilizing Explainable AI (XAI) and Remote Sensing

“…Policy makers, along with government health system managers, have developed response plans and tools to defend humanity against the pandemic crisis [3][4][5]. However, these measures, such as social isolation and lockdown restrictions, are short-term and imposed to mitigate and eliminate the spread of the pandemic among citizens in urban environments [6,7].…”