<div>This document pretends to provide an overview about the lights and shadows on the latest trends in this specific area.</div><div>Unlike previously released literature reviews, that are providing a wide overview about any type of AI techniques applied to overall aspects of the pandemics, this document will focus specifically on the use of DL techniques applied to COVID-19 time series forecasting. The production in this field within the last months has become quite large.</div><div>After setting a group of quality criteria, related to problem definition, dataset manipulation, model identification and evaluation, 96 papers has been screened.</div><div>Most of the analysed papers did not meet the common quality standards of scientific work: none of them positively scored in all of the criteria, while only about one third scored positively in at least half of the defined criteria. The emergency character of this scientific production led to getting away from some of the basic requirements for quality scientific work.</div>

<p>Dynamic traffic flow forecasting remains an open issue to this day. As other spatio-temporal problems, traffic prediction deals with both temporal and spatial nonlinear relationships, with the particularity that nearby points in the Euclidean space might be allocated in different roads, adding another layer of complexity. Traffic prediction has witnessed a revolution with the appearance of deep learning, with graph neural networks being prominently responsible for a steep increase in forecasting accuracy. In this paper, we consider the use of an automatic attention mechanism in order to improve the prediction capabilities of a traffic graph convolutional network. This model is based on the composition of gated recurrent units and graph convolution networks to model space and time simultaneously. To overcome the spatial modelling limitations of the original model, our proposal replaces the graph convolutional layer with a graph attention mechanism. Our aim is to model spatial relations in an automatic, more dynamic way. In order to prove the validity and usefulness of our proposal, we have performed a thorough experimentation over two known traffic datasets used in previous research, plus a new, complex one which we have curated and published. Our results portray a clear and statistically significant advantage with the inclusion of spatial attention, surpassing the performance of a wide set of state-of-the-art models on every tested scenario.</p>

<p>Dynamic traffic flow forecasting remains an open issue to this day. As other spatio-temporal problems, traffic prediction deals with both temporal and spatial nonlinear relationships, with the particularity that nearby points in the Euclidean space might be allocated in different roads, adding another layer of complexity. Traffic prediction has witnessed a revolution with the appearance of deep learning, with graph neural networks being prominently responsible for a steep increase in forecasting accuracy. In this paper, we consider the use of an automatic attention mechanism in order to improve the prediction capabilities of a traffic graph convolutional network. This model is based on the composition of gated recurrent units and graph convolution networks to model space and time simultaneously. To overcome the spatial modelling limitations of the original model, our proposal replaces the graph convolutional layer with a graph attention mechanism. Our aim is to model spatial relations in an automatic, more dynamic way. In order to prove the validity and usefulness of our proposal, we have performed a thorough experimentation over two known traffic datasets used in previous research, plus a new, complex one which we have curated and published. Our results portray a clear and statistically significant advantage with the inclusion of spatial attention, surpassing the performance of a wide set of state-of-the-art models on every tested scenario.</p>

<div>This document pretends to provide an overview about the lights and shadows on the latest trends in this specific area.</div><div>Unlike previously released literature reviews, that are providing a wide overview about any type of AI techniques applied to overall aspects of the pandemics, this document will focus specifically on the use of DL techniques applied to COVID-19 time series forecasting. The production in this field within the last months has become quite large.</div><div>After setting a group of quality criteria, related to problem definition, dataset manipulation, model identification and evaluation, 96 papers has been screened.</div><div>Most of the analysed papers did not meet the common quality standards of scientific work: none of them positively scored in all of the criteria, while only about one third scored positively in at least half of the defined criteria. The emergency character of this scientific production led to getting away from some of the basic requirements for quality scientific work.</div>